Joshua Suetterlein
Title
Cited by
Cited by
Year
Using a" codelet" program execution model for exascale machines: position paper
S Zuckerman, J Suetterlein, R Knauerhase, GR Gao
Proceedings of the 1st International Workshop on Adaptive Self-Tuning …, 2011
1482011
Using a" codelet" program execution model for exascale machines: position paper
S Zuckerman, J Suetterlein, R Knauerhase, GR Gao
Proceedings of the 1st International Workshop on Adaptive Self-Tuning …, 2011
1482011
Toward an execution model for extreme-scale systems-runnemede and beyond
GR Gao, J Suetterlein, S Zuckerman
Technical Memo, 2011
322011
Application characterization at scale: Lessons learned from developing a distributed open community runtime system for high performance computing
J Landwehr, J Suetterlein, A Márquez, J Manzano, GR Gao
Proceedings of the ACM International Conference on Computing Frontiers, 164-171, 2016
112016
Position paper: Using a codelet program execution model for exascale machines
S Zuckerman, J Suetterlein, R Knauerhase, GR Gao
EXADAPT Workshop 10 (2000417.2000424), 2011
112011
DARTS: a runtime based on the Codelet execution model
J Suetterlein
University of Delaware, 2014
102014
Automatic locality exploitation in the codelet model
C Chen, Y Wu, J Suetterlein, L Zheng, M Guo, GR Gao
2013 12th IEEE International Conference on Trust, Security and Privacy in …, 2013
82013
Asynchronous runtimes in action: An introspective framework for a next gen runtime
J Suetterlein, J Landwehr, A Márquez, JB Manzano, GR Gao
2016 IEEE International Parallel and Distributed Processing Symposium …, 2016
72016
Extending the roofline model for asynchronous many-task runtimes
JD Suetterlein, J Landwehr, A Marquez, J Manzano, GR Gao
2016 IEEE International Conference on Cluster Computing (CLUSTER), 493-496, 2016
62016
Mapa: Multi-accelerator pattern allocation policy for multi-tenant gpu servers
K Ranganath, JD Suetterlein, JB Manzano, SL Song, D Wong
arXiv preprint arXiv:2110.03214, 2021
42021
Designing scalable distributed memory models: A case study
J Landwehr, J Suetterlein, J Manzano, A Marquez, KJ Barker, GR Gao
Proceedings of the Computing Frontiers Conference, 174-182, 2017
42017
A parallel graph environment for real-world data analytics workflows
VG Castellana, M Drocco, J Feo, J Firoz, T Kanewala, A Lumsdaine, ...
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2019
32019
A case for asynchronous many task runtimes: a modeling approach for high performance computing and Big Data analytics
J Suetterlein
University of Delaware, 2017
22017
TAZeR: Hiding the cost of remote I/O in distributed scientific workflows
J Suetterlein, RD Friese, NR Tallent, M Schram
2019 IEEE International Conference on Big Data (Big Data), 383-394, 2019
12019
Toward a unified hpc and big data runtime
J Suetterlein, J Landwehr, JF Manzano, A Marquez
STREAM Workshop, 2015
12015
Towards An Energy-Efficient Scheduler in the Codelet Model
C Chen, Y Wu, J Suetterlein, L Zheng, GR Gao
IEEE Symposium on Low-Power and High-Speed Chips (IEEE COOL Chips XVI …, 2013
12013
On the Marriage of Asynchronous Many Task Runtimes and Big Data: A Glance
J Suetterlein, J Manzano, A Marquez, GR Gao
2020 IEEE 27th International Conference on High Performance Computing, Data …, 2020
2020
Effectively Using Remote I/O For Work Composition in Distributed Workflows
RD Friese, BO Mutlu, NR Tallent, J Suetterlein, J Strube
2020 IEEE International Conference on Big Data (Big Data), 426-433, 2020
2020
Adaptive Runtime Features For Distributed Graph Algorithms
JS Firoz, M Zalewski, J Suetterlein, A Lumsdaine
2018 IEEE 25th International Conference on High Performance Computing (HiPC …, 2018
2018
Verification of the Extended Roofline Model for Asynchronous Many Task Runtimes
J Suetterlein, J Landwehr, A Marquez, J Manzano, KJ Barker, GR Gao
Proceedings of the Third International Workshop on Extreme Scale Programming …, 2017
2017
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