Computer Scientist
High-Performance Computing Group
Computer Scientist
High-Performance Computing Group

Biography

Joshua Suetterlein is a member of the High-Performance Computing group at Pacific Northwest National Laboratory. His main area of focus is low-level runtime systems (asynchronous fine-grain execution models). Suetterlein’s research interests include the exploration of novel execution models (program/memory models) to improve performance. In addition to software development, he has experience in performance modeling, big data, and grid computing.

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

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