Computer Scientist
Future Computing Technologies Group
Computer Scientist
Future Computing Technologies Group

Biography

Dr. Joshua Suetterlein received his PhD in Computer and Electrical Engineering from the University of Delaware in 2019. He has been a research scientist at Pacific Northwest National Laboratory (PNNL) in the Future Computing Technologies group (formerly known as the High-Performance Computing (HPC) group) since 2018.

Dr. Suetterlein's research focuses on high-performance system software, novel computing architectures, performance modeling, AI/ML, and reinforcement learning. His expertise extends to asynchronous fine-grain runtime systems, dataflow inspired programming models, distributed reinforcement learning, Fabric Attached Memory (FAM), and roofline models.

Currently, Dr. Suetterlein serves as the Site Principal Investigator of a project exploring the Performance and Scalability of Distributed Deep Learning in conjunction with the University of Texas El Paso. Additionally, he is the thrust lead in the Advanced Memory to Support Artificial Intelligence for Science (AMAIS) project, where he collaborates with industrial partners to scale out system software for novel disaggregated memory architectures.

Research Interest

  • High-performance system software
  • Novel computing architectures
  • Performance modeling
  • AI/ML
  • Reinforcement learning
  • Asynchronous fine-grain runtime systems
  • Dataflow inspired programming models
  • Distributed reinforcement learning
  • Fabric Attached Memory (FAM)
  • Roofline models

Education

  • PhD in electrical and computer engineering, University of Delaware
  • MS in electrical and computer engineering, University of Delaware
  • BS in computer engineering, University of Delaware

Publications

2024

  • J. Suetterlein et al., "Automatic Extraction of Network Configurations for Realistic Simulation and Validation," 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Indianapolis, IN, USA, 2024, pp. 310-312, doi: 10.1109/ISPASS61541.2024.00041.
  • M. N. Newaz, S. Ghosh, J. Suetterlein, N. R. Tallent, M. Atiqul Mollah and H. Ming, "Graph Analytics on Jellyfish topology," 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS), San Francisco, CA, USA, 2024, pp. 839-851, doi: 10.1109/IPDPS57955.2024.00079.
  • Joshua Suetterlein, Joseph Manzano, and Andres Marquez. 2024. Synchronization for CXL Based Memory. In Proceedings of the International Symposium on Memory Systems (MEMSYS '24). Association for Computing Machinery, New York, NY, USA, 178–185. https://doi.org/10.1145/3695794.3695810

2023

  • Himanshu Sharma, Joshua D Suetterlein, Sumathi Lakshmiranganatha, Thomas Flynn, Draguna Vrabie, Christine Sweeney, and Vinay Ramakrishniah. 2023. EXARL-PARS: Parallel Augmented Random Search Using Reinforcement Learning at Scale for Applications in Power Systems. In Companion Proceedings of the 14th ACM International Conference on Future Energy Systems (e-Energy '23 Companion). Association for Computing Machinery, New York, NY, USA, 1. https://doi.org/10.1145/3599733.3600261
  • S. J. Young, J. Suetterlein, J. Firoz, J. Manzano and K. Barker, "Finding Your Niche: An Evolutionary Approach to HPC Topologies," 2023 IEEE High Performance Extreme Computing Conference (HPEC), Boston, MA, USA, 2023, pp. 1-9, doi: 10.1109/HPEC58863.2023.10363484.

2022

  • Ranganath K., J.S. Firoz, J.D. Suetterlein, J.B. Manzano Franco, A. Marquez, M.V. Raugas, and D. Wong. 2022. "LC-MEMENTO: A Memory Model for Accelerated Architectures." In The 34th International Workshop on Languages and Compilers for Parallel Computing (LCPC 2021), October 13-14, 2021. Lecture Notes in Computer Science, edited by X. Li and S. Chandrasekaran, 13181, 67-82. PNNL-SA-166245. doi:10.1007/978-3-030-99372-6_5
  • Suetterlein J.D., J.B. Manzano Franco, A. Marquez, and G.R. Gao. 2022. "Extending an Asynchronous Runtime System for High Throughput Applications: A Case Study." Journal of Parallel and Distributed Computing 163. PNNL-SA-170009. doi:10.1016/j.jpdc.2022.01.027

2021

  • Ranganath K., J.D. Suetterlein, J.B. Manzano Franco, S. Song, and D. Wong. 2021. "MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers." In Proceedings of the International Conference for High Performance Computing Networking, Storage and Analysis (SC 201), November 14-19, 2021, Virtual, Online, Art. No. 99. New York, New York: Association for Computing Machinery. PNNL-SA-165192. doi:10.1145/3458817.3480853

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
  • Suetterlein J.D., J.B. Manzano Franco, A. Marquez, and G.R. Gao. 2020. "On the Marriage of Asynchronous Many Task Runtimes and Big Data: A Glance." In Proceedings of the 27th International Conference on High Performance Computing, Data, and Analytics (HiPC 2020), December 16-19, 2020, Pune, India, 233-242. Piscataway, New Jersey: IEEE. PNNL-SA-157240. doi:10.1109/HiPC50609.2020.00037

2019

  • Castellana V.G., M. Drocco, J.T. Feo, J. Firoz, T.A. Kanewala, A. Lumsdaine, and J.B. Manzano Franco, et al. 2019. "A Parallel Graph Environment for Real-World Data Analytics Workflows." In Design, Automation & Test in Europe Conference & Exhibition (DATE 2019), March 25-29, 2019, Florence, Italy, 1313-1318. Piscataway, New Jersey: IEEE. PNNL-SA-140268. doi:10.23919/DATE.2019.8715196
  • 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

  • Firoz J.S., M.J. Zalewski, J.D. Suetterlein, and A. Lumsdaine. 2018. "Adaptive Runtime Features For Distributed Graph Algorithms." In IEEE 25th International Conference on High Performance Computing (HiPC 2018), December 17-20. 2018, Bengaluru, India, 82-91. Los Alamitos, California:IEEE Computer Society. PNNL-SA-138864. doi:10.1109/HiPC.2018.00018

2017

  • Landwehr J.B., J.D. Suetterlein, J.B. Manzano Franco, A. Marquez, K.J. Barker, and G.R. Gao. 2017. "Designing Scalable Distributed Memory Models: A Case Study." In Proceedings of the Computing Frontiers Conference (CF 2017), May 15-17, 2017, Siena, Italy, 174-182. New York, New York: ACM. PNNL-SA-124960. doi:10.1145/3075564.3077425

2016

  • Landwehr J.B., J.D. Suetterlein, A. Marquez, J.B. Manzano Franco, and G.R. Gao. 2016. "Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing." In Proceedings of the ACM International Conference on Computing Frontiers (CF 2016), May 16-28, 2016, Como, Italy. New York, New York: ACM. PNNL-SA-116663. doi:10.1145/2903150.2903166
  • Suetterlein J.D., J.B. Landwehr, A. Marquez, J.B. Manzano Franco, and G.R. Gao. 2016. "Asynchronous Runtimes in Action: An Introspective Framework for a Next Gen Runtime." In IEEE International Parallel and Distributed Processing Symposium Workshops, May 23-27, 2016 Chicago, Illinois, 1744-1751. Piscataway, New Jersey: IEEE. PNNL-SA-116477. doi:10.1109/IPDPSW.2016.191
  • Suetterlein J.D., J.B. Landwehr, A. Marquez, J.B. Manzano Franco, and G.R. Gao. 2016. "Extending the Roofline Model for Asynchronous Many-Task Runtimes." In IEEE International Conference on Cluster Computing (CLUSTER 2016), September 12-16, 2016, Taipei, Taiwan, 493-496. Los Alamitos, California:IEEE Computer Society. PNNL-SA-119731. doi:10.1109/CLUSTER.2016.47