2021

Cupeiro Figueroa, I. Cimmino, M. Drgoňa, J. Helsen, L. (2021). ”Fluid temperature predictions of geothermal borefields using load estimations via state observers”, Journal of Building Performance Simulation, 14 (1), 1-19. DOI: 10.1080/19401493.2020.1838612.

Ghosh, P. Krishnamoorthy, S. Kalyanaraman, A. (2021). ”PaKman: A Scalable Algorithm for Generating Genomic Contigs on Distributed Memory Machines”, IEEE Transactions on Parallel and Distributed Systems, 32 (5), 1191-1209. DOI: 10.1109/TPDS.2020.3043241.

Brabec, J. Brandejs, J. Kowalski, K. Xantheas, S. Legeza, Ö. Veis, L. (2021). ”Massively parallel quantum chemical density matrix renormalization group method”, Journal of Computational Chemistry, 42 (8), 534-544. DOI: 10.1002/jcc.26476.

Chen, Y. Xu, Z. Wang, C. Bao, J. Koeppel, B. Yan, L. Gao, P. Wang, W. (2021). ”Analytical modeling for redox flow battery design”, Journal of Power Sources, 482. DOI: 10.1016/j.jpowsour.2020.228817

Murugesan, V. Nie, Z. Zhang, X. Gao, P. Zhu, Z. Huang, Q. Yan, L. Reed, D. Wang, W. (2021). ”Accelerated design of vanadium redox flow battery electrolytes through tunable solvation chemistry”, Cell Reports Physical Science, 2 (2). DOI: 10.1016/j.xcrp.2021.100323.

Zhang, X. Fu, X. Zhuang, D. Xie, C. Song, S. (2021). ”Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design”, IEEE Transactions on Computers, DOI: 10.1109/TC.2021.3056929.

Geng, T. Li, A. Wang, T. Wu, C. Li, Y. Shi, R. Wu, W. Herbordt, M. (2021). ”O3BNN-R: An Out-of-Order Architecture for High-Performance and Regularized BNN Inference”, IEEE Transactions on Parallel and Distributed Systems, 32 (1), 199-213. DOI: 10.1109/TPDS.2020.3013637.

Kharazmi, E. Zhang, Z. Karniadakis, G.E.M. (2021). ”hp-VPINNs: Variational physics-informed neural networks with domain decomposition”, Computer Methods in Applied Mechanics and Engineering, 374. DOI: 10.1016/j.cma.2020.113547.

Kwak, W.-J. Lim, H.-S. Gao, P. Feng, R. Chae, S. Zhong, L. Read, J. Engelhard, M.H. Xu, W. Zhang, J.-G. (2021). ”Effects of Fluorinated Diluents in Localized High-Concentration Electrolytes for Lithium–Oxygen Batteries”, Advanced Functional Materials, 31 (2). DOI: 10.1002/adfm.202002927.

Minutoli, M. Castellana, V.G. Saporetti, N. Devecchi, S. Lattuada, M. Fezzardi, P. Tumeo, A. Ferrandi, F. (2021). ”Svelto: High-Level Synthesis of Multi-Threaded Accelerators for Graph Analytics”, IEEE Transactions on Computers. DOI: 10.1109/TC.2021.3057860.

Wang, T. Li, L. Pallaka, M.R. Das, H. Whalen, S. Soulami, A. Upadhyay, P. Kappagantula, K.S. (2021). ”Mechanical and microstructural characterization of AZ31 magnesium‑carbon fiber reinforced polymer joint obtained by friction stir interlocking technique”, Materials and Design, 198. DOI: 10.1016/j.matdes.2020.109305.

Lu, L. Meng, X. Mao, Z. Karniadakis, G.E. (2021). ”DeepXDE: A deep learning library for solving differential equations”, SIAM Review, 63 (1), 208-228. DOI: 10.1137/19M1274067.

Li, A. Su, S. (2021). ”Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs”, IEEE Transactions on Parallel and Distributed Systems, 32 (7), 1878-1891. DOI: 10.1109/TPDS.2020.3045828.

Cao, X. Gao, P. Ren, X. Zou, L. Engelhard, M.H. Matthews, B.E. Hu, J. Niu, C. Liu, D. Arey, B.W. Wang, C. Xiao, J. Liu, J. Xu, W. Zhang, J.-G. (2021). ”Effects of fluorinated solvents on electrolyte solvation structures and electrode/electrolyte interphases for lithium metal batteries”, Proceedings of the National Academy of Sciences of the United States of America, 118 (9). DOI: 10.1073/pnas.2020357118.

Cromwell, E. Shuai, P. Jiang, P. Coon, E.T. Painter, S.L. Moulton, J.D. Lin, Y. Chen, X. (2021). ”Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks”, Frontiers in Earth Science, 9. DOI: 10.3389/feart.2021.613011.

Aksoy, S.G. Bruillard, P. Young, S.J. Raugas, M. (2021). ”Ramanujan graphs and the spectral gap of supercomputing topologies”, Journal of Supercomputing, 77 (2), 1177-1213. DOI: 10.1007/s11227-020-03291-1.

Bauman, N.P. Liu, H. Bylaska, E.J. Krishnamoorthy, S. Low, G.H. Granade, C.E. Wiebe, N. Baker, N.A. Peng, B. Roetteler, M. Troyer, M. Kowalski, K. (2021). ”Toward Quantum Computing for High-Energy Excited States in Molecular Systems: Quantum Phase Estimations of Core-Level States”, Journal of Chemical Theory and Computation, 17 (1), 201-210. DOI: 10.1021/acs.jctc.0c00909.

Tartakovsky, A.M. Barajas-Solano, D.A. He, Q. (2021). ”Physics-informed machine learning with conditional Karhunen-Loève expansions”, Journal of Computational Physics, 426. DOI: 10.1016/j.jcp.2020.109904.

Silva, S.J. Burrows, S.M. Evans, M.J. Halappanavar, M. (2021). ”A Graph Theoretical Intercomparison of Atmospheric Chemical Mechanisms”, Geophysical Research Letters, 48 (1). DOI: 10.1029/2020GL090481.

Childs, A.M. Su, Y. Tran, M.C. Wiebe, N. Zhu, S. (2021). ”Theory of Trotter Error with Commutator Scaling”, Physical Review X, 11 (1). DOI: 10.1103/PhysRevX.11.011020.

Huggins, W.J. McClean, J.R. Rubin, N.C. Jiang, Z. Wiebe, N. Whaley, K.B. Babbush, R. (2021). ”Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers”, npj Quantum Information, 7 (1). DOI: 10.1038/s41534-020-00341-7.

Yang, L. Meng, X. Karniadakis, G.E. (2021). ”B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data”, Journal of Computational Physics, 425. DOI: 10.1016/j.jcp.2020.109913.

Hagen, L.H. Brooke, C.G. Shaw, C.A. Norbeck, A.D. Piao, H. Arntzen, M.Ø. Olson, H.M. Copeland, A. Isern, N. Shukla, A. Roux, S. Lombard, V. Henrissat, B. O’Malley, M.A. Grigoriev, I.V. Tringe, S.G. Mackie, R.I. Pasa-Tolic, L. Pope, P.B. Hess, M. (2021). ”Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber”, ISME Journal, 15 (2), 421-434. DOI: 10.1038/s41396-020-00769-x.

Bhuiyan, T.H. Medal, H.R. Nandi, A.K. Halappanavar, M. (2021). ”Risk-averse bi-level stochastic network interdiction model for cyber-security risk management”, International Journal of Critical Infrastructure Protection, 32. DOI: 10.1016/j.ijcip.2021.100408.

2020

Darulová, J. Pauka, S.J. Wiebe, N. Chan, K.W. Gardener, G.C. Manfra, M.J. Cassidy, M.C. Troyer, M. (2020). ”Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots”, Physical Review Applied, 13 (5). DOI: 10.1103/PhysRevApplied.13.054005.

Berry, D.W. Childs, A.M. Su, Y. Wang, X. Wiebe, N. (2020). ”Time-dependent hamiltonian simulation with L1-norm scaling”, Quantum, 4. DOI: 10.22331/q-2020-04-20-254.

Palmer, B.J. Chun, J. Morris, J.F. Mundy, C.J. Schenter, G.K. (2020). ”Correlation function approach for diffusion in confined geometries”, Physical Review E, 102 (2). DOI: 10.1103/PhysRevE.102.022129.

Kwak, W.-J. Chae, S. Feng, R. Gao, P. Read, J. Engelhard, M.H. Zhong, L. Xu, W. Zhang, J.-G. (2020). ”Optimized Electrolyte with High Electrochemical Stability and Oxygen Solubility for Lithium-Oxygen and Lithium-Air Batteries”, ACS Energy Letters, 5 (7), 2182-2190. DOI: 10.1021/acsenergylett.0c00809.

Zou, P. Li, A. Barker, K. Ge, R. (2020). ”Indicator-Directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems”, Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020, 559-568. DOI: 10.1109/CCGrid49817.2020.00-37.

Minutoli, M. Castellana, V.G. Tan, C. Manzano, J. Amatya, V. Tumeo, A. Brooks, D. Wei, G.-Y. (2020). ”SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators”, IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD, 2020. DOI: 10.1145/3400302.3415781.

Poudel, S. Sharma, P. Dubey, A. Schneider, K.P. (2020). ”Advanced FLISR with Intentional Islanding Operations in an ADMS Environment Using GridAPPS-D”, IEEE Access, 8, 113766-113778. DOI: 10.1109/ACCESS.2020.3003325.

Firoz, J.S. Li, A. Li, J. Barker, K. (2020). ”On the Feasibility of Using Reduced-Precision Tensor Core Operations for Graph Analytics”, 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020, DOI: 10.1109/HPEC43674.2020.9286152.

Ren, X. Gao, P. Zou, L. Jiao, S. Cao, X. Zhang, X. Jia, H. Engelhard, M.H. Matthews, B.E. Wu, H. Lee, H. Niu, C. Wang, C. Arey, B.W. Xiao, J. Liu, J. Zhang, J.-G. Xu, W. (2020). ”Role of inner solvation sheath within salt–solvent complexes in tailoring electrode/electrolyte interphases for lithium metal batteries”, Proceedings of the National Academy of Sciences of the United States of America, 117 (46), 28603-28613. DOI: 10.1073/pnas.2010852117. 

Jian, J. Fernandez, C.A. Burghardt, J. Bonneville, A. Gupta, V. Garrison, G. (2020). ”Alternative, less water-intensive, fracturing fluids for enhanced geothermal systems”, 54th U.S. Rock Mechanics/Geomechanics Symposium.

Yang, N. Edington, S.C. Choi, T.H. Henderson, E.V. Heindel, J.P. Xantheas, S.S. Jordan, K.D. Johnsn, M.A. (2020). ”Mapping the temperature-dependent and network site-specific onset of spectral diffusion at the surface of a water cluster cage”, Proceedings of the National Academy of Sciences of the United States of America, 117 (42), 26047-26052. DOI: 10.1073/pnas.2017150117.

Meng, X. Karniadakis, G.E. (2020). ”A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems”, Journal of Computational Physics, 401. DOI: 10.1016/j.jcp.2019.109020.

Gawande, N.A. Daily, J.A. Siegel, C. Tallent, N.R. Vishnu, A. (2020). ”Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing”, Future Generation Computer Systems, 108, 1162-1172. DOI: 10.1016/j.future.2018.04.073.

Barik, R. Minutoli, M. Halappanavar, M. Tallent, N.R. Kalyanaraman, A. (2020). ”Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation”, Proceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020, 240-251. DOI: 10.1109/IISWC50251.2020.00031.

Wang, T. Geng, T. Li, A. Jin, X. Herbordt, M. (2020). ”FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters”, IEEE Transactions on Computers, 69 (8), 1143-1158. DOI: 10.1109/TC.2020.3000118.

Cai, Y. Sun, C.Y. Li, Y.L. Hu, S.Y. Zhu, N.Y. Barker, E.I. Qian, L.Y. (2020). ”Phase field modeling of discontinuous dynamic recrystallization in hot deformation of magnesium alloys”, International Journal of Plasticity, 133. DOI: 10.1016/j.ijplas.2020.102773.

Singh, R.K. Bao, J. Wang, C. Fu, Y. Xu, Z. (2020). ”Hydrodynamics of countercurrent flows in a structured packed column: Effects of initial wetting and dynamic contact angle”, Chemical Engineering Journal, 398. DOI: 10.1016/j.cej.2020.125548.

Jia, H. Xu, Y. Burton, S.D. Gao, P. Zhang, X. Matthews, B.E. Engelhard, M.H. Zhong, L. Bowden, M.E. Xiao, B. Han, K.S. Wang, C. Xu, W. (2020). ”Enabling Ether-Based Electrolytes for Long Cycle Life of Lithium-Ion Batteries at High Charge Voltage”, ACS Applied Materials and Interfaces, 12 (49), 54893-54903. DOI: 10.1021/acsami.0c18177.

Liu, X.T. Halappanavar, M. Barker, K.J. Lumsdaine, A. Gebremedhin, A.H. (2020). ”Direction-optimizing label propagation and its application to community detection”, 17th ACM International Conference on Computing Frontiers 2020, CF 2020 - Proceedings, 192-201. DOI: 10.1145/3387902.3392634.

Chen, Y. Glaesemann, K. Li, X. Palmer, B. Huang, R. Vyakaranam, B. (2020). ”A generic advanced computing framework for executing windows-based dynamic contingency analysis tool in parallel on cluster machines”, IEEE Power and Energy Society General Meeting, 2020. DOI: 10.1109/PESGM41954.2020.9281477.

Zhang, D. Guo, L. Karniadakis, G.E. (2020). ”Learning in modal space: Solving time-dependent stochastic PDEs using physics-informed neural networks”, SIAM Journal on Scientific Computing, 42 (2), A639-A665. DOI: 10.1137/19M1260141.

Flamini, F. Walschaers, M. Spagnolo, N. Wiebe, N. Buchleitner, A. Sciarrino, F. (2020). ”Validating multi-photon quantum interference with finite data”, Quantum Science and Technology, 5 (4). DOI: 10.1088/2058-9565/aba03a.

Azad, A. Aznaveh, M.M. Beamer, S. Blanco, M. Chen, J. D'Alessandro, L. Dathathri, R. Davis, T. Deweese, K. Firoz, J. Gabb, H.A. Gill, G. Hegyi, B. Kolodziej, S. Low, T.M. Lumsdaine, A. Manlaibaatar, T. Mattson, T.G. McMillan, S. Peri, R. Pingali, K. Sridhar, U. Szarnyas, G. Zhang, Y. Zhang, Y. (2020). ”Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite”, Proceedings - 2020 IEEE International Symposium on Workload Characterization, IISWC 2020, 216-227. DOI: 10.1109/IISWC50251.2020.00029.

Peng, B. Kowalski, K. Panyala, A. Krishnamoorthy, S. (2020). ”Green's function coupled cluster simulation of the near-valence ionizations of DNA-fragments”, Journal of Chemical Physics, 152 (1). DOI: 10.1063/1.5138658.

Minutoli, M. Drocco, M. Halappanavar, M. Tumeo, A. Kalyanaraman, A. (2020). ”CuRipples: Influence maximization on multi-CPU systems”, Proceedings of the International Conference on Supercomputing. DOI: 10.1145/3392717.3392750.

Firoz, J.S. Jenkins, L. Joslyn, C. Praggastis, B. Purvine, E. Raugas, M. (2020). ”Computing hypergraph homology in chapel”, Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020, 667-670. DOI: 10.1109/IPDPSW50202.2020.00112.

Jia, H. Gao, P. Zou, L. Han, K.S. Engelhard, M.H. He, Y. Zhang, X. Zhao, W. Yi, R. Wang, H. Wang, C. Li, X. Zhang, J.-G. (2020). ”Controlling ion coordination structure and diffusion kinetics for optimized electrode-electrolyte interphases and high-performance Si anodes”, Chemistry of Materials, 32 (20), 8956-8964. DOI: 10.1021/acs.chemmater.0c02954.

Wiebe, N. (2020). ”Key questions for the quantum machine learner to ask themselves”, New Journal of Physics, 22 (9). DOI: 10.1088/1367-2630/abac39.

Wan, H. Woodward, C.S. Zhang, S. Vogl, C.J. Stinis, P. Gardner, D.J. Rasch, P.J. Zeng, X. Larson, V.E. Singh, B. (2020). ”Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling”, Journal of Advances in Modeling Earth Systems, 12 (10). DOI: 10.1029/2019MS001982.

Tan, C. Xie, C. Li, A. Barker, K.J. Tumeo, A. (2020). ”OpenCGRA: An Open-Source Unified Framework for Modeling, Testing, and Evaluating CGRAs”, Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors, 2020, 381-388. DOI: 10.1109/ICCD50377.2020.00070.

Aksoy S.G., K.E. Nowak, and S.J. Young. 2019. "A Linear-Time Algorithm and Analysis of Graph Relative Hausdorff Distance." SIAM Journal on Mathematics of Data Science 1, no. 4:647-666. PNNL-SA-141641. DOI:10.1137/19M1248224.

Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N. (2020). Circuit-centric quantum classifiers. Physical Review A, 101 (3). DOI:10.1103/PhysRevA.101.032308.

Meng, X., Karniadakis, G.E. (2020). A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. Journal of Computational Physics, 401. DOI: 10.1016/j.jcp.2019.109020.

Jagtap, A.D., Kawaguchi, K., Karniadakis, G.E. (2020). Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. Journal of Computational Physics, 404. DOI: 10.1016/j.jcp.2019.109136.

Laureanti, J., Brandi, J., Offor, E., Engel, D., Rallo, R., Ginovska, B., Martinez, X., Baaden, M., Baker, N.A. (2020). Visualizing biomolecular electrostatics in virtual reality with UnityMol-APBS. Protein Science, 29 (1),237-246. DOI: 10.1002/pro.3773.

Gawande, N.A., Daily, J.A., Siegel, C., Tallent, N.R., Vishnu, A. (2020). Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing. Future Generation Computer Systems, 108,1162-1172. DOI: 10.1016/j.future.2018.04.073.

Kou, E., Urquijo, P., Altmannshofer, W., Beaujean, F., Bell, G., Beneke, M., Bigi, I.I., et al. (2020). Erratum: The Belle II Physics Book (Progress of Theoretical and Experimental Physics (2019) 2019 (123C01) DOI: 10.1093/ptep/ptz106). Progress of Theoretical and Experimental Physics, 2020 (2). DOI: 10.1093/ptep/ptaa008.

Alexander, F., Almgren, A., Bell, J., Bhattacharjee, A., Chen, J., et al. (2020). Exascale applications: Skin in the game. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 378 (2166). DOI: 10.1098/rsta.2019.0056.

Drgoňa, J., Picard, D., Helsen, L. (2020). Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration. Journal of Process Control, 88,63-77. DOI: 10.1016/j.jprocont.2020.02.007.

Darulová, J., Pauka, S.J., Wiebe, N., Chan, K.W., Gardener, G.C., Manfra, M.J., Cassidy, M.C., Troyer, M. (2020). Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots. Physical Review Applied, 13 (5). DOI: 10.1103/PhysRevApplied.13.054005.

Peng, B., Kowalski, K., Panyala, A., Krishnamoorthy, S. (2020). Green's function coupled cluster simulation of the near-valence ionizations of DNA-fragments. Journal of Chemical Physics, 152 (1). DOI: 10.1063/1.5138658.

Wang, Y., Xiao, Z., Hu, S., Li, Y., Shi, S.-Q. (2020). A phase field study of the thermal migration of gas bubbles in UO2 nuclear fuel under temperature gradient. Computational Materials Science, 183. DOI: 10.1016/j.commatsci.2020.109817.

Aprà, E., Bylaska, E.J., de Jong, W.A., Govind, N., Kowalski, K., Straatsma, T.P., Valiev, M., et al. (2020). NWChem: Past, present, and future. The Journal of chemical physics, 152 (18). DOI: 10.1063/5.0004997.

Abudinén, F., Adachi, I., Ahlburg, P., Aihara, H., Akopov, N., Aloisio, A., Ameli, F., Andricek, L., Anh Ky, N., et al. (2020). Measurement of the integrated luminosity of the Phase 2 data of the Belle II experiment. Chinese Physics C, 44 (2). DOI: 10.1088/1674-1137/44/2/021001.

Rehr, J.J., Vila, F.D., Kas, J.J., Hirshberg, N.Y., Kowalski, K., Peng, B. (2020). Equation of motion coupled-cluster cumulant approach for intrinsic losses in x-ray spectra. The Journal of chemical physics, 152 (17). DOI: 10.1063/5.0004865.

Bauman, N.P., Peng, B., Kowalski, K. (2020). Coupled Cluster Green's function formulations based on the effective Hamiltonians. Molecular Physics. DOI: 10.1080/00268976.2020.1725669.

Bakker, C., Bhattacharya, A., Chatterjee, S., Vrabie, D.L. (2020). Learning and Information Manipulation: Repeated Hypergames for Cyber-Physical Security. IEEE Control Systems Letters, 4 (2),295-300. DOI: 10.1109/LCSYS.2019.2925681.

Tartakovsky, A.M., Marrero, C.O., Perdikaris, P., Tartakovsky, G.D., Barajas-Solano, D. (2020). Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems. Water Resources Research, 56 (5). DOI: 10.1029/2019WR026731.

Li, A., Song, S.L., Chen, J., Li, J., Liu, X., Tallent, N.R., Barker, K.J. (2020). Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect. IEEE Transactions on Parallel and Distributed Systems, 31 (1),94-110. DOI: 10.1109/TPDS.2019.2928289. 

Zhou, W., Zhou, E., Liu, G., Lin, L., Lumsdaine, A. (2020). Unsupervised Monocular Depth Estimation from Light Field Image. IEEE Transactions on Image Processing, 29,1606-1617. DOI: 10.1109/TIP.2019.2944343.

Zou, P., Lp, A., Barker, K., Ge, R. (2020). Indicator-Directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems. Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020,559-568. DOI: 10.1109/CCGrid49817.2020.00-37.

Poudel, S., Sharma, P., Dubey, A., Schneider, K.P. (2020). Advanced FLISR with Intentional Islanding Operations in an ADMS Environment Using GridAPPS-D. IEEE Access, 8,113766-113778. DOI: 10.1109/ACCESS.2020.3003325.

Zhang, D., Guo, L., Karniadakis, G.E. (2020). Learning in modal space: Solving time-dependent stochastic PDEs using physics-informed neural networks. SIAM Journal on Scientific Computing, 42 (2), A639-A665. DOI: 10.1137/19M1260141.

Lin, P., Song, Q., Wu, Y., Pi, J. (2020). Repairing entities using star constraints in multirelational graphs. Proceedings - International Conference on Data Engineering, 2020, 229-240. DOI: 10.1109/ICDE48307.2020.00027.

Joslyn, C.A., Charles, L., Deperno, C., Gould, N., Nowak, K., Praggastis, B., Purvine, E., Robinson, M., Strules, J., Whitney, P. (2020). A sheaf theoretical approach to uncertainty quantification of heterogeneous geolocation information. Sensors (Switzerland), 20 (12),1-36. DOI: 10.3390/s20123418.

Wang, T., Geng, T., Li, A., Jin, X., Herbordt, M. (2020). FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters. IEEE Transactions on Computers, 69 (8),1143-1158. DOI: 10.1109/TC.2020.3000118.

Liu, X.T., Halappanavar, M., Barker, K.J., Lumsdaine, A., Gebremedhin, A.H. (2020). Direction-optimizing label propagation and its application to community detection. 17th ACM International Conference on Computing Frontiers 2020, CF 2020 - Proceedings,192-201. DOI: 10.1145/3387902.3392634.

Chen, X., Duan, J., Karniadakis, G.E.M. (2020). Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements. European Journal of Applied Mathematics. DOI: 10.1017/S0956792520000169. 

Chakraborty, I., Chen, Y., Li, J., Vrabie, D. (2020). Theoretical Development of Controller Transfer applied to Dynamical Systems. e-Energy 2020 - Proceedings of the 11th ACM International Conference on Future Energy Systems,445-453. DOI: 10.1145/3396851.3402368.

Shi, R., Dong, P., Geng, T., Ding, Y., Ma, X., So, H.K.-H., Herbordt, M., Li, A., Wang, Y. (2020). CSB-RNN: A faster-than-realtime RNN acceleration framework with compressed structured blocks. Proceedings of the International Conference on Supercomputing. DOI: 10.1145/3392717.3392749.

Flamini, F., Walschaers, M., Spagnolo, N., Wiebe, N., Buchleitner, A., Sciarrino, F. (2020). Validating multi-photon quantum interference with finite data. Quantum Science and Technology, 5 (4). DOI: 10.1088/2058-9565/aba03a.

Minutoli, M., Drocco, M., Halappanavar, M., Tumeo, A., Kalyanaraman, A. (2020). CuRipples: Influence maximization on multi-CPU systems. Proceedings of the International Conference on Supercomputing. DOI: 10.1145/3392717.3392750.

Singh, R.K., Bao, J., Wang, C., Fu, Y., Xu, Z. (2020). Hydrodynamics of countercurrent flows in a structured packed column: Effects of initial wetting and dynamic contact angle. Chemical Engineering Journal, 398. DOI: 10.1016/j.cej.2020.125548.

Hagen, A., Church, E., Strube, J., Bhattacharya, K., Amatya, V. (2020). Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs. Journal of Physics: Conference Series, 1525 (1). DOI: 10.1088/1742-6596/1525/1/012104.

Stinis, P., Lei, H., Li, J., Wan, H. (2020). Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise. Monthly Weather Review, 48, 2251-2263. DOI: 10.1175/MWR-D-19-0178.1.

Li, Y., Hu, S., Barker, E., Overman, N., Whalen, S., Mathaudhu, S. (2020). Effect of grain structure and strain rate on dynamic recrystallization and deformation behavior: A phase field-crystal plasticity model. Computational Materials Science, 180. https:/doi.org/10.1016/j.commatsci.2020.109707

Stinis, P. (2020). Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning. CEUR Workshop Proceedings, 2587. https:/doi.org/-

Sabet, A.H.N., Qiu, J., Zhao, Z., Krishnamoorthy, S. (2020). Reliability Analysis for Unreliable FSM Computations. ACM Transactions on Architecture and Code Optimization, 17 (2). https:/doi.org/10.1145/3377456

Reyes, B.C., Otero-Muras, I., Shuen, M.T., Tartakovsky, A.M., Petyuk, V.A. (2020). CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks. Bioinformatics (Oxford, England), 36 (12),3922-3924. https:/doi.org/10.1093/bioinformatics/btaa241

Joslyn, C.A., Aksoy, S., Arendt, D., Firoz, J., Jenkins, L., Praggastis, B., Purvine, E., Zalewski, M. (2020). Hypergraph analytics of domain name system relationships. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12091,1-15. https:/doi.org/10.1007/978-3-030-48478-1_1

Arai, E., Tartakovsky, A., Holt, R.G., Grace, S., Ryan, E. (2020). Comparison of surface tension generation methods in smoothed particle hydrodynamics for dynamic systems. Computers and Fluids, 203. https:/doi.org/10.1016/j.compfluid.2020.104540

Jestilä, J.S., Denton, J.K., Perez, E.H., Khuu, T., Aprà, E., Xantheas, S.S., Johnson, M.A., Uggerud, E. (2020). Characterization of the alkali metal oxalates (MC2O4-) and their formation by CO2 reduction: Via the alkali metal carbonites (MCO2-). Physical Chemistry Chemical Physics, 22 (14),7460-7473. https:/doi.org/10.1039/d0cp00547a

Bakker, C., Webster, J.B., Nowak, K.E., Chatterjee, S., Perkins, C.J., Brigantic, R. (2020). Multi-Game Modeling for Counter-Smuggling. Reliability Engineering and System Safety, 200. https:/doi.org/10.1016/j.ress.2020.106958

Li, J., Lakshminarasimhan, M., Wu, X., Li, A., Olschanowsky, C., Barker, K. (2020). A parallel sparse tensor benchmark suite on CPUs and GPUs. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 403-404. https:/doi.org/10.1145/3332466.3374513

Battu, A.K., Pope, T.R., Varga, T., Christ, J.F., Fenn, M.D., Rosenthal, W.S., Kuang, W., Thomas, M., Arnold, A.M., Schram, M., Warner, M.G., Barrett, C.A., Kennedy, Z.C. (2020). Build orientation dependent microstructure in polymer laser sintering: Relationship to part performance and evolution with aging. Additive Manufacturing, 36. https:/doi.org/10.1016/j.addma.2020.101464

Zhang, X., Song, S.L., Xie, C., Wang, J., Zhang, W., Fu, X. (2020). Enabling highly efficient capsule networks processing through a PIM-based architecture design. Proceedings - 2020 IEEE International Symposium on High Performance Computer Architecture, HPCA 2020,542-555. https:/doi.org/10.1109/HPCA47549.2020.00051

Biró, C., Bosek, B., Smith, H.C., Trotter, W.T., Wang, R., Young, S.J. (2020). Planar Posets that are Accessible from Below Have Dimension at Most 6. Order. https:/doi.org/10.1007/s11083-020-09525-4

Bilbrey, J.A., Marrero, C.O., Sassi, M., Ritzmann, A.M., Henson, N.J., Schram, M. (2020). Tracking the chemical evolution of iodine species using recurrent neural networks. ACS Omega, 5 (9),4588-4594. https:/doi.org/10.1021/acsomega.9b04104

Wang, S., Huang, R., Ke, X., Zhao, J., Fan, R., Wang, H., Huang, Z., Sathanur, A., Vrabie, D. (2020). Risk-oriented PMU placement approach in electric power systems. IET Generation, Transmission and Distribution, 14 (2),301-307. https:/doi.org/10.1049/iet-gtd.2019.0957

Bilbrey, J.A., Heindel, J.P., Schram, M., Bandyopadhyay, P., Xantheas, S.S., Choudhury, S. (2020). A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters. Journal of Chemical Physics, 153 (2). https:/doi.org/10.1063/5.0009933

Xiao, Z., Wang, Y., Hu, S., Li, Y., Shi, S.-Q. (2020). A quantitative phase-field model of gas bubble evolution in UO2. Computational Materials Science, 184. https:/doi.org/10.1016/j.commatsci.2020.109867

Wang, T., Upadhyay, P., Reza-E-Rabby, M., Li, X., Li, L., Soulami, A., Kappagantula, K.S., Whalen, S. (2020). Joining of thermoset carbon fiber reinforced polymer and AZ31 magnesium alloy sheet via friction stir interlocking. International Journal of Advanced Manufacturing Technology, 109 (3-4),689-698. https:/doi.org/10.1007/s00170-020-05717-9

Aksoy, S.G., Bruillard, P., Young, S.J., Raugas, M. (2020). Ramanujan graphs and the spectral gap of supercomputing topologies. Journal of Supercomputing. https:/doi.org/10.1007/s11227-020-03291-1

Tipireddy, R., Barajas-Solano, D.A., Tartakovsky, A.M. (2020). Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. Journal of Computational Physics, 418. https:/doi.org/10.1016/j.jcp.2020.109604

Pal, S., Biswas, S., Sridhar, S., Ashok, A., Hansen, J., Amatya, V. (2020). Understanding impacts of data integrity attacks on transactive control systems. 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020. https:/doi.org/10.1109/ISGT45199.2020.9087734

He, Q., Barajas-Solano, D., Tartakovsky, G., Tartakovsky, A.M. (2020). Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport. Advances in Water Resources, 141. https:/doi.org/10.1016/j.advwatres.2020.103610

Drocco, M., Castellana, V.G., Minutoli, M. (2020). Practical Distributed Programming in C++. HPDC 2020 - Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, 35-39. https:/doi.org/10.1145/3369583.3392680

Keller, M.T., Young, S.J. (2020). Hereditary semiorders and enumeration of semiorders by dimension. Electronic Journal of Combinatorics, 27 (1). https:/doi.org/10.37236/8140

Gioiosa, R., Mutlu, B.O., Lee, S., Vetter, J.S., Picierro, G., Cesati, M. (2020). The minos computing library: Efficient parallel programming for extremely heterogeneous systems. GPGPU 2020 - Proceedings of the 2020 General Purpose Processing Using GPU, 1-10. https:/doi.org/10.1145/3366428.3380770

Jagtap, A.D., Kharazmi, E., Karniadakis, G.E. (2020). Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems. Computer Methods in Applied Mechanics and Engineering, 365. https:/doi.org/10.1016/j.cma.2020.113028

Esfahani, A.A., Böser, S., Buzinsky, N., Cervantes, R., Claessens, C., De Viveiros, L., Fertl, M., Formaggio, J.A., Gladstone, L., Guigue, M., Heeger, K.M., Johnston, J., Jones, A.M., Kazkaz, K., Laroque, B.H., Lindman, A., Machado, E., Monreal, B., Morrison, E.C., Nikkel, J.A., Novitski, E., Oblath, N.S., Pettus, W., Robertson, R.G.H., Rybka, G., Saldaña, L., Sibille, V., Schram, M., Slocum, P.L., Sun, Y.-H., Thümmler, T., Vandevender, B.A., Weiss, T.E., Wendler, T., Zayas, E. (2020). Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8. New Journal of Physics, 22 (3). https:/doi.org/10.1088/1367-2630/ab71bd

Li, J., Tartakovsky, A.M. (2020). Gaussian process regression and conditional polynomial chaos for parameter estimation. Journal of Computational Physics, 416. https:/doi.org/10.1016/j.jcp.2020.109520

Thomas, M., Schram, M., Fox, K., Strube, J., Oblath, N.S., Rallo, R., Kennedy, Z.C., Varga, T., Battu, A.K., Barrett, C.A. (2020). Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. MRS Advances,1547-1555. https:/doi.org/10.1557/adv.2020.103

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Bulgac, A., Jin, S., Roche, K.J., Schunck, N., Stetcu, I. (2019). Fission dynamics of Pu 240 from saddle to scission and beyond. Physical Review C, 100 (3). https:/doi.org/10.1103/PhysRevC.100.034615

Mayer, M., Van Lessen, V., Rohdenburg, M., Hou, G.-L., Yang, Z., Exner, R.M., Aprà, E., Azov, V.A., Grabowsky, S., Xantheas, S.S., Asmis, K.R., Wang, X.-B., Jenne, C., Warneke, J. (2019). Rational design of an argon-binding superelectrophilic anion. Proceedings of the National Academy of Sciences of the United States of America, 116 (17),8167-8172. https:/doi.org/10.1073/pnas.1820812116

Gupta, V., Lee, T., Vivek, A., Choi, K.S., Mao, Y., Sun, X., Daehn, G. (2019). A robust process-structure model for predicting the joint interface structure in impact welding. Journal of Materials Processing Technology, 264,107-118. https:/doi.org/10.1016/j.jmatprotec.2018.08.047

Stinis, P., Hagge, T., Tartakovsky, A.M., Yeung, E. (2019). Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. Journal of Computational Physics, 397. https:/doi.org/10.1016/j.jcp.2019.07.042

Rakshit, A., Bandyopadhyay, P., Heindel, J.P., Xantheas, S.S. (2019). Atlas of putative minima and low-lying energy networks of water clusters n = 3-25. Journal of Chemical Physics, 151 (21). https:/doi.org/10.1063/1.5128378

Boyer, M.A., Marsalek, O., Heindel, J.P., Markland, T.E., McCoy, A.B., Xantheas, S.S. (2019). Beyond Badger's Rule: The Origins and Generality of the Structure-Spectra Relationship of Aqueous Hydrogen Bonds. Journal of Physical Chemistry Letters, 10 (5),918-924. https:/doi.org/10.1021/acs.jpclett.8b03790

Warneke, J., Konieczka, S.Z., Hou, G.-L., Aprà, E., Kerpen, C., Keppner, F., Schäfer, T.C., Deckert, M., Yang, Z., Bylaska, E.J., Johnson, G.E., Laskin, J., Xantheas, S.S., Wang, X.-B., Finze, M. (2019). Properties of perhalogenated {: Closo -B10} and { closo -B11} multiply charged anions and a critical comparison with { closo -B12} in the gas and the condensed phase. Physical Chemistry Chemical Physics, 21 (11),5903-5915. https:/doi.org/10.1039/c8cp05313h

Esfahani, A.A., Bansal, V., Böser, S., Buzinsky, N., Cervantes, R., Claessens, C., De Viveiros, L., Doe, P.J., Fertl, M., Formaggio, J.A., Gladstone, L., Guigue, M., Heeger, K.M., Johnston, J., Jones, A.M., Kazkaz, K., Laroque, B.H., Leber, M., Lindman, A., Machado, E., Monreal, B., Morrison, E.C., Nikkel, J.A., Novitski, E., Oblath, N.S., Pettus, W., Robertson, R.G.H., Rybka, G., Saldaña, L., Sibille, V., Schram, M., Slocum, P.L., Sun, Y.-H., Tedeschi, J.R., Thümmler, T., Vandevender, B.A., Wachtendonk, M., Walter, M., Weiss, T.E., Wendler, T., Zayas, E. (2019). Electron radiated power in cyclotron radiation emission spectroscopy experiments. Physical Review C, 99 (5). https:/doi.org/10.1103/PhysRevC.99.055501

Meng, K., Li, J., Tan, G., Sun, N. (2019). A pattern based algorithmic autotuner for graph processing on GPUs. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP,201-213. https:/doi.org/10.1145/3293883.3295716

Bauman, N.P., Bylaska, E.J., Krishnamoorthy, S., Low, G.H., Wiebe, N., Granade, C.E., Roetteler, M., Troyer, M., Kowalski, K. (2019). Downfolding of many-body Hamiltonians using active-space models: Extension of the sub-system embedding sub-algebras approach to unitary coupled cluster formalisms. Journal of Chemical Physics, 151 (1). https:/doi.org/10.1063/1.5094643

Li, Y.L., Zeidman, B.D., Hu, S.Y., Henager, C.H., Besmann, T.M., Grandjean, A. (2019). A physics-based mesoscale phase-field model for predicting the uptake kinetics of radionuclides in hierarchical nuclear wasteform materials. Computational Materials Science, 159,103-109. https:/doi.org/10.1016/j.commatsci.2018.11.041

Peng, B., Van Beeumen, R., Williams-Young, D.B., Kowalski, K., Yang, C. (2019). Approximate Green's Function Coupled Cluster Method Employing Effective Dimension Reduction. Journal of Chemical Theory and Computation, 15 (5),3185-3196. https:/doi.org/10.1021/acs.jctc.9b00172

Song, Q., Namaki, M.H., Wu, Y. (2019). Answering why-questions for subgraph queries in multi-attributed graphs. Proceedings - International Conference on Data Engineering, 2019,40-51. https:/doi.org/10.1109/ICDE.2019.00013

Geng, T., Wang, T., Wu, C., Yang, C., Wu, W., Li, A., Herbordt, M.C. (2019). O3BNN: An out-of-order architecture for high-performance binarized neural network inference with fine-grained pruning. Proceedings of the International Conference on Supercomputing ,461-472. https:/doi.org/10.1145/3330345.3330386

Aprà, E., Warneke, J., Xantheas, S.S., Wang, X.-B. (2019). A benchmark photoelectron spectroscopic and theoretical study of the electronic stability of [B 12 H 12 ] 2-. Journal of Chemical Physics, 150 (16). https:/doi.org/10.1063/1.5089510

MacKey, P., Porterfield, K., Fitzhenry, E., Choudhury, S., Chin, G. (2019). A Chronological Edge-Driven Approach to Temporal Subgraph Isomorphism. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018,3972-3979. https:/doi.org/10.1109/BigData.2018.8622100

Yang, X., Barajas-Solano, D., Tartakovsky, G., Tartakovsky, A.M. (2019). Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. Journal of Computational Physics, 395 ,410-431. https:/doi.org/10.1016/j.jcp.2019.06.041

Tranter, A., Love, P.J., Mintert, F., Wiebe, N., Coveney, P.V. (2019). Ordering of Trotterization: Impact on errors in quantum simulation of electronic structure. Entropy, 21 (12). https:/doi.org/10.3390/e21121218

Firoz, J.S., Zalewski, M., Kanewala, T., Lumsdaine, A. (2019). Synchronization-Avoiding Graph Algorithms. Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018, 52-61. https:/doi.org/10.1109/HiPC.2018.00015

Kim, J., Sukumaran-Rajam, A., Thumma, V., Krishnamoorthy, S., Panyala, A., Pouchet, L.-N., Rountev, A., Sadayappan, P. (2019). A Code Generator for High-Performance Tensor Contractions on GPUs. CGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization, 85-95. https:/doi.org/10.1109/CGO.2019.8661182

Huang, Q., Huang, R., Palmer, B.J., Liu, Y., Jin, S., Diao, R., Chen, Y., Zhang, Y. (2019). A generic modeling and development approach for WECC composite load model. Electric Power Systems Research, 172, 1-10. https:/doi.org/10.1016/j.epsr.2019.02.027

Lei, H., Li, J., Gao, P., Stinis, P., Baker, N.A. (2019). A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness. Computer Methods in Applied Mechanics and Engineering, 350,199-227. https:/doi.org/10.1016/j.cma.2019.03.014

Pouchard, L., Baldwin, S., Elsethagen, T., Jha, S., Raju, B., Stephan, E., Tang, L., Van Dam, K.K. (2019). Computational reproducibility of scientific workflows at extreme scales. International Journal of High Performance Computing Applications, 33 (5),763-776. https:/doi.org/10.1177/1094342019839124

Geng, T., Wang, T., Wu, C., Yang, C., Song, S.L., Li, A., Herbordt, M. (2019). LP-BNN: Ultra-low-latency BNN inference with layer parallelism. Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors, 2019, 9-16. https:/doi.org/10.1109/ASAP.2019.00-43

Cromwell, E., Flynn, D. (2019). Lidar cloud detection with fully convolutional networks. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 619-627. https:/doi.org/10.1109/WACV.2019.00071

Li, Y., Hu, S., Henager, C.H. (2019). Microstructure-based model of nonlinear ultrasonic response in materials with distributed defects. Journal of Applied Physics, 125 (14). https:/doi.org/10.1063/1.5083957

Mutlu, B.O., Kestor, G., Manzano, J., Unsal, O., Chatterjee, S., Krishnamoorthy, S. (2019). Characterization of the Impact of Soft Errors on Iterative Methods. Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018, 203-214. https:/doi.org/10.1109/HiPC.2018.00031

Li, J., Stinis, P. (2019). Mori-Zwanzig reduced models for uncertainty quantification. Journal of Computational Dynamics, 6 (1),39-68. https:/doi.org/10.3934/jcd.2019002

Wang, X., Tumeo, A., Leidel, J.D., Li, J., Chen, Y. (2019). MAC: Memory access coalescer for 3D-stacked memory. ACM International Conference Proceeding Series. https:/doi.org/10.1145/3337821.3337867

Dobrian, F., Halappanavar, M., Pothen, A., Al-Herz, A. (2019). A 2/3-approximation algorithm for vertex weighted matching in bipartite graphs. SIAM Journal on Scientific Computing, 41 (1), A566-A591. https:/doi.org/10.1137/17M1140029

Cottam, J.A., Purohit, S., Mackey, P., Chin, G. (2019). Multi-Channel Large Network Simulation Including Adversarial Activity. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 3947-3950. https:/doi.org/10.1109/BigData.2018.8622305

Shigorina, E., Tartakovsky, A.M., Kordilla, J. (2019). Investigation of gravity-driven infiltration instabilities in smooth and rough fractures using a pairwise-force smoothed particle hydrodynamics model. Vadose Zone Journal, 18 (1). https:/doi.org/10.2136/vzj2018.08.0159

Xie, C., Zhang, X., Li, A., Fu, X., Song, S. (2019). PIM-VR: Erasing motion anomalies in highly-interactive virtual reality world with customized memory cube. Proceedings - 25th IEEE International Symposium on High Performance Computer Architecture, HPCA 2019, 609-622. https:/doi.org/10.1109/HPCA.2019.00013

Singh, A., Altintas, I., Schram, M., Tallent, N. (2019). Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 3905-3914. https:/doi.org/10.1109/BigData.2018.8622509

Liu, J., Li, D., Kestor, G., Vetter, J. (2019). Runtime concurrency control and operation scheduling for high performance neural network training. Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019, 188-199. https:/doi.org/10.1109/IPDPS.2019.00029

Li, J., Uçar, B., Çatalyürek, Ü.V., Sun, J., Barker, K., Vuduc, R. (2019). Efficient and effective sparse tensor reordering. Proceedings of the International Conference on Supercomputing ,227-237. https:/doi.org/10.1145/3330345.3330366

Li, A., Geng, T., Wang, T., Herbordt, M., Song, S.L., Barker, K. (2019). BSTC: A novel binarized-soft-tensor-core design for accelerating bit-based approximated neural nets. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. https:/doi.org/10.1145/3295500.3356169

Barajas-Solano, D.A., Tartakovsky, A.M. (2019). Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. Journal of Computational Physics, 395, 247-262. https:/doi.org/10.1016/j.jcp.2019.06.010

Yang, L., Treichler, S., Kurth, T., Fischer, K., Barajas-Solano, D., Romero, J., Churavy, V., Tartakovsky, A., Houston, M., Prabhat, Karniadakis, G. (2019). Highly-Scalable, physics-informed GANs for learning solutions of stochastic PDEs. Proceedings of DLS 2019: Deep Learning on Supercomputers - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis ,1-11. https:/doi.org/10.1109/DLS49591.2019.00006

Castellana, V.G., Drocco, M., Feo, J., Firoz, J., Kanewala, T., Lumsdaine, A., Manzano, J., Marquez, A., Minutoli, M., Suetterlein, J., Tumeo, A., Zalewski, M. (2019). A Parallel Graph Environment for Real-World Data Analytics Workflows. Proceedings of the 2019 Design, Automation and Test in Europe Conference and Exhibition, DATE 2019,1313-1318. https:/doi.org/10.23919/DATE.2019.8715196

Dong, J., Ramachandran, T., Im, P., Huang, S., Chandan, V., Vrabie, D.L., Kuruganti, T. (2019). Online learning for commercial buildings. e-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems,522-530. https:/doi.org/10.1145/3307772.3331029

Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanaraman, A., Gebremedhin, A.H. (2019). MiniVite: A graph analytics benchmarking tool for massively parallel systems. Proceedings of PMBS 2018: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis,51-56. https:/doi.org/10.1109/PMBS.2018.8641631

Nur, N., Sridhar, S., Pal, S., Ashok, A., Amatya, V.C. (2019). A clustering approach for consumer baselining and anomaly detection in transactive control. e-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems,516-521. https:/doi.org/10.1145/3307772.3331028

Bemis, K.A., Guo, D., Harry, A.J., Thomas, M., Lanekoff, I., Stenzel-Poore, M.P., Stevens, S.L., Laskin, J., Vitek, O. (2019). Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs. International Journal of Mass Spectrometry, 437, 49-57. https:/doi.org/10.1016/j.ijms.2018.07.006

Price, J., Stinis, P. (2019). Renormalized reduced order models with memory for long time prediction. Multiscale Modeling and Simulation, 17 (1),68-91. https:/doi.org/10.1137/17M1151389

Mutlu, E., Kowalski, K., Krishnamoorthy, S. (2019). Toward generalized tensor algebra for ab initio Quantum chemistry methods. Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI),46-56. https:/doi.org/10.1145/3315454.3329958

Fang, B., Halawa, H., Pattabiraman, K., Ripeanu, M., Krishnamoorthy, S. (2019). BonVoision: Leveraging spatial data smoothness for recovery from memory soft errors. Proceedings of the International Conference on Supercomputing ,484-496. https:/doi.org/10.1145/3330345.3330388

Liu, X., Firoz, J.S., Zalewski, M., Halappanavar, M., Barker, K.J., Lumsdaine, A., Gebremedhin, A.H. (2019). Distributed direction-optimizing label propagation for community detection. 2019 IEEE High Performance Extreme Computing Conference, HPEC 2019. https:/doi.org/10.1109/HPEC.2019.8916215

Khan, A., Choromanski, K., Pothen, A., Ferdous, S.M., Halappanavar, M., Tumeo, A. (2019). Adaptive anonymization of data using b-edge cover. Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, 743-753. https:/doi.org/10.1109/SC.2018.00062

Bhuiyan, T.H., Halappanavar, M., Friese, R.D., Medal, H., de la Torre, L., Sathanur, A., Tallent, N.R. (2019). Stochastic programming approach for resource selection under demand uncertainty. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11332 ,107-126. https:/doi.org/10.1007/978-3-030-10632-4_6

Spies, K.A., Viswanathan, V.V., Soulami, A., Hovanski, Y., Joshi, V.V. (2019). Galvanically Graded Interface: A Computational Model for Mitigating Galvanic Corrosion Between Magnesium and Mild Steel. Minerals, Metals and Materials Series, 135-144. https:/doi.org/10.1007/978-3-030-05789-3_21

Aksoy, S.G., Nowak, K.E., Purvine, E., Young, S.J. (2019). Relative Hausdorff distance for network analysis. Applied Network Science, 4 (1). https:/doi.org/10.1007/s41109-019-0198-0

Castellana, V.G., Lattuada, M., Minutoli, M., Fezzardi, P., Tumeo, A., Ferrandi, F. (2019). Software defined architectures for data analytics. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, 711-718. https:/doi.org/10.1145/3287624.3288754

Nisa, I., Li, J., Sukumaran-Rajam, A., Vuduc, R., Sadayappan, P. (2019). Load-balanced sparse MTTKRP on GPUS. Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019, 123-133. https:/doi.org/10.1109/IPDPS.2019.00023

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Fang, B., Chen, J., Pattabiraman, K., Ripeanu, M., Krishnamoorthy, S. (2019). Towards Predicting the Impact of Roll-Forward Failure Recovery for HPC Applications. Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, 13-14. https:/doi.org/10.1109/DSN-S.2019.00012

Lim, K.S., Riihimaki, L.D., Shi, Y., Flynn, D., Kleiss, J.M., Berg, L.K., Gustafson, W.I., Zhang, Y., Johnson, K.L. (2019). Long-term retrievals of cloud type and fair-weather shallow cumulus events at the ARM SGP site. Journal of Atmospheric and Oceanic Technology, 36 (10),2031-2043. https:/doi.org/10.1175/JTECH-D-18-0215.1

Mutlu, B.O., Kestor, G., Cristal, A., Unsal, O., Krishnamoorthy, S. (2019). Ground-Truth Prediction to Accelerate Soft-Error Impact Analysis for Iterative Methods. Proceedings - 26th IEEE International Conference on High Performance Computing, HiPC 2019, 333-344. https:/doi.org/10.1109/HiPC.2019.00048

Zou, P., Li, A., Barker, K., Ge, R. (2019). Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines∗. Proceedings of the 2019 IEEE International Symposium on Workload Characterization, IISWC 2019, 253-256. https:/doi.org/10.1109/IISWC47752.2019.9042165

Kumar, S., Eyraud-Dubois, L., Krishnamoorthy, S. (2019). Performance models for data transfers: A case study with molecular chemistry kernels. ACM International Conference Proceeding Series. https:/doi.org/10.1145/3337821.3337921

Choi, K.S., Barker, E.I., Sun, X., Song, J., Xiong, S.-M., Forsmark, J., Li, M. (2019). An integrated two-dimensional modeling method for predicting ductility of thin-walled die cast magnesium. International Journal of Fracture, 219 (2),203-220. https:/doi.org/10.1007/s10704-019-00390-w

Gawande, N., Kowalski, K., Palmer, B., Krishnamoorthy, S., Apra, E., Manzano, J., Amatya, V., Crawford, J. (2019). Accelerating the Global Arrays ComEx Runtime Using Multiple Progress Ranks. Proceedings of ExaMPI 2019: Workshop on Exascale MPI - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis, 29-38. https:/doi.org/10.1109/ExaMPI49596.2019.00009

Jenkins, L., Firoz, J.S., Zalewski, M., Joslyn, C., Raugas, M. (2019). Graph algorithms in PGAS: Chapel and UPC++. 2019 IEEE High Performance Extreme Computing Conference, HPEC 2019. https:/doi.org/10.1109/HPEC.2019.8916309

Young, J.S., Hein, E., Eswar, S., Lavin, P., Li, J., Riedy, J., Vuduc, R., Conte, T. (2019). A microbenchmark characterization of the Emu chick. Parallel Computing, 87, 60-69. https:/doi.org/10.1016/j.parco.2019.04.012

Bin, R.E.N., Balakrishna, S., Jo, Y., Krishnamoorthy, S., Agrawal, K., Kulkarni, M. (2019). Extracting SIMD parallelism from recursive task-parallel programs. ACM Transactions on Parallel Computing, 6 (4). https:/doi.org/10.1145/3365663

Firoz, J.S., Zalewski, M., Suetterlein, J., Lumsdaine, A. (2019). Adaptive Runtime Features for Distributed Graph Algorithms. Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018, 82-91. https:/doi.org/10.1109/HiPC.2018.00018

Friese, R., Tumeo, A., Gioiosa, R., Raugas, M., Warfel, T. (2019). Advert: An Asynchronous Runtime for Fine-Grained Network Systems. Proceedings of IPDRM 2019: 3rd Annual Workshop on Emerging Parallel and Distributed Runtime Systems and Middleware - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis, 9-17. https:/doi.org/10.1109/IPDRM49579.2019.00006

Chakraborty, I., Chandan, V., Vrabie, D. (2019). A sequential DNN based baseline energy prediction framework with long term error mitigation. e-Energy 2019 - Proceedings of the 10th ACM International Conference on Future Energy Systems, 508-515. https:/doi.org/10.1145/3307772.3331027

Tumeo, A., Feo, J., Villa, O. (2019). Special Issue on: Systems for Learning, Inferencing, and Discovering (SLID). Journal of Parallel and Distributed Computing, 129, 59-60. https:/doi.org/10.1016/j.jpdc.2019.04.001

Ghosh, P., Krishnamoorthy, S., Kalyanaraman, A. (2019). Pakman: Scalable assembly of large genomes on distributed memory machines. Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019, 578-589. https:/doi.org/10.1109/IPDPS.2019.00067

Fernandez, C.A., Gupta, V., Dai, G.L., Kuprat, A.P., Bonneville, A., Appriou, D., Horner, J.A., Martin, P.F., Burghardt, J.A. (2019). Insights into a Greener Stimuli-Responsive Fracturing Fluid for Geothermal Energy Recovery. ACS Sustainable Chemistry and Engineering, 7 (24),19660-19668. https:/doi.org/10.1021/acssuschemeng.9b04802

Cromwell, E., Flynn, D. (2019). Lidar Cloud Detection with Fully Convoltional Networks. Optics InfoBase Conference Papers, 2019. https:/doi.org/-

Kilic, O.O., Tallent, N.R., Friese, R.D. (2019). Rapidly Measuring Loop Footprints. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2019-. https:/doi.org/10.1109/CLUSTER.2019.8891025

Sathanur, A.V., Sripimonwan, B., Halappanavar, M., Chatterjee, S., Ganguly, A., Clark, K. (2019). Identification of Critical Airports from the Perspective of Delay and Disruption Propagation in Air Travel Networks. 2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019. https:/doi.org/10.1109/HST47167.2019.9032999

Nisa, I., Li, J., Sukumaran-Rajam, A., Rawat, P.S., Krishnamoorthy, S., Sadayappan, P. (2019). An efficient mixed-mode representation of sparse tensors. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. https:/doi.org/10.1145/3295500.3356216

Suetterlein, J., Friese, R.D., Tallent, N.R., Schram, M. (2019). TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, 383-394. https:/doi.org/10.1109/BigData47090.2019.9006418

Wickramasinghe, U., Lumsdaine, A., Ekanayake, S., Swany, M. (2019). RDMA managed buffers: A case for accelerating communication bound processes via fine-grained events for zero-copy message passing. Proceedings - 2019 18th International Symposium on Parallel and Distributed Computing, ISPDC 2019, 121-130. https:/doi.org/10.1109/ISPDC.2019.00025

Dylewsky, D., Yang, X., Tartakovsky, A., Kutz, J.N. (2019). Engineering structural robustness in power grid networks susceptible to community desynchronization. Applied Network Science, 4 (1). https:/doi.org/10.1007/s41109-019-0137-0

Firoz, J.S., Zalewski, M., Lumsdaine, A. (2019). A Synchronization-Avoiding distance-1 grundy coloring algorithm for power-law graphs. Parallel Architectures and Compilation Techniques - Conference Proceedings, PACT, 2019, 420-431. https:/doi.org/10.1109/PACT.2019.00040

Ashtari Esfahani, A., Böser, S., Buzinsky, N., Cervantes, R., Claessens, C., Viveiros, L.D., Fertl, M., Formaggio, J.A., Gladstone, L., Guigue, M., Heeger, K.M., Johnston, J., Jones, A.M., Kazkaz, K., Laroque, B.H., Lindman, A., Machado, E., Monreal, B., Morrison, E.C., Nikkel, J.A., Novitski, E., Oblath, N.S., Pettus, W., Robertson, R.G.H., Rybka, G., Saldaña, L., Sibille, V., Schram, M., Slocum, P.L., Sun, Y.-H., Tedeschi, J.R., Thümmler, T., Vandevender, B.A., Wachtendonk, M., Walter, M., Weiss, T.E., Wendler, T., Zayas, E. (2019). Locust: C++ software for simulation of RF detection. New Journal of Physics, 21 (11). https:/doi.org/10.1088/1367-2630/ab550d

Bakker, C., Nowak, K.E., Steven Rosenthal, W. (2019). Learning Koopman Operators for Systems with Isolated Critical Points. Proceedings of the IEEE Conference on Decision and Control, 2019, 7733-7739. https:/doi.org/10.1109/CDC40024.2019.9029818

Cromwell, E., Flynn, D. (2019). Lidar cloud detection with fully convoltional networks. Optics and Photonics for Sensing the Environment - Proceedings Optical Sensors and Sensing Congress (ES, FTS, HISE, Sensors). https:/doi.org/-

Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanarainan, A. (2019). Scaling and quality of modularity optimization methods for graph clustering. 2019 IEEE High Performance Extreme Computing Conference, HPEC 2019. https:/doi.org/10.1109/HPEC.2019.8916299

Barajas-Solano, D.A., Alexander, F.J., Anghel, M., Tartakovsky, D.M. (2019). Efficient gHMC Reconstruction of Contaminant Release History. Frontiers in Environmental Science, 7. https:/doi.org/10.3389/fenvs.2019.00149

Zhou, W., Zhou, E., Yan, Y., Lin, L., Lumsdaine, A. (2019). Learning Depth Cues from Focal Stack for Light Field Depth Estimation. Proceedings - International Conference on Image Processing, ICIP, 2019,1074-1078. https:/doi.org/10.1109/ICIP.2019.8804270

Li, J., Wang, X., Tumeo, A., Williams, B., Leidel, J.D., Chen, Y. (2019). PIMS: A lightweight processing-in-memory accelerator for stencil computations. ACM International Conference Proceeding Series, 41-52. https:/doi.org/10.1145/3357526.3357550

Sathanur, A.V., Amatya, V., Khan, A., Rallo, R., Maass, K. (2019). Graph analytics and optimization methods for insights from the uber movement data. Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities, SCC 2019. https:/doi.org/10.1145/3357492.3358625

Tumeo, A., Castellana, V.G., Feo, J. (2019). Foreword. Proceedings of IA3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis , VII-VIII. https:/doi.org/10.1109/IA3.2018.00005

Khan, A., Halappanavar, M., Hagge, T., Kowalski, K., Pothen, A., Krishnamoorthy, S. (2019). Mapping Arbitrarily Sparse Two-Body Interactions on One-Dimensional Quantum Circuits. Proceedings - 26th IEEE International Conference on High Performance Computing, HiPC 2019, 52-62. https:/doi.org/10.1109/HiPC.2019.00018

Xie, C., Xin, F., Chen, M., Song, S.L. (2019). OO-VR: NUMA friendly object-oriented VR rendering framework for future NUMA-based multi-GPU systems. Proceedings - International Symposium on Computer Architecture, 53-65. https:/doi.org/10.1145/3307650.3322247

Ghosh, S., Halappanavar, M., Kalyanaraman, A., Khan, A., Gebremedhin, A.H. (2019). Exploring MPI communication models for graph applications using graph matching as a case study. Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019, 761-770. https:/doi.org/10.1109/IPDPS.2019.00085

Guan, S., Ma, H., Wu, Y. (2019). Attribute-driven backbone discovery. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 187-195. https:/doi.org/10.1145/3292500.3330934

Chen, P.-Y., Choudhury, S., Rodriguez, L., Hero, A.O., Ray, I. (2019). Toward cyber-resiliency metrics for action recommendations against lateral movement attacks. Advances in Information Security, 75, 71-92. https:/doi.org/10.1007/978-3-030-18214-4_5

Mutlu, E., Panyala, A., Krishnamoorthy, S. (2019). HPC Software Verification in Action: A Case Study with Tensor Transposition. Proceedings of Correctness 2018: 2nd International Workshop on Software Correctness for HPC Applications, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis, 9-16. https:/doi.org/10.1109/Correctness.2018.00006

Ishiuchi, S.-I., Wako, H., Xantheas, S.S., Fujii, M. (2019). Probing the selectivity of Li+ and Na+ cations on noradrenaline at the molecular level. Faraday Discussions, 217, 396-413. https:/doi.org/10.1039/c8fd00186c

Hofer, W., Edgar, T., Vrabie, D., Nowak, K. (2019). Model-driven Deception for Control System Environments. 2019 IEEE International Symposium on Technologies for Homeland Security, HST 2019. https:/doi.org/10.1109/HST47167.2019.9032927

Joardar, B.K., Ghosh, P., Pande, P.P., Kalyanaraman, A., Krishnamoorthy, S. (2019). Noc-enabled software/hardware co-design framework for accelerating k-mer counting. Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2019. https:/doi.org/10.1145/3313231.3352367

Subasi, O., Tipireddy, R., Krishnamoorthy, S. (2019). Quantification, Trade-off Analysis, and Optimal Checkpoint Placement for Reliability and Availability. Proceedings - 25th IEEE International Conference on High Performance Computing, HiPC 2018, 183-192. https:/doi.org/10.1109/HiPC.2018.00029

Tumeo, A., Castellana, V.G., Feo, J. (2019). Message from the workshop co-chairs. 2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms, IA3 2019. https:/doi.org/10.1109/IA349570.2019.00004

2018

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Wang, L., Ye, J., Zhao, Y., Wu, W., Li, A., Song, S.L., Xu, Z., Kraska, T. (2018). SuperNeurons: Dynamic GPU memory management for training deep neural networks. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 41-53. https:/doi.org/10.1145/3178487.3178491

Seo, S., Amer, A., Balaji, P., Bordage, C., Bosilca, G., Brooks, A., Carns, P., Castello, A., Genet, D., Herault, T., Iwasaki, S., Jindal, P., Kale, L.V., Krishnamoorthy, S., Lifflander, J., Lu, H., Meneses, E., Snir, M., Sun, Y., Taura, K., Beckman, P. (2018). Argobots: A Lightweight Low-Level Threading and Tasking Framework. IEEE Transactions on Parallel and Distributed Systems, 29 (3),512-526. https:/doi.org/10.1109/TPDS.2017.2766062

Mukhopadhyay, A., Xantheas, S.S., Saykally, R.J. (2018). The water dimer II: Theoretical investigations. Chemical Physics Letters, 700, 163-175. https:/doi.org/10.1016/j.cplett.2018.03.057

Ling, B., Oostrom, M., Tartakovsky, A.M., Battiato, I. (2018). Hydrodynamic dispersion in thin channels with micro-structured porous walls. Physics of Fluids, 30 (7). https:/doi.org/10.1063/1.5031776

Tallent, N.R., Gawande, N.A., Siegel, C., Vishnu, A., Hoisie, A. (2018). Evaluating On-Node GPU interconnects for deep learning workloads. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10724, 3-21. https:/doi.org/10.1007/978-3-319-72971-8_1

Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanaraman, A., Lu, H., Chavarria-Miranda, D., Khan, A., Gebremedhin, A. (2018). Distributed louvain algorithm for graph community detection. Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, 885-895. https:/doi.org/10.1109/IPDPS.2018.00098

Song, Q., Wu, Y., Lin, P., Dong, L.X., Sun, H. (2018). Mining Summaries for Knowledge Graph Search. IEEE Transactions on Knowledge and Data Engineering, 30 (10),1887-1900. https:/doi.org/10.1109/TKDE.2018.2807442

Feldman, D.R., Collins, W.D., Biraud, S.C., Risser, M.D., Turner, D.D., Gero, P.J., Tadić, J., Helmig, D., Xie, S., Mlawer, E.J., Shippert, T.R., Torn, M.S. (2018). Observationally derived rise in methane surface forcing mediated by water vapour trends. Nature Geoscience, 11 (4),238-243. https:/doi.org/10.1038/s41561-018-0085-9

Veličković, D., Chu, R.K., Carrell, A.A., Thomas, M., Paša-Tolić, L., Weston, D.J., Anderton, C.R. (2018). Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS2 Increases Confidence in Both Molecular Identification and Localization. Analytical Chemistry, 90 (1),702-707. https:/doi.org/10.1021/acs.analchem.7b04319

Peng, B., Kowalski, K. (2018). Green's Function Coupled-Cluster Approach: Simulating Photoelectron Spectra for Realistic Molecular Systems. Journal of Chemical Theory and Computation, 14 (8),4335-4352. https:/doi.org/10.1021/acs.jctc.8b00313

Shen, D., Li, A., Song, S.L., Liu, X. (2018). Cudaadvisor: Llvm-based runtime profiling for modern GPUs. CGO 2018 - Proceedings of the 2018 International Symposium on Code Generation and Optimization, 2018, 214-227. https:/doi.org/10.1145/3168831

Li, A., Song, S.L., Chen, J., Liu, X., Tallent, N., Barker, K. (2018). Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite. 2018 IEEE International Symposium on Workload Characterization, IISWC 2018, 191-202. https:/doi.org/10.1109/IISWC.2018.8573483

Heindel, J.P., Yu, Q., Bowman, J.M., Xantheas, S.S. (2018). Benchmark Electronic Structure Calculations for H3O+(H2O)n, n = 0-5, Clusters and Tests of an Existing 1,2,3-Body Potential Energy Surface with a New 4-Body Correction. Journal of Chemical Theory and Computation, 14 (9),4553-4566. https:/doi.org/10.1021/acs.jctc.8b00598

Ansari, T.Q., Xiao, Z., Hu, S., Li, Y., Luo, J.-L., Shi, S.-Q. (2018). Phase-field model of pitting corrosion kinetics in metallic materials. npj Computational Materials, 4 (1). https:/doi.org/10.1038/s41524-018-0089-4

Li, A., Liu, W., Wang, L., Barker, K., Song, S.L. (2018). Warp-consolidation: A novel execution model for GPUs. Proceedings of the International Conference on Supercomputing ,53-64. https:/doi.org/10.1145/3205289.3205294

Peng, B., Kowalski, K. (2018). Properties of advanced coupled-cluster Green's function. Molecular Physics, 116 (5-6),561-569. https:/doi.org/10.1080/00268976.2017.1351630

Naas, A.E., Solden, L.M., Norbeck, A.D., Brewer, H., Hagen, L.H., Heggenes, I.M., McHardy, A.C., Mackie, R.I., Paša-Tolic, L., Arntzen, M.Ø., Eijsink, V.G.H., Koropatkin, N.M., Hess, M., Wrighton, K.C., Pope, P.B. (2018). "Candidatus Paraporphyromonas polyenzymogenes" encodes multi-modular cellulases linked to the type IX secretion system. Microbiome, 6 (1). https:/doi.org/10.1186/s40168-018-0421-8

Ye, F.X.-F., Stinis, P., Qian, H. (2018). Dynamic looping of a free-draining polymer. SIAM Journal on Applied Mathematics, 78 (1),104-123. https:/doi.org/10.1137/17M1127260

Melton, R.B., Schneider, K.P., Lightner, E., McDermott, T.E., Sharma, P., Zhang, Y., Ding, F., Vadari, S., Podmore, R., Dubey, A., Wies, R.W., Stephan, E.G. (2018). Leveraging standards to create an open platform for the development of advanced distribution applications. IEEE Access, 6 ,37361-37370. https:/doi.org/10.1109/ACCESS.2018.2851186

Pan, L., Gao, P., Tervoort, E., Tartakovsky, A.M., Niederberger, M. (2018). Surface energy-driven ex situ hierarchical assembly of low-dimensional nanomaterials on graphene aerogels: A versatile strategy. Journal of Materials Chemistry A, 6 (38),18551-18560. https:/doi.org/10.1039/c8ta07338d

Nandanoori, S.P., Kundu, S., Vrabie, D., Kalsi, K., Lian, J. (2018). Prioritized Threshold Allocation for Distributed Frequency Response. 2018 IEEE Conference on Control Technology and Applications, CCTA 2018, 237-244. https:/doi.org/10.1109/CCTA.2018.8511411

Huang, R., Jin, S., Chen, Y., Diao, R., Palmer, B., Huang, Q., Huang, Z. (2018). Faster than real-time dynamic simulation for large-size power system with detailed dynamic models using high-performance computing platform. IEEE Power and Energy Society General Meeting, 2018, 1-5. https:/doi.org/10.1109/PESGM.2017.8274505

Escorihuela, L., Fernández, A., Rallo, R., Martorell, B. (2018). Molecular dynamics simulations of zinc oxide solubility: From bulk down to nanoparticles. Food and Chemical Toxicology, 112, 518-525. https:/doi.org/10.1016/j.fct.2017.07.038

Subasi, O., Di, S., Bautista-Gomez, L., Balaprakash, P., Unsal, O., Labarta, J., Cristal, A., Krishnamoorthy, S., Cappello, F. (2018). Exploring the capabilities of support vector machines in detecting silent data corruptions. Sustainable Computing: Informatics and Systems, 19, 277-290. https:/doi.org/10.1016/j.suscom.2018.01.004

Silber, I., Verlinde, J., Eloranta, E.W., Flynn, C.J., Flynn, D.M. (2018). Polar Liquid Cloud Base Detection Algorithms for High Spectral Resolution or Micropulse Lidar Data. Journal of Geophysical Research: Atmospheres, 123 (8),4310-4322. https:/doi.org/10.1029/2017JD027840

Roy, P., Song, S.L., Krishnamoorthy, S., Vishnu, A., Sengupta, D., Liu, X. (2018). NUMA-Caffe: NUMA-Aware Deep Learning Neural Networks. ACM Transactions on Architecture and Code Optimization, 15 (2). https:/doi.org/10.1145/3199605

Zhou, W., Liang, L., Zhang, H., Lumsdaine, A., Lin, L. (2018). Scale and Orientation Aware EPI-Patch Learning for Light Field Depth Estimation. Proceedings - International Conference on Pattern Recognition, 2018- ,2362-2367. https:/doi.org/10.1109/ICPR.2018.8545490

Liu, G., Miliordos, E., Ciborowski, S.M., Tschurl, M., Boesl, U., Heiz, U., Zhang, X., Xantheas, S.S., Bowen, K. (2018). Communication: Water activation and splitting by single metal-atom anions. Journal of Chemical Physics, 149 (22). https:/doi.org/10.1063/1.5050913

Duan, Q., Al-Shaer, E., Chatterjee, S., Halappanavar, M., Oehmen, C. (2018). Proactive routing mutation against stealthy Distributed Denial of Service attacks: metrics, modeling, and analysis. Journal of Defense Modeling and Simulation, 15 (2),219-230. https:/doi.org/10.1177/1548512917731002

Setyawan, W., Cooper, M.W.D., Roche, K.J., Kurtz, R.J., Uberuaga, B.P., Andersson, D.A., Wirth, B.D. (2018). Atomistic model of xenon gas bubble re-solution rate due to thermal spike in uranium oxide. Journal of Applied Physics, 124 (7). https:/doi.org/10.1063/1.5042770

Friese, R.D., Halappanavar, M., Sathanur, A.V., Schram, M., Kerbyson, D.J., de la Torre, L. (2018). Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10773, 103-121. https:/doi.org/10.1007/978-3-319-77398-8_6

Tang, Q., Xie, S., Zhang, Y., Phillips, T.J., Santanello, J.A., Cook, D.R., Riihimaki, L.D., Gaustad, K.L. (2018). Heterogeneity in Warm-Season Land-Atmosphere Coupling Over the U.S. Southern Great Plains. Journal of Geophysical Research: Atmospheres, 123 (15),7867-7882. https:/doi.org/10.1029/2018JD028463

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Yang, X., Lei, H., Gao, P., Thomas, D.G., Mobley, D.L., Baker, N.A. (2018). Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations. Journal of Chemical Theory and Computation, 14 (2),759-767. https:/doi.org/10.1021/acs.jctc.7b00905

Rosenthal, W.S., Tartakovsky, A.M., Huang, Z. (2018). Ensemble Kalman Filter for Dynamic State Estimation of Power Grids Stochastically Driven by Time-Correlated Mechanical Input Power. IEEE Transactions on Power Systems, 33 (4),3701-3710. https:/doi.org/10.1109/TPWRS.2017.2764492

Blaziak, K., Tzeli, D., Xantheas, S.S., Uggerud, E. (2018). The activation of carbon dioxide by first row transition metals (Sc-Zn). Physical Chemistry Chemical Physics, 20 (39),25495-25505. https:/doi.org/10.1039/c8cp04231d

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Hong, C., Sukumaran-Rajam, A., Kim, J., Rawat, P.S., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Sadayappan, P. (2018). GPU code optimization using abstract kernel emulation and sensitivity analysis. Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI),736-751. https:/doi.org/10.1145/3192366.3192397

Castallana, V.G., Minutoli, M. (2018). SHAD: The scalable high-performance algorithms and data-structures library. Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, 442-451. https:/doi.org/10.1109/CCGRID.2018.00071

Song, Q., Zong, B., Wu, Y., Tang, L.-A., Zhang, H., Jiang, G., Chen, H. (2018). TGNet: Learning to rank nodes in temporal graphs. International Conference on Information and Knowledge Management, Proceedings,97-106. https:/doi.org/10.1145/3269206.3271698

Wang, L., Ye, J., Zhao, Y., Wu, W., Li, A., Song, S.L., Xu, Z., Kraska, T. (2018). SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks. ACM SIGPLAN Notices, 53 (1),41-53. https:/doi.org/10.1145/3178487.3178491

Kundu, S., Ramachandran, T., Chen, Y., Vrabie, D. (2018). Optimal Energy Consumption Forecast for Grid Responsive Buildings: A Sensitivity Analysis. 2018 IEEE Conference on Control Technology and Applications, CCTA 2018, 230-236. https:/doi.org/10.1109/CCTA.2018.8511607

Kanewala, T., Zalewski, M., Lumsdaine, A. (2018). Parallel asynchronous distributed-memory maximal independent set algorithm with work ordering. Proceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017, 2017, 52-61. https:/doi.org/10.1109/HiPC.2017.00016

Aksoy, S.G., Purvine, E., Cotilla-Sanchez, E., Halappanavar, M., Lambiotte, R. (2018). A generative graph model for electrical infrastructure networks. Journal of Complex Networks, 7 (1),128-162. https:/doi.org/10.1093/comnet/cny016

Firoz, J.S., Zalewski, M., Lumsdaine, A., Barnas, M. (2018). Runtime scheduling policies for distributed graph algorithms. Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, 640-649. https:/doi.org/10.1109/IPDPS.2018.00073

Hou, G.-L., Govind, N., Xantheas, S.S., Wang, X.-B. (2018). Deviation from the trans-Effect in Ligand-Exchange Reactions of Zeise's Ions PtCl3(C2H4)- with Heavier Halides (Br-, I-). Journal of Physical Chemistry A, 122 (5),1209-1214. https:/doi.org/10.1021/acs.jpca.7b10808

Tipireddy, R., Stinis, P., Tartakovsky, A.M. (2018). Stochastic basis adaptation and spatial domain decomposition for partial differential equations with random coefficients. SIAM-ASA Journal on Uncertainty Quantification, 6 (1),273-301. https:/doi.org/10.1137/16M1097134

Bakker, C., Halappanavar, M., Visweswara Sathanur, A. (2018). Dynamic graphs, community detection, and Riemannian geometry. Applied Network Science, 3 (1). https:/doi.org/10.1007/s41109-018-0059-2

Hodas, N.O., Stinis, P. (2018). Doing the impossible: Why neural networks can be trained at all. Frontiers in Psychology, 9. https:/doi.org/10.3389/fpsyg.2018.01185

Barajas-Solano, D.A., Tartakovsky, A.M. (2018). Probability and cumulative density function methods for the stochastic advection-reaction equation. SIAM-ASA Journal on Uncertainty Quantification, 6 (1),180-212. https:/doi.org/10.1137/16M1109163

Lin, P., Song, Q., Shen, J., Wu, Y. (2018). Discovering graph patterns for fact checking in knowledge graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10827, 783-801. https:/doi.org/10.1007/978-3-319-91452-7_50

Kestor, G., Mutlu, B.O., Manzano, J., Subasi, O., Unsal, O., Krishnamoorthy, S. (2018). Comparative analysis of soft-error detection strategies: A case study with iterative methods. 2018 ACM International Conference on Computing Frontiers, CF 2018 - Proceedings, 173-182. https:/doi.org/10.1145/3203217.3203240

McDermott, T.E., Stephan, E.G., Gibson, T.D. (2018). Alternative Database Designs for the Distribution Common Information Model. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 2018-. https:/doi.org/10.1109/TDC.2018.8440470

Kestor, G., Peng, I.B., Gioiosa, R., Krishnamoorthy, S. (2018). Understanding scale-dependent soft-error behavior of scientific applications. Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, 482-491. https:/doi.org/10.1109/CCGRID.2018.00075

Stetcu, I., Bulgac, A., Jin, S., Roche, K.J., Schunck, N. (2018). Real time description of fission. Proceedings of the 15th International Conference on Nuclear Reaction Mechanisms, NRM 2018, 197-202. https:/doi.org/-

Wickramasinghe, U., Lumsdaine, A. (2018). RMalloc() and RpIPE(): A uGNI-based distributed remote memory allocator and access library for one-sided messaging. Proceedings of the 8th International Workshop on Runtime and Operating Systems for Supercomputers, ROSS 2018 - In conjunction with HPDC 2018. https:/doi.org/10.1145/3217189.3217191

Hong, C., Sukumaran-Rajam, A., Kim, J., Rawat, P.S., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Sadayappan, P. (2018). POSTER: Performance modeling for GPUs using abstract kernel emulation. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 397-398. https:/doi.org/10.1145/3178487.3178524

Subasi, O., Krishnamoorthy, S. (2018). On the theory of speculative checkpointing: Time and energy considerations. 2018 ACM International Conference on Computing Frontiers, CF 2018 - Proceedings, 165-172. https:/doi.org/10.1145/3203217.3203232

Firoz, J.S., Zalewski, M., Lumsdaine, A. (2018). POSTER: A scalable distance-1 vertex coloring algorithm for power-law graphs. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 391-392. https:/doi.org/10.1145/3178487.3178521

Kanewala, T.A., Zalewski, M., Lumsdaine, A. (2018). Distributed, shared-memory parallel triangle counting. Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2018. https:/doi.org/10.1145/3218176.3218229

Roy, P., Krishnamoorthy, S., Song, S.L., Liu, X. (2018). Lightweight detection of cache conflicts. CGO 2018 - Proceedings of the 2018 International Symposium on Code Generation and Optimization, 2018, 200-213. https:/doi.org/10.1145/3168819

Kowalski, K., Brabec, J., Peng, B. (2018). Regularized and Renormalized Many-Body Techniques for Describing Correlated Molecular Systems: A Coupled-Cluster Perspective. Annual Reports in Computational Chemistry, 14, 3-45. https:/doi.org/10.1016/bs.arcc.2018.06.001

Young, S.J., Makarov, Y., Diao, R., Fan, R., Huang, R., Orbrien, J., Halappanavar, M., Vallem, M., Huang, Z.H. (2018). Synthetic Power Grids from Real World Models. IEEE Power and Energy Society General Meeting, 2018-. https:/doi.org/10.1109/PESGM.2018.8585792

Escorihuela, L., Martorell, B., Rallo, R., Fernández, A. (2018). Toward computational and experimental characterisation for risk assessment of metal oxide nanoparticles. Environmental Science: Nano, 5 (10),2241-2251. https:/doi.org/10.1039/c8en00389k

Zhou, W., Li, P., Lumsdaine, A., Lin, L. (2018). Light-field flow: A subpixel-accuracy depth flow estimation with geometric occlusion model from a single light-field image. Proceedings - International Conference on Image Processing, ICIP, 2017, 1632-1636. https:/doi.org/10.1109/ICIP.2017.8296558

Bruillard, P., Ortiz-Marrero, C.M. (2018). Classification of rank 5 premodular categories. Journal of Mathematical Physics, 59 (1). https:/doi.org/10.1063/1.5020256

Hong, C., Sukumaran-Rajam, A., Kim, J., Rawat, P.S., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Sadayappan, P. (2018). GPU code optimization using abstract kernel emulation and sensitivity analysis. ACM SIGPLAN Notices, 53 (4),736-751. https:/doi.org/10.1145/3192366.3192397

Panyala, A., Subasi, O., Halappanavar, M., Kalyanaraman, A., Chavarría-Miranda, D., Krishnamoorthy, S. (2018). Approximate Computing Techniques for Iterative Graph Algorithms. Proceedings - 24th IEEE International Conference on High Performance Computing, HiPC 2017, 2017- ,23-32. https:/doi.org/10.1109/HiPC.2017.00013

Chamberlin, J., Zalewski, M., McMillan, S., Lumsdaine, A. (2018). PyGB: GraphBLAS DSL in python with dynamic compilation into efficient C++. Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018, 310-319. https:/doi.org/10.1109/IPDPSW.2018.00059

Yang, X., Li, W., Tartakovsky, A. (2018). Sliced-inverse-regression-aided rotated compressive sensing method for uncertainty quantification. SIAM-ASA Journal on Uncertainty Quantification, 6 (4),1532-1554. https:/doi.org/10.1137/17M1148955

Chakraborty, I., Vrabie, D. (2018). Fault Detection for Dynamical Systems using Differential Geometric and Concurrent Learning Approach. IFAC-PapersOnLine, 51 (24),1395-1402. https:/doi.org/10.1016/j.ifacol.2018.09.552

Ritter, M., Wood, L., Kuhr, T., Bracko, M., Elsethagen, T., Fox, K., Hall, J., Pulvermacher, C., Raju, B., Schram, M., Stephan, E. (2018). Belle II Conditions Database. Journal of Physics: Conference Series, 1085 (3). https:/doi.org/10.1088/1742-6596/1085/3/032032

Chen, Y., Etingov, P., Fitzhenry, E., Sharma, P., Nguyen, T., Makarov, Y., Rice, M., Allwardt, C., Widergren, S. (2018). Bringing probabilistic analysis capability from planning to operation. Control Engineering Practice, 71, 18-25. https:/doi.org/10.1016/j.conengprac.2017.06.006

Li, W., Lian, J., Engel, D., Wang, H. (2018). Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads. Journal of Control and Decision, 5 (2),148-168. https:/doi.org/10.1080/23307706.2017.1353931

Chen, Y., Palmer, B., Sharma, P., Yuan, Y., Mathew, B., Huang, Z. (2018). A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty. Conference Record of the 3rd IEEE International Workshop on Electronic Power Grid, eGrid 2018. https:/doi.org/10.1109/eGRID.2018.8598684

Tumeo, A., Franke, H., Palermo, G., Feo, J. (2018). Guest Editorial: Special Issue on Computing Frontiers. International Journal of Parallel Programming, 46 (2),333-335. https:/doi.org/10.1007/s10766-018-0556-z

Chakraborty, I., Chakraborty, R., Vrabie, D. (2018). Generative adversarial network based autoencoder: Application to fault detection problem for closed-loop dynamical systems ∗. CEUR Workshop Proceedings, 2289. https:/doi.org/-

Tartakovsky, A.M. (2018). Effective Stochastic Model For Reactive Transport. Reactive Transport Modeling: Applications in Subsurface Energy and Environmental Problems ,511-531. https:/doi.org/10.1002/9781119060031.ch11

Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanaraman, A., Gebremedhin, A.H. (2018). Scalable Distributed Memory Community Detection Using Vite. 2018 IEEE High Performance Extreme Computing Conference, HPEC 2018. https:/doi.org/10.1109/HPEC.2018.8547534

Shekar, V., Fiondella, L., Chatterjee, S., Halappanavar, M. (2018). A game-theoretic method to efficiently assess the vulnerability of a dynamic transportation network. PSAM 2018 - Probabilistic Safety Assessment and Management. https:/doi.org/-

Palmer, B. (2018). Coarse-grained hydrodynamics from correlation functions. Physical Review E, 97 (2). https:/doi.org/10.1103/PhysRevE.97.022106

Schram, M., Bansal, V., Ledesma, A. (2018). The management of heterogeneous resources in Belle II. Journal of Physics: Conference Series, 1085 (3). https:/doi.org/10.1088/1742-6596/1085/3/032006

Friese, R.D., Crowder, J.A., Siegel, H.J., Carbone, J.N. (2018). Bi-objective study for the assignment of unmanned aerial vehicles to targets. 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018. https:/doi.org/-

Firoz, J.S., Zalewski, M., Lumsdaine, A. (2018). POSTER: A Scalable Distance-1 Vertex Coloring Algorithm for Power-Law Graphs. ACM SIGPLAN Notices, 53 (1),391-392. https:/doi.org/10.1145/3178487.3178521

Wickramasinghe, U., Lumsdaine, A. (2018). Enabling efficient inter-node message passing and remote memory access via a uGNI based light-weight network substrate for cray interconnects. Proceedings - 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2018, 578-588. https:/doi.org/10.1109/CCGRID.2018.00006

Kim, J., Sukumaran-Rajam, A., Hong, C., Panyala, A., Srivastava, R.K., Krishnamoorthy, S., Sadayappan, P. (2018). Optimizing tensor contractions in CCSD(T) for efficient execution on GPUs. Proceedings of the International Conference on Supercomputing, 96-106. https:/doi.org/10.1145/3205289.3205296

Vallem, M.R., Vyakaranam, B., Tipireddy, R., Holzer, J.T., Makarov, Y.V., Samaan, N.A. (2018). Stochastic correlation analysis to rank the impact of intermittent wind generation on unreliability margins of power systems. 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings. https:/doi.org/10.1109/PMAPS.2018.8440541

Tumeo, A. (2018). Architecture independent integrated early performance and energy estimation. 2017 8th International Green and Sustainable Computing Conference, IGSC 2017, 2017,1-6. https:/doi.org/10.1109/IGCC.2017.8323602

Chin, G., Fitzhenry, E., McBain, A., Beus, S., Marinovici, L., Hansen, J., Studarus, K. (2018). Visual steering and modeling environment for smart grid models and simulations. IEEE Power and Energy Society General Meeting, 2018,1-5. https:/doi.org/10.1109/PESGM.2017.8274093

Kassianov, E., Monroe, J., Riihimaki, L., Flynn, C., Cromwell, E., Hodges, G., McComiskey, A. (2018). Climatology of aerosol optical depth at mid-continental US site: Ground-based observations. Proceedings of SPIE - The International Society for Optical Engineering, 10786. https:/doi.org/10.1117/12.2326690

Bhatia, U., Chatterjee, S., Ganguly, A.R., Gao, J., Halappanavar, M., Oster, M., Clark, K., Brigantic, R., Tipireddy, R. (2018). Aviation Transportation, Cyber Threats, and Network-of-Networks: Modeling Perspectives for Translating Theory to Practice. 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018. https:/doi.org/10.1109/THS.2018.8574123

Kalyanaraman, A., Halappanavar, M. (2018). Guest editorial: Advances in parallel graph processing: Algorithms, architectures, and application frameworks. IEEE Transactions on Multi-Scale Computing Systems, 4 (3),188-189. https:/doi.org/10.1109/TMSCS.2018.2858297

Abdesselam, A., Adachi, I., Adamczyk, K., Ahn, J.K., Aihara, H., Al Said, S., Arinstein, K., Arita, Y., Asner, et al. (2018). Measurement of the D∗− polarization in the decay B0 → D∗−τ+ντ. 10th International Workshop on the CKM Unitarity Triangle, CKM 2018. https:/doi.org/-

Choudhury, S., Purohit, S., Lin, P., Wu, Y., Holder, L., Agarwal, K. (2018). Percolator: Scalable pattern discovery in dynamic graphs. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018- ,759-762. https:/doi.org/10.1145/3159652.3160589

Subasi, O., Chang, C.-K., Erez, M., Krishnamoorthy, S. (2018). Characterizing the impact of soft errors affecting floating-point ALUs using RTL-level fault injection. ACM International Conference Proceeding Series. https:/doi.org/10.1145/3225058.3225089

Liu, Y., Chase, J.M., Gorton, I., Rice, M., Wynne, A. (2018). Modelling and coordinating multi-source distributed power system models in service-oriented architecture. International Journal of High Performance Computing and Networking, 12 (2),191-206. https:/doi.org/10.1504/ijhpcn.2018.094369

Hong, C., Sukumaran-Rajam, A., Kim, J., Rawat, P.S., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Sadayappan, P. (2018). POSTER: Performance Modeling for GPUs using Abstract Kernel Emulation. ACM SIGPLAN Notices, 53 (1),397-398. https:/doi.org/10.1145/3178487.3178524

Young, S.J., Makarov, Y., Diao, R., Halappanavar, M., Vallem, M., Fan, R., Huang, R., Orbrien, J., Huang, Z.H. (2018). Topological Power Grid Statistics from a Network-of-Networks Perspective. IEEE Power and Energy Society General Meeting, 2018-. https:/doi.org/10.1109/PESGM.2018.8586475

Friese, R.D., Tallent, N.R., Schram, M., Halappanavar, M., Barker, K.J. (2018). Optimizing Distributed Data-Intensive Workflows. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2018- ,279-289. https:/doi.org/10.1109/CLUSTER.2018.00045

2017

Li, Y., Hu, S., Sun, X., Stan, M. (2017). A review: Applications of the phase field method in predicting microstructure and property evolution of irradiated nuclear materials. npj Computational Materials, 3 (1). https:/doi.org/10.1038/s41524-017-0018-y

Li, A., Liu, W., Kristensen, M.R.B., Vinter, B., Wang, H., Hou, K., Marquez, A., Song, S.L. (2017). Exploring and analyzing the real impact of modern on-package memory on hpc scientific kernels. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. https:/doi.org/10.1145/3126908.3126931

Warneke, J., Hou, G.-L., Aprà, E., Jenne, C., Yang, Z., Qin, Z., Kowalski, K., Wang, X.-B., Xantheas, S.S. (2017). Electronic Structure and Stability of [B12X12]2- (X = F-At): A Combined Photoelectron Spectroscopic and Theoretical Study. Journal of the American Chemical Society, 139 (41),14749-14756. https:/doi.org/10.1021/jacs.7b08598

Liu, W., Li, A., Hogg, J.D., Duff, I.S., Vinter, B. (2017). Fast synchronization-free algorithms for parallel sparse triangular solves with multiple right-hand sides. Concurrency Computation, 29 (21). https:/doi.org/10.1002/cpe.4244

Peng, I.B., Gioiosa, R., Kestor, G., Cicotti, P., Laure, E., Markidis, S. (2017). Exploring the performance benefit of hybrid memory system on HPC environments. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, 683-692. https:/doi.org/10.1109/IPDPSW.2017.115

Peng, I.B., Gioiosa, R., Kestor, G., Cicotti, P., Laure, E., Markidis, S. (2017). RTHMS: A tool for data placement on hybrid memory system. International Symposium on Memory Management, ISMM, 128677, 82-91. https:/doi.org/10.1145/3092255.3092273

Li, A., Song, S.L., Liu, W., Liu, X., Kumar, A., Corporaal, H. (2017). Locality-aware CTA Clustering for modern GPUs. International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 127193, 297-311. https:/doi.org/10.1145/3037697.3037709

Solanas, A., Casino, F., Batista, E., Rallo, R. (2017). Trends and challenges in smart healthcare research: A journey from data to wisdom. RTSI 2017 - IEEE 3rd International Forum on Research and Technologies for Society and Industry, Conference Proceedings. https:/doi.org/10.1109/RTSI.2017.8065986

Choudhury, S., Agarwal, K., Purohit, S., Zhang, B., Pirrung, M., Smith, W., Thomas, M. (2017). NOUS: Construction and querying of dynamic knowledge graphs. Proceedings - International Conference on Data Engineering, 1563-1565. https:/doi.org/10.1109/ICDE.2017.228

Ling, B., Bao, J., Oostrom, M., Battiato, I., Tartakovsky, A.M. (2017). Modeling variability in porescale multiphase flow experiments. Advances in Water Resources, 105, 29-38. https:/doi.org/10.1016/j.advwatres.2017.04.005

Ranshous, S., Joslyn, C.A., Kreyling, S., Nowak, K., Samatova, N.F., West, C.L., Winters, S. (2017). Exchange pattern mining in the bitcoin transaction directed hypergraph. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10323, 248-263. https:/doi.org/10.1007/978-3-319-70278-0_16

Peng, I.B., Markidis, S., Laure, E., Kestor, G., Gioiosa, R. (2017). Exploring Application Performance on Emerging Hybrid-Memory Supercomputers. Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 473-480. https:/doi.org/10.1109/HPCC-SmartCity-DSS.2016.0074

Jin, S., Bulgac, A., Roche, K., Wlazłowski, G. (2017). Coordinate-space solver for superfluid many-fermion systems with the shifted conjugate-orthogonal conjugate-gradient method. Physical Review C, 95 (4). https:/doi.org/10.1103/PhysRevC.95.044302

Gawande, N.A., Landwehr, J.B., Daily, J.A., Tallent, N.R., Vishnu, A., Kerbyson, D.J. (2017). Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, 399-408. https:/doi.org/10.1109/IPDPSW.2017.36

Varga, T., Droubay, T.C., Kovarik, L., Nandasiri, M.I., Shutthanandan, V., Hu, D., Kim, B., Jeon, S., Hong, S., Li, Y., Chambers, S.A. (2017). Coupled Lattice Polarization and Ferromagnetism in Multiferroic NiTiO3 Thin Films. ACS Applied Materials and Interfaces, 9 (26),21879-21890. https:/doi.org/10.1021/acsami.7b04481

Shigorina, E., Kordilla, J., Tartakovsky, A.M. (2017). Smoothed particle hydrodynamics study of the roughness effect on contact angle and droplet flow. Physical Review E, 96 (3). https:/doi.org/10.1103/PhysRevE.96.033115

Lu, H., Halappanavar, M., Chavarria-Miranda, D., Gebremedhin, A.H., Panyala, A., Kalyanaraman, A. (2017). Algorithms for balanced graph colorings with applications in parallel computing. IEEE Transactions on Parallel and Distributed Systems, 28 (5),1240-1256. https:/doi.org/10.1109/TPDS.2016.2620142

Bruillard, P., Galindo, C., Hagge, T., Ng, S.-H., Plavnik, J.Y., Rowell, E.C., Wang, Z. (2017). Fermionic modular categories and the 16-fold way. Journal of Mathematical Physics, 58 (4). https:/doi.org/10.1063/1.4982048

Peng, B., Kowalski, K. (2017). Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations. Journal of Chemical Theory and Computation, 13 (9),4179-4192. https:/doi.org/10.1021/acs.jctc.7b00605

Cheng, G., Choi, K.S., Hu, X.H., Sun, X. (2017). Computational material design for Q&P steels with plastic instability theory. Materials and Design, 132, 526-538. https:/doi.org/10.1016/j.matdes.2017.07.029

Douberly, G.E., Miller, R.E., Xantheas, S.S. (2017). Formation of Exotic Networks of Water Clusters in Helium Droplets Facilitated by the Presence of Neon Atoms. Journal of the American Chemical Society, 139 (11),4152-4156. https:/doi.org/10.1021/jacs.7b00510

Halappanavar, M., Lu, H., Kalyanaraman, A., Tumeo, A. (2017). Scalable static and dynamic community detection using Grappolo. 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017. https:/doi.org/10.1109/HPEC.2017.8091047

Ren, B., Krishnamoorthy, S., Agrawal, K., Kulkarni, M. (2017). Exploiting vector and multicore parallelism for recursive, data- and task-parallel programs. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 117-130. https:/doi.org/10.1145/3018743.3018763

Cheng, G., Hu, X.H., Choi, K.S., Sun, X. (2017). Predicting grid-size-dependent fracture strains of DP980 with a microstructure-based post-necking model. International Journal of Fracture, 207 (2),211-227. https:/doi.org/10.1007/s10704-017-0229-8

Namaki, M.H., Wu, Y., Song, Q., Lin, P., Ge, T. (2017). Discovering graph temporal association rules. International Conference on Information and Knowledge Management, Proceedings, 131841, 1697-1706. https:/doi.org/10.1145/3132847.3133014

Pan, W., Kim, K., Perego, M., Tartakovsky, A.M., Parks, M.L. (2017). Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics. Journal of Computational Physics, 334, 125-144. https:/doi.org/10.1016/j.jcp.2016.12.042

Bañares, M.A., Haase, A., Tran, L., Lobaskin, V., Oberdörster, G., Rallo, R., Leszczynski, J., Hoet, P., Korenstein, R., Hardy, B., Puzyn, T. (2017). CompNanoTox2015: novel perspectives from a European conference on computational nanotoxicology on predictive nanotoxicology. Nanotoxicology, 11 (7),839-845. https:/doi.org/10.1080/17435390.2017.1371351

Tartakovsky, A.M., Panzeri, M., Tartakovsky, G.D., Guadagnini, A. (2017). Uncertainty Quantification in Scale-Dependent Models of Flow in Porous Media. Water Resources Research, 53 (11),9392-9401. https:/doi.org/10.1002/2017WR020905

Namaki, M.H., Lin, P., Wu, Y. (2017). Event pattern discovery by keywords in graph streams. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018, 982-987. https:/doi.org/10.1109/BigData.2017.8258019

Kordilla, J., Noffz, T., Dentz, M., Geyer, T., Tartakovsky, A.M. (2017). Effect of Unsaturated Flow Modes on Partitioning Dynamics of Gravity-Driven Flow at a Simple Fracture Intersection: Laboratory Study and Three-Dimensional Smoothed Particle Hydrodynamics Simulations. Water Resources Research, 53 (11),9496-9518. https:/doi.org/10.1002/2016WR020236

Li, A., Song, S.L., Liu, W., Liu, X., Kumar, A., Corporaal, H. (2017). Locality-aware CTA clustering for modern GPUs. ACM SIGPLAN Notices, 52 (4),297-311. https:/doi.org/10.1145/3037697.3037709

Hawley, A.K., Torres-Beltrán, M., Zaikova, E., Walsh, D.A., Mueller, A., Scofield, M., Kheirandish, S., Payne, C., Pakhomova, L., Bhatia, M., Shevchuk, O., Gies, E.A., Fairley, D., Malfatti, S.A., Norbeck, A.D., Brewer, H.M., Pasa-Tolic, L., Del Rio, T.G., Suttle, C.A., Tringe, S., Hallam, S.J. (2017). A compendium of multi-omic sequence information from the Saanich Inlet water column. Scientific Data, 4. https:/doi.org/10.1038/sdata.2017.160

Willow, S.Y., Xantheas, S.S. (2017). Molecular-Level Insight of the Effect of Hofmeister Anions on the Interfacial Surface Tension of a Model Protein. Journal of Physical Chemistry Letters, 8 (7),1574-1577. https:/doi.org/10.1021/acs.jpclett.7b00069

Peng, B., Govind, N., Aprà, E., Klemm, M., Hammond, J.R., Kowalski, K. (2017). Coupled Cluster Studies of Ionization Potentials and Electron Affinities of Single-Walled Carbon Nanotubes. Journal of Physical Chemistry A, 121 (6),1328-1335. https:/doi.org/10.1021/acs.jpca.6b10874

Perarnau, S., Zounmevo, J.A., Dreher, M., Essen, B.C.V., Gioiosa, R., Iskra, K., Gokhale, M.B., Yoshii, K., Beckman, P. (2017). Argo NodeOS: Toward Unified Resource Management for Exascale. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017, 153-162. https:/doi.org/10.1109/IPDPS.2017.25

Subasi, O., Kestor, G., Krishnamoorthy, S. (2017). Toward a General Theory of Optimal Checkpoint Placement. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2017- ,464-474. https:/doi.org/10.1109/CLUSTER.2017.127

Cheng, G., Choi, K.S., Hu, X., Sun, X. (2017). Predicting Deformation Limits of Dual-Phase Steels Under Complex Loading Paths. JOM, 69 (6),1046-1051. https:/doi.org/10.1007/s11837-017-2333-7

Peng, I.B., Markidis, S., Gioiosa, R., Kestor, G., Laure, E. (2017). MPI Streams for HPC Applications. Advances in Parallel Computing, 30, 75-92. https:/doi.org/10.3233/978-1-61499-816-7-75

Subasi, O., Di, S., Balaprakash, P., Unsal, O., Labarta, J., Cristal, A., Krishnamoorthy, S., Cappello, F. (2017). MACORD: Online adaptive machine learning framework for silent error detection. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2017- ,717-724. https:/doi.org/10.1109/CLUSTER.2017.128

Hamm, P., Fanourgakis, G.S., Xantheas, S.S. (2017). A surprisingly simple correlation between the classical and quantum structural networks in liquid water. Journal of Chemical Physics, 147 (6). https:/doi.org/10.1063/1.4993166

Peng, I.B., Gioiosa, R., Kestor, G., Laure, E., Markidis, S. (2017). Preparing HPC Applications for the Exascale Era: A Decoupling Strategy. Proceedings of the International Conference on Parallel Processing, 1-10. https:/doi.org/10.1109/ICPP.2017.9

Soulami, A., Burkes, D.E., Joshi, V.V., Lavender, C.A., Paxton, D. (2017). Finite-element model to predict roll-separation force and defects during rolling of U-10Mo alloys. Journal of Nuclear Materials, 494, 182-191. https:/doi.org/10.1016/j.jnucmat.2017.07.006

Li, A., Zhao, W., Song, S.L. (2017). BVF: Enabling significant on-chip power savings via bit-value-favor for throughput processors. Proceedings of the Annual International Symposium on Microarchitecture, MICRO, 131207, 532-545. https:/doi.org/10.1145/3123939.3123944

Ashraf, R.A., Gioiosa, R., Kestor, G., DeMara, R.F. (2017). Exploring the effect of compiler optimizations on the reliability of hpc applications. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, 1274-1283. https:/doi.org/10.1109/IPDPSW.2017.7

Tipireddy, R., Stinis, P., Tartakovsky, A.M. (2017). Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. Journal of Computational Physics, 351, 203-215. https:/doi.org/10.1016/j.jcp.2017.08.067

Savic, V., Hector, L., Basu, U., Basudhar, A., Gandikota, I., Stander, N., Park, T., Pourboghrat, F., Choi, K.S., Sun, X., Hu, J., Abu-Farha, F., Kumar, S. (2017). Integrated Computational Materials Engineering (ICME) Multi-Scale Model Development for Advanced High Strength Steels. SAE Technical Papers, 2017- (March). https:/doi.org/10.4271/2017-01-0226

Xu, K., Schreiber, D.K., Li, Y., Johnson, B.R., McCloy, J. (2017). Effect of defects, magnetocrystalline anisotropy, and shape anisotropy on magnetic structure of iron thin films by magnetic force microscopy. AIP Advances, 7 (5). https:/doi.org/10.1063/1.4976580

Yoo, S., Xantheas, S.X. (2017). Structures, energetics, and spectroscopic fingerprints of water clusters n = 2−24. Handbook of Computational Chemistry, 1139-1173. https:/doi.org/10.1007/978-3-319-27282-5_21

Rivas-Gomez, S., Gioiosa, R., Peng, I.B., Kestor, G., Narasimhamurthy, S., Laure, E., Markidis, S. (2017). MPI windows on storage for HPC applications. ACM International Conference Proceeding Series. https:/doi.org/10.1145/3127024.3127034

Panyala, A., Chavarría-Miranda, D., Manzano, J.B., Tumeo, A., Halappanavar, M. (2017). Exploring performance and energy tradeoffs for irregular applications: A case study on the Tilera many-core architecture. Journal of Parallel and Distributed Computing, 104, 234-251. https:/doi.org/10.1016/j.jpdc.2016.06.006

Peng, B., Kowalski, K. (2017). Low-rank factorization of electron integral tensors and its application in electronic structure theory. Chemical Physics Letters, 672, 47-53. https:/doi.org/10.1016/j.cplett.2017.01.056

Tipireddy, R., Kumar, S. (2017). Spatially-degraded adhesive anchors under material uncertainty. International Journal of Adhesion and Adhesives, 76, 61-69. https:/doi.org/10.1016/j.ijadhadh.2017.02.010

Rubio-Herrero, J., Chandan, V., Siegel, C., Vishnu, A., Vrabie, D. (2017). A learning framework for control-oriented modeling of buildings. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2017, 473-478. https:/doi.org/10.1109/ICMLA.2017.00079

Rivas-Gomez, S., Markidis, S., Peng, I.B., Laure, E., Kestor, G., Gioiosa, R. (2017). Extending message passing interface windows to storage. Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, 727-730. https:/doi.org/10.1109/CCGRID.2017.44

Tallent, N.R., Kerbyson, D.J., Hoisie, A. (2017). Representative paths analysis. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. https:/doi.org/10.1145/3126908.3126962

Shekar, V., Fiondella, L., Chatterjee, S., Halappanavar, M. (2017). Quantitative assessment of transportation network vulnerability with dynamic traffic simulation methods. 2017 IEEE International Symposium on Technologies for Homeland Security, HST 2017. https:/doi.org/10.1109/THS.2017.7943454

Lifflander, J., Krishnamoorthy, S. (2017). Cache locality optimization for recursive programs. Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 128414, 1-16. https:/doi.org/10.1145/3062341.3062385

Castellana, V.G., Minutoli, M., Bhatt, S., Agarwal, K., Bleeker, A., Feo, J., Chavarria-Miranda, D., Haglin, D. (2017). High-Performance Data Analytics beyond the Relational and Graph Data Models with GEMS. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, 1029-1038. https:/doi.org/10.1109/IPDPSW.2017.70

Sharma, V.C., Gopalakrishnan, G., Krishnamoorthy, S. (2017). PRESAGE: Protecting Structured Address Generation against Soft Errors. Proceedings - 23rd IEEE International Conference on High Performance Computing, HiPC 2016, 252-261. https:/doi.org/10.1109/HiPC.2016.037

Bulgac, A., Jin, S., Magierski, P., Roche, K., Schunck, N., Stetcu, I. (2017). Nuclear Fission: From more phenomenology and adjusted parameters to more fundamental theory and increased predictive power. EPJ Web of Conferences, 163. https:/doi.org/10.1051/epjconf/201716300007

Wang, X., Xu, Z., Soulami, A., Hu, X., Lavender, C., Joshi, V. (2017). Modeling Early-Stage Processes of U-10 Wt.%Mo Alloy Using Integrated Computational Materials Engineering Concepts. JOM, 69 (12),2532-2537. https:/doi.org/10.1007/s11837-017-2608-z

Stephan, E., Raju, B., Elsethagen, T., Pouchard, L., Gamboa, C. (2017). A scientific data provenance harvester for distributed applications. 2017 New York Scientific Data Summit, NYSDS 2017 - Proceedings. https:/doi.org/10.1109/NYSDS.2017.8085041

Subasi, O., Krishnamoorthy, S. (2017). A Gaussian Process Approach for Effective Soft Error Detection. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2017- ,608-612. https:/doi.org/10.1109/CLUSTER.2017.129

Tipireddy, R., Lerchen, M., Ramuhalli, P. (2017). Virtual sensors for robust on-line monitoring (OLM) and diagnostics. 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017, 2, 719-728. https:/doi.org/-

Peng, I.B., Markidis, S., Laure, E., Kestor, G., Gioiosa, R. (2017). Idle period propagation in message-passing applications. Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 937-944. https:/doi.org/10.1109/HPCC-SmartCity-DSS.2016.0134

Amatya, V., Vishnu, A., Siegel, C., Daily, J. (2017). What does fault tolerant deep learning need from MPI?. ACM International Conference Proceeding Series. https:/doi.org/10.1145/3127024.3127037

Subasi, O., Unsal, O., Krishnamoorthy, S. (2017). Automatic risk-based selective redundancy for fault-tolerant task-parallel HPC applications. Proceedings of ESPM2 2017: 3rd International Workshop on Extreme Scale Programming Models and Middleware - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis. https:/doi.org/10.1145/3152041.3152083

Ceriani, M., Secchi, S., Villa, O., Tumeo, A., Palermo, G. (2017). Exploring efficient hardware support for applications with irregular memory patterns on multinode manycore architectures. IEEE Transactions on Parallel and Distributed Systems, 28 (6),1635-1648. https:/doi.org/10.1109/TPDS.2014.2345073

Saha, S., Vullikanti, A., Halappanavar, M. (2017). FlipNet: Modeling Covert and Persistent Attacks on Networked Resources. Proceedings - International Conference on Distributed Computing Systems, 2444-2451. https:/doi.org/10.1109/ICDCS.2017.298

Landwehr, J., Suetterlein, J., Manzano, J., Marquez, A., Barker, K.J., Gao, G.R. (2017). Designing scalable distributed memory models: A case study. ACM International Conference on Computing Frontiers 2017, CF 2017 ,174-182. https:/doi.org/10.1145/3075564.3077425

Ramachandran, T., Kundu, S., Chen, Y., Vrabie, D. (2017). Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency. Proceedings of the American Control Conference, 2925-2930. https:/doi.org/10.23919/ACC.2017.7963395

Ward, T.B., Miliordos, E., Carnegie, P.D., Xantheas, S.S., Duncan, M.A. (2017). Ortho-para interconversion in cation-water complexes: The case of V+(H2O) and Nb+(H2O) clusters. Journal of Chemical Physics, 146 (22). https:/doi.org/10.1063/1.4984826

Singh, A., Stephan, E., Schram, M., Altintas, I. (2017). Deep learning on operational facility data related to large-scale distributed area scientific workflows. Proceedings - 13th IEEE International Conference on eScience, eScience 2017 ,586-591. https:/doi.org/10.1109/eScience.2017.94

Tipireddy, R., Chatterjee, S., Paulson, P., Oster, M., Halappanavar, M. (2017). Agent-centric approach for cybersecurity decision-support with partial observability. 2017 IEEE International Symposium on Technologies for Homeland Security, HST 2017. https:/doi.org/10.1109/THS.2017.7943478

Maglalang, J., Krishnamoorthy, S., Agrawal, K. (2017). Locality-Aware Dynamic Task Graph Scheduling. Proceedings of the International Conference on Parallel Processing ,70-80. https:/doi.org/10.1109/ICPP.2017.16

Ramuhalli, P., Tipireddy, R., Lerchen, M., Shumaker, B., Coble, J., Nair, A., Boring, S. (2017). Robust online monitoring for calibration assessment of transmitters and instrumentation. 10th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2017, 2, 1176-1184. https:/doi.org/-

Bylaska, E.J., Aprà, E., Kowalski, K., Jacquelin, M., De Jong, W.A., Vishnu, A., Palmer, B., Daily, J., Straatsma, T.P., Hammond, J.R., Klemm, M. (2017). Transitioning NWChem to the next generation of manycore machines. Exascale Scientific Applications: Scalability and Performance Portability, 165-186. https:/doi.org/10.1201/b21930

Kanewala, T., Zalewski, M., Lumsdaine, A. (2017). Distributed-memory fast maximal independent set. 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017. https:/doi.org/10.1109/HPEC.2017.8091032

Wood, L., Elsethagen, T., Schram, M., Stephan, E. (2017). Conditions Database for the Belle II Experiment. Journal of Physics: Conference Series, 898 (4). https:/doi.org/10.1088/1742-6596/898/4/042060

Keller, M.T., Young, S.J. (2017). Combinatorial reductions for the Stanley depth of I and S/I. Electronic Journal of Combinatorics, 24 (3). https:/doi.org/10.37236/6783

Zhou, W., Lumsdaine, A., Lin, L., Zhang, W., Wang, R. (2017). Edge-aware light-field flow for depth estimation and occlusion detection. IS and T International Symposium on Electronic Imaging Science and Technology, 94-99. https:/doi.org/10.2352/ISSN.2470-1173.2017.17.COIMG-431

Schram, M., Bansal, V., Friese, R.D., Tallent, N.R., Yin, J., Barker, K.J., Stephan, E., Halappanavar, M., Kerbyson, D.J. (2017). Integrating prediction, provenance, and optimization into high energy workflows. Journal of Physics: Conference Series, 898 (6). https:/doi.org/10.1088/1742-6596/898/6/062052

Cottam, J.A., Blaha, L., Zarzhitsky, D., Thomas, M., Skomski, E. (2017). Crossing the Streams: Fuzz testing with user input. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018, 4362-4371. https:/doi.org/10.1109/BigData.2017.8258466

Berikkyzy, Z., Cox, C., Dairyko, M., Hogenson, K., Kumbhat, M., Lidický, B., Messerschmidt, K., Moss, K., Nowak, K., Palmowski, K.F., Stolee, D. (2017). (4, 2)-Choosability of Planar Graphs with Forbidden Structures. Graphs and Combinatorics, 33 (4),751-787. https:/doi.org/10.1007/s00373-017-1812-5

Kanewala, T.A., Zalewski, M., Lumsdaine, A. (2017). Families of graph algorithms: SSSP case study. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10417, 428-441. https:/doi.org/10.1007/978-3-319-64203-1_31

Tumeo, A., Ceriani, M., Palermo, G., Minutoli, M., Castellana, V.G., Ferrandi, F. (2017). Real-time considerations for rugged embedded systems. Rugged Embedded Systems: Computing in Harsh Environments, 39-56. https:/doi.org/10.1016/B978-0-12-802459-1.00003-8

Rajbhandari, S., Rastello, F., Kowalski, K., Krishnamoorthy, S., Sadayappan, P. (2017). Optimizing the four-index integral transform using data movement lower bounds analysis. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 327-340. https:/doi.org/10.1145/3018743.3018771

Sun, X., Cheng, G., Hu, X.H., Choi, K.S. (2017). Predicting size-dependent fracture strain of DP980. ICF 2017 - 14th International Conference on Fracture, 2 ,382-383. https:/doi.org/-

Sathanur, A.V., Choudhury, S., Joslyn, C., Purohit, S. (2017). When labels fall short: Property graph simulation via blending of network structure and vertex attributes. International Conference on Information and Knowledge Management, Proceedings, 131841, 2287-2290. https:/doi.org/10.1145/3132847.3133065

Purvine, E., Cotilla-Sanchez, E., Halappanavar, M., Huang, Z., Lin, G., Lu, S., Wang, S. (2017). Comparative study of clustering techniques for real-time dynamic model reduction. Statistical Analysis and Data Mining, 10 (5),263-276. https:/doi.org/10.1002/sam.11352

Gioiosa, R. (2017). Resilience for extreme scale computing. Rugged Embedded Systems: Computing in Harsh Environments, 123-148. https:/doi.org/10.1016/B978-0-12-802459-1.00005-1

Kassianov, E., Riley, E.A., Kleiss, J.M., Long, C.N., Riihimaki, L., Flynn, D., Flynn, C., Berg, L.K. (2017). Macrophysical properties of continental cumulus clouds from active and passive remote sensing. Proceedings of SPIE - The International Society for Optical Engineering, 10424. https:/doi.org/10.1117/12.2278029

Barker, K.J., Kerbyson, D.J. (2017). Portable methodologies for energy optimization on large-scale power-constrained systems. Exascale Scientific Applications: Scalability and Performance Portability, 1-16. https:/doi.org/10.1201/b21930

Purohit, S., Choudhury, S., Holder, L.B. (2017). Application-specific graph sampling for frequent subgraph mining and community detection. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018, 1000-1005. https:/doi.org/10.1109/BigData.2017.8258022

Firoz, J.S., Kanewala, T.A., Zalewski, M., Barnas, M., Lumsdaine, A. (2017). Distributed Control: The Benefits of Eliminating Global Synchronization via Effective Scheduling in the Context of Graph Applications. ACM SIGPLAN Notices, 52 (8),441-442. https:/doi.org/10.1145/3018743.3019036

Minutoli, M., Castellana, V.G., Tumeo, A., Lattuada, M., Ferrandi, F. (2017). A dynamically scheduled architecture for the synthesis of graph methods. 2016 IEEE Hot Chips 28 Symposium, HCS 2016. https:/doi.org/10.1109/HOTCHIPS.2016.7936228

Suetterlein, J., Landwehr, J., Marquez, A., Manzano, J., Barker, K.J., Gao, G.R. (2017). Verification of the extended roofline model for asynchronous many task runtimes. Proceedings of ESPM2 2017: 3rd International Workshop on Extreme Scale Programming Models and Middleware - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis. https:/doi.org/10.1145/3152041.3152087

Getov, V., Macduff, M., Kerbyson, D.J., Hoisie, A. (2017). Application-Specific Energy Modeling of Multi-Core Processors. Advances in Parallel Computing, 30 ,35-54. https:/doi.org/10.3233/978-1-61499-816-7-35

Prasad, S.K., Banicescu, I., Barnas, M., Giménez, D., Lumsdaine, A. (2017). Keeping up with technology: Teaching Parallel, Distributed and High-Performance Computing. Journal of Parallel and Distributed Computing, 105, 1-3. https:/doi.org/10.1016/j.jpdc.2017.03.001

Gioiosa, R., Warfel, T., Tumeo, A., Friese, R. (2017). Pushing the limits of irregular access patterns on emerging network architecture: A case study. Proceedings - IEEE International Conference on Cluster Computing, ICCC, 2017- ,874-881. https:/doi.org/10.1109/CLUSTER.2017.125

Palmer, B. (2017). Application of PGAS programming to power grid simulation. Proceedings of PAW 2016: 1st PGAS Applications Workshop - Held in conjunction with SC 2016: The International Conference for High Performance Computing, Networking, Storage and Analysis, 33-40. https:/doi.org/10.1109/PAW.2016.010

Xu, Z., Tartakovsky, A.M. (2017). Method of model reduction and multifidelity models for solute transport in random layered porous media. Physical Review E, 96 (3). https:/doi.org/10.1103/PhysRevE.96.033314

Larche, M., Prowant, M., Bruillard, P., Hagge, T., Fifield, L.S., Hughes, M., Sun, X. (2017). A comparison of different NDE signal processing techniques based on waveform entropies applied to long fiber-graphite/epoxy-plates. Proceedings of SPIE - The International Society for Optical Engineering, 10169. https:/doi.org/10.1117/12.2260490

Firoz, J.S., Kancwala, T.A., Zalewskr, M., Barnas, M., Lumsdaine, A. (2017). Distributed control: The benefits of eliminating global synchronization via effective scheduling: In the context of graph applications. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 441-442. https:/doi.org/10.1145/3018743.3019036

Shekar, V., Fiondella, L., Chatterjee, S., Halappanavar, M. (2017). Quantifying economic and environmental impacts of transportation network disruptions with dynamic traffic simulation. 2017 IEEE International Symposium on Technologies for Homeland Security, HST 2017. https:/doi.org/10.1109/THS.2017.7943472

Shippert, T., Gaustad, K. (2017). An architecture for consolidating multidimensional time-series data onto a common coordinate grid. Earth Science Informatics, 10 (2),247-256. https:/doi.org/10.1007/s12145-016-0285-z

Dunning, T.H. (2017). Foreword. Exascale Scientific Applications: Scalability and Performance Portability, xi-xii. https:/doi.org/10.1201/b21930

Ren, B., Krishnamoorthy, S., Agrawal, K., Kulkarni, M. (2017). Exploiting Vector and Multicore Parallelism for Recursive, Data- and Task-Parallel Programs. ACM SIGPLAN Notices, 52 (8),117-130. https:/doi.org/10.1145/3018743.3018763

Tumeo, A., Halappanavar, M., Feo, J. (2017). GraML 17 first workshop on the intersection of graph algorithms and machine learning. Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017, 1529-1530. https:/doi.org/10.1109/IPDPSW.2017.217

Tumeo, A., Feo, J., Villa, O. (2017). Foreword. Proceedings of IA3 2016 - 6th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2016: The International Conference for High Performance Computing, Networking, Storage and Analysis, v-vi. https:/doi.org/10.1109/IA3.2016.004

Castellana, V.G., Tumeo, A., Minutoli, M., Lattuada, M., Ferrandi, F. (2017). Considerations on the use of custom accelerators for big data analytics. Big Data Management and Processing ,279-296. https:/doi.org/10.1201/9781315154008

Lifflander, J., Krishnamoorthy, S. (2017). Cache locality optimization for recursive programs. ACM SIGPLAN Notices, 52 (6),1-16. https:/doi.org/10.1145/3062341.3062385

Rajbhandari, S., Rastello, F., Kowalski, K., Krishnamoorthy, S., Sadayappan, P. (2017). Optimizing the Four-Index Integral Transform Using Data Movement Lower Bounds Analysis. ACM SIGPLAN Notices, 52 (8),327-340. https:/doi.org/10.1145/3018743.3018771

2016

Bulgac, A., Magierski, P., Roche, K.J., Stetcu, I. (2016). Induced Fission of Pu 240 within a Real-Time Microscopic Framework. Physical Review Letters, 116 (12). https:/doi.org/10.1103/PhysRevLett.116.122504

Yang, X., Mehmani, Y., Perkins, W.A., Pasquali, A., Schönherr, M., Kim, K., Perego, M., Parks, M.L., Trask, N., Balhoff, M.T., Richmond, M.C., Geier, M., Krafczyk, M., Luo, L.-S., Tartakovsky, A.M., Scheibe, T.D. (2016). Intercomparison of 3D pore-scale flow and solute transport simulation methods. Advances in Water Resources, 95, 176-189. https:/doi.org/10.1016/j.advwatres.2015.09.015

Tartakovsky, A.M., Panchenko, A. (2016). Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics. Journal of Computational Physics, 305, 1119-1146. https:/doi.org/10.1016/j.jcp.2015.08.037

Tartakovsky, A.M., Trask, N., Pan, K., Jones, B., Pan, W., Williams, J.R. (2016). Smoothed particle hydrodynamics and its applications for multiphase flow and reactive transport in porous media. Computational Geosciences, 20 (4),807-834. https:/doi.org/10.1007/s10596-015-9468-9

Cheng, G., Choi, K.S., Hu, X., Sun, X. (2016). Determining individual phase properties in a multi-phase Q&P steel using multi-scale indentation tests. Materials Science and Engineering A, 652, 384-395. https:/doi.org/10.1016/j.msea.2015.11.072

Willow, S.Y., Zeng, X.C., Xantheas, S.S., Kim, K.S., Hirata, S. (2016). Why Is MP2-Water ''Cooler'' and ''Denser'' than DFT-Water?. Journal of Physical Chemistry Letters, 7 (4),680-684. https:/doi.org/10.1021/acs.jpclett.5b02430

Miliordos, E., Xantheas, S.S. (2016). The origin of the reactivity of the criegee intermediate: Implications for atmospheric particle growth. Angewandte Chemie - International Edition, 55 (3),1015-1019. https:/doi.org/10.1002/anie.201509685

Dunning, T.H., Xu, L.T., Takeshita, T.Y., Lindquist, B.A. (2016). Insights into the Electronic Structure of Molecules from Generalized Valence Bond Theory. Journal of Physical Chemistry A, 120 (11),1763-1778. https:/doi.org/10.1021/acs.jpca.5b12335

Hu, X., Choi, K.S., Sun, X., Ren, Y., Wang, Y. (2016). Determining Individual Phase Flow Properties in a Quench and Partitioning Steel with In Situ High-Energy X-Ray Diffraction and Multiphase Elasto-Plastic Self-Consistent Method. Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science, 47 (12),5733-5749. https:/doi.org/10.1007/s11661-016-3373-2

Tao, D., Song, S.L., Krishnamoorthy, S., Wu, P., Liang, X., Zhang, E.Z., Kerbyson, D., Chen, Z. (2016). New-sum: A novel online ABFT scheme for general iterative methods. HPDC 2016 - Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, 43-55. https:/doi.org/10.1145/2907294.2907306

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Hong, C., Bao, W., Cohen, A., Krishnamoorthy, S., Pouchet, L.-N., Rastello, F., Ramanujam, J., Sadayappan, P. (2016). Effective padding of multidimensional arrays to avoid cache conflict misses. Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 13-17- ,129-144. https:/doi.org/10.1145/2908080.2908123

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Makarov, Y.V., Meng, D., Vyakaranam, B., Diao, R., Palmer, B., Huang, Z. (2016). Direct methods to estimate the most limiting voltage level and thermal violations in coordinates of power transfers on critical transmission paths. Proceedings of the Annual Hawaii International Conference on System Sciences, 2016- ,2466-2471. https:/doi.org/10.1109/HICSS.2016.308

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Chelmis, C., Choudhury, S., Pal, A., Panangadan, A., Tong, W., Xia, Y. (2016). The 5th international workshop on parallel and distributed computing for large scale machine learning and big data analytics (ParLearning 2016). Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016, 1390-1391. https:/doi.org/10.1109/IPDPSW.2016.246

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Sathanur, A.V., Halappanavar, M. (2016). Influence maximization on complex networks with intrinsic nodal activation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10047, 133-141. https:/doi.org/10.1007/978-3-319-47874-6_10

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Minutoli, M., Castellana, V.G., Tumeo, A., Ferrandi, F., Lattuada, M. (2016). A Dynamically Scheduled Architecture for the Synthesis of Graph Database Queries. Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016. https:/doi.org/10.1109/FCCM.2016.41

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