Computer Scientist
Computer Scientist

Biography

Ryan Friese is a computer scientist at Pacific Northwest National Laboratory. His research interests span hardware/software co-design of runtime and system software for novel architectures, high-performance computing (HPC) network simulation and modeling, the analysis and optimization of data movement in large-scale distributed workflows, and performance modeling of irregular applications. His recent work has focused on enabling memory safe programming on HPC systems by leading the development of the Lamellar Runtime, an asynchronous distributed runtime written in the Rust Programming Language.

Friese has led or been a main contributor in the following research prototypes:

  • Lamellar is a distributed runtime for HPC written in the Rust programming language, focusing on partitioned global address space abstraction and asynchronous active messages. The goal of Lamellar is to provide a memory safe runtime as an alternative to legacy C and C++ runtimes.
  • TAZeR is a remote I/O framework for transparently minimizing the access latencies of remote I/O in workflows. TAZeR's primary strategy is capturing dynamic and irregular intertask locality, both temporal and spatial, via adaptive hierarchical staging that ensures most frequently accessed data is “close.”
  • MCL, or Minos Computing Library, is a modern task-based, asynchronous programming model and runtime for executing complex scientific workflows on extremely heterogeneous systems.

Disciplines and Skills

  • Distributed computing
  • Parallel programming
  • System software
  • Modeling and simulation
  • Rust 
  • C 
  • C++

Education

  • PhD in electrical and computer engineering, Colorado State University
  • MS in electrical engineering, Colorado State University
  • BS in computer engineering, Colorado State University
  • BS in computer science, Colorado State University

Affiliations and Professional Service

  • Institute of Electrical and Electronics Engineers Computer Society 
  • Eta Kappa Nu International Electrical Engineering Honor Society

Awards and Recognitions

  • Colorado State University International Presidential Fellow, 2013 – 2014
  • Best Paper Award, the 8th Symposium on Advances in Artificial Intelligence and Applications, 2013
  • Best Paper Award, the 2nd International Conference on Advanced Communications and Computations, 2012
  • Graduate Research Fellowship, National Science Foundation 
  • Doctoral Student Scholar, Colorado State University Information Science & Technology Center, 2012 – 2015
  • Electrical and Computer Engineering Academic Excellence Award, Colorado State University, 2010
  • Merrill-Gheen Award for Most Outstanding Male Scholar Athlete, Colorado State University, 2009 – 2010
  • Academic All-American, National Collegiate Athletics Association, 2009 – 2011
  • Water Pik Excellence in Education Award, Colorado State University, 2009
  • Scholar Athlete Award, Mountain West Conference, Colorado State University, 2007 – 2011
  • Colorado Distinguished Scholars Award, Colorado State University, 2006 – 2010
  • Walter Scott Jr. Engineering Award, Colorado State University, 2006 – 2010

Publications

2024

  • Friese R.D., R. Gioiosa, J. Cottam, E. Mutlu, G. Roek, P. Thomadakis, and M. Raugas. 2024. “Lamellar: A Rust-based asynchronous tasking and PGAS runtime for high performance computing,” in the 2024 Parallel Applications Workshop, Alternatives to MPI+X (PAW-ATM), Atlanta, GA.
  • Suetterlein J.D., S.J. Young, J.S. Firoz, J.B. Manzano Franco, R.D. Friese, N.R. Tallent, and K.J. Barker, et al. 2024. "Automatic Extraction of Network Configurations for Realistic Simulation and Validation." In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2024) May 5-7, 2024, Indianapolis, IN, 310-312. Piscataway, New Jersey: IEEE. PNNL-SA-193572. doi:10.1109/ISPASS61541.2024.00041

2023

  • Kamatar A.V., R.D. Friese, and R. Gioiosa. 2023. "A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes." In IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW 2023), May 15-19, 2023, St. Petersburg, FL, 6-15. Piscataway, New Jersey: IEEE. PNNL-SA-182931. doi:10.1109/IPDPSW59300.2023.00015 
  • Machovec D., H.J. Siegel, J.A. Crowder, S. Pasricha, A.A Maciejewski, R.D. Friese. 2023. “Surveillance mission scheduling with unmanned aerial vehicles in dynamic heterogeneous environments,” Journal of Supercomputing, vol. 79, 13864–13888. doi:10.1007/s11227-023-05225-z

2020

  • Friese R.D., B. Mutlu, N.R. Tallent, J.D. Suetterlein, and J.F. Strube. 2020. "Effectively Using Remote I/O For Work Composition in Distributed Workflows." In IEEE International Conference on Big Data (Big Data 2020), December 10-13, 2020, Atlanta, GA, 426-433. Piscataway, New Jersey: IEEE. PNNL-SA-155757. doi:10.1109/BigData50022.2020.9378352
  • Kamatar A.V., R.D. Friese, and R. Gioiosa. 20020. “Locality-aware scheduling for scalable heterogeneous environments,” in 2020 IEEE/ACM International Workshop on Runtime and Operating Systems for operating systems, Atlanta, GA, pp. 50-58, doi:10.1109/ROSS51935.2020.00011
  • Kilic O.O., N.R. Tallent, and R.D. Friese. 2020. "Rapid Memory Footprint Access Diagnostics." In 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2020), August 23-25, 2020, Boston, MA, 273-284. Piscataway, New Jersey: IEEE. PNNL-SA-151215. doi:10.1109/ISPASS48437.2020.00047

2019

  • Friese R.D., J.A. Crowder, H.J. Siegel, and J.N. Carbone. 2019. “Surveillance mission planning: model, performance measure, bi-objective analysis, partial surveils,” in 2019 International conference on Artificial Intelligence, Las Vegas, NV.
  • Friese R.D., A. Tumeo, R. Gioiosa, M.V. Raugas, and T.E. Warfel. 2019. "ADVERT: An Asynchronous Runtime for Fine-Grained Network Systems." In IEEE/ACM Third Annual Workshop on Emerging Parallel and Distributed Runtime Systems and Middleware (IPDRM 2019), November 22, 2019, Denver, CO, 9-17. Piscataway, New Jersey: IEEE. PNNL-SA-139086. doi:10.1109/IPDRM49579.2019.00006
  • Kilic O.O., N.R. Tallent, and R.D. Friese. 2019. "Rapidly Measuring Loop Footprints." In IEEE International Conference on Cluster Computing (CLUSTER 2019), September 23-26, 2019, Albuquerque, NM. Piscataway, New Jersey: IEEE. PNNL-SA-146801. doi:10.1109/CLUSTER.2019.8891025
  • Schram M., N.R. Tallent, R.D. Friese, A. Singh, and I. Altintas. 2019. "Application of Deep Learning on Integrating Prediction, Provenance, and Optimization." In Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), EPJ Web of Conferences, 214, Article No. 06007. PNNL-SA-147454. doi:10.1051/epjconf/201921406007
  • Suetterlein J.D., R.D. Friese, N.R. Tallent, and M. Schram. 2019. "TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows." In IEEE International Conference on Big Data (Big Data 2019), December 9-12, 2019, Los Angeles, CA, 383-394. Piscataway, New Jersey:IEEE. PNNL-SA-148879. doi:10.1109/BigData47090.2019.9006418  

2018

  • Bhuiyan T.H., M. Halappanavar, R.D. Friese, H. Medal, L. De La Torre, A. Visweswara Sathanur, and N.R. Tallent. 2018. "Stochastic Programming Approach for Resource Selection under Demand Uncertainty." In 22nd International Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP 2018), May 25, 2018, Vancouver, BC, Lecture Notes in Computer Science, edited by D Klusacek, W Cirne, and N Desai, 11332, 107–126. Cham: Springer. PNNL-SA-130071. doi:10.1007/978-3-030-10632-4_6 
  • Friese R.D., N.R. Tallent, M. Schram, M. Halappanavar, K.J. Barker. 2018. “Optimizing distributed data-intensive workflows,” in the 2018 IEEE International Conference of Cluster Computing (CLUSTER), Belfast, UK, 279–289, doi:10.1109/CLUSTER.2018.00045

2017

  • Friese R.D., M. Halappanavar, A. Sathanur, M. Schram, D. Kerbyson, L. De la Torre. 2017. “Towards efficient resource allocation for distributed workflows under demand uncertainties,” in 2017 Workshop on Job Scheduling Strategies for Parallel Processing, 103–121. doi:10.1007/978-3-319-77398-8_6
  • Friese R.D., N.R. Tallent, A. Vishnu, D.J. Kerbyson, and A. Hoisie. 2017. "Generating Performance Models for Irregular Applications." In IEEE International Parallel and Distributed Processing Symposium (IPDPS 2017), May 29–June 2, 2017, Orlando, Florida, 317-326. Piscataway, New Jersey: IEEE. PNNL-SA-123945. doi:10.1109/IPDPS.2017.61
  • Gioiosa R., T. Warfel, A. Tumeo, R.D. Friese. 2017. “Pushing the limits of irregular access patterns on emerging network architecture: a case study,” in the 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, HI, pp. 874–881, doi:10.1109/CLUSTER.2017.125
  • Schram M., V. Bansal, R.D. Friese, N.R. Tallent, J. Yin, K.J. Barker, and E.G. Stephan, et al. 2017. "Integrating prediction, provenance, and optimization into high energy workflows." Journal of Physics: Conference Series 898, no. 6:Article No. 062052. PNNL-SA-129007. doi:10.1088/1742-6596/898/6/062052

2016

  • Friese R.D. 2016. “Efficient genetic algorithm encoding for large-scale multi-objective resource allocation,” the 2016 IEEE Large Scale Parallel Processing Workshop (LSPP’16), Chicago, IL, 1360–1369, doi:10.1109/IPDPSW.2016.36
  • D. Dauwe, E. Jonardi, R.D. Friese, S. Pasricha, A.A. Maciejewski, D.A. Bader, and D.A. Bader, et al. 2016. "HPC Node Performance and Energy Modeling with the Co-Location of Applications." Journal of Supercomputing 72, no. 12:4771-4809. PNNL-SA-118182. doi:10.1007/s11227-016-1783-y

2015

  • Dauwe D., E. Jonardi, R. Friese, S. Pasricha, A. A. Maciejewski, D. A. Bader, and H. J. Siegel. 2015. “A methodology for co-location aware application performance modeling in multicore computing,” in 17th IEEE Workshop on Advances in Parallel and Distributed Computational Models (APDCM 2015), Hyderabad, India, 434–443, doi:10.1109/IPDPSW.2015.38
  • Khemka B., R. Friese, S. Pasricha, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. M. Hilton, R. Rambharos,  M. Wright, and S. Poole. 2015. “Comparison of energy-constrained resource allocation heuristics under different task management environments,” The 2015 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2015), Las Vegas, NV.
  • Khemka B., R. Friese, S. Pasricha, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. M. Hilton, R. Rambharos, and S. Poole. 2015. “Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system,” Sustainable Computing: Informatics and Systems, Elsevier, vol. 5, pp. 14–30, doi:10.1016/j.suscom.2014.08.001
  • Tarplee K.M., R. Friese, A. A. Maciejewski, and H. J. Siegel. 2015. “Scalable linear programming based resource allocation for makespan minimization in heterogeneous computing systems,” Journal of Parallel and Distributed Computing, Elsevier, vol. 84, pp. 76–86, doi:10.1016/j.jpdc.2015.07.002
  • Tarplee K.M., R. Friese, A. A. Maciejewski, H. J. Siegel, and E. Chong. 2015. “Energy and makespan tradeoffs in heterogeneous computing systems using efficient linear programming techniques,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 6, pp. 1633–1646, doi:10.1109/TPDS.2015.2456020
  • Tarplee K.M., R. Friese, A. A. Maciejewski, and H. J. Siegel. 2015. “Efficient and scalable Pareto front generation for energy and makespan in heterogeneous computing systems,” in Recent Advances in Computational Optimization (S. Fidanova, ed.), vol. 580 of Studies in Computational Intelligence Series, pp. 161–180, doi:10.1007/978-3-319-12631-9_10

2014

  • Dauwe D., R. Friese, S. Pasricha, A. A. Maciejewski, G. A. Koenig, and H. J. Siegel. 2014. “Modeling the effects on power and performance from memory interference of co-located applications in multicore systems,” in The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2014), Las Vegas, NV, pp. 3–9.
  • Khemka B., R. Friese, L. D. Brice˜no, H. J. Siegel, A. A. Maciejewski, G. A. Koenig, C. Groer, G. Okonski, M. M. Hilton, R. Rambharos, and S. Poole. 2014. “Utility functions and resource management in an oversubscribed heterogeneous computing environment,” IEEE Transactions on Computers, vol. 64, no. 8, pp. 2394–2407, doi:10.1109/TC.2014.2360513
  • Khemka B., R. Friese, S. Pasricha, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. Hilton, R. Rambharos, and S. Poole. 2014. “Utility driven dynamic resource management in an oversubscribed energy-constrained heterogeneous system,” in 23rd IEEE Heterogeneity in Computing Workshop (HCW 2014), Phoenix, AZ, pp. 58–67, doi:10.1109/IPDPSW.2014.12
  • Siegel H.J., B. Khemka, R. Friese, S. Pasricha, A. A. Maciejewski, G. A. Koenig, S. Powers, M. Hilton, J. Rambharos, G. Okonski, and S. W. Poole. 2014. “Energy-aware resource management for computing systems,” in 7th International Conference on Contemporary Computing (IC3), Noida, India, pp. 7–12, doi:10.1109/IC3.2014.6897139

2013

  • Friese R., T. Brinks, C. Oliver, A. A. Maciejewski, H. J. Siegel, and S. Pasricha. 2013. “A machine-by machine analysis of a bi-objective resource allocation problem,” in The 2013 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2013), LasVegas, NV, pp. 3–9.
  • Friese R., B. Khemka, A. A. Maciejewski, H. J. Siegel, G. A. Koenig, S. Powers, M. Hilton, J. Rambharos, G. Okonski, and S. W. Poole. 2013. “An analysis framework for investigating the trade-offs between system performance and energy consumption in a heterogeneous computing environment,” in 22nd IEEE Heterogeneity in Computing Workshop (HCW 2013), Boston, MA, pp. 19–30, doi:10.1109/IPDPSW.2013.142
  • Maxwell P., A. A. Maciejewski, H. J. Siegel, J. Potter, G. Pfister, J. Smith, and R. Friese. 2013. “Robust static planning tool for military village search missions: Model and heuristics,” Journal of Defense Modeling and Simulation, SAGE, vol. 10, no. 1, pp. 31–47, doi:10.1177/1548512911430218
  • Tarplee K.M., R. Friese, A. A. Maciejewski, and H. J. Siegel. 2013. “Efficient and scalable computation of the energy and makespan Pareto front for heterogeneous computing systems,” in 6th Workshop on Computational Optimization (WCO 13), Krakow, Poland, pp. 401–408. Received “The 2013 Zdzislaw Pawlak Best Paper Award,” by the Award Committee the of 8th Symposium on Advances in Artificial Intelligence and Applications.

2012

  • Friese R., T. Brinks, C. Oliver, H. J. Siegel, and A. A. Maciejewski. 2012. “Analyzing the trade-offs between minimizing makespan and minimizing energy consumption in a heterogeneous resource allocation problem,” in The 2nd International Conference on Advanced Communications and Computation (INFOCOMP 2012), Venice, Italy, pp. 81–89. Received one of seven best paper awards given.

2011

  • Friese R., P. Maxwell, A. A. Maciejewski, and H. J. Siegel. 2011. “A graphical user interface for simulating robust military village searches,” in International Conference on Modeling, Simulation and Visualization Methods (MSV 11), Las Vegas, NV, pp. 75–81.

2010

  • Maxwell P., R. Friese, A. A. Maciejewski, H. J. Siegel, J. Potter, and J. Smith. 2010. “A demonstration of a simulation tool for planning robust military village searches,” in Huntsville Simulation Conference (HSC ’10), Huntsville, AL.