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Joshua Suetterlein
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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
1512011
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
1512011
Toward an execution model for extreme-scale systems-runnemede and beyond
GR Gao, J Suetterlein, S Zuckerman
Technical Memo, 2011
322011
Mapa: Multi-accelerator pattern allocation policy for multi-tenant gpu servers
K Ranganath, JD Suetterlein, JB Manzano, SL Song, D Wong
Proceedings of the International Conference for High Performance Computing …, 2021
162021
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
142016
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
122011
DARTS: a runtime based on the Codelet execution model
J Suetterlein
University of Delaware, 2014
112014
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
92016
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
92013
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
82016
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
72017
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
62019
CAPSL Technical Memo 104: Toward an Execution Model for Extreme-Scale Systems-Runnemede and Beyond
G Gao, J Suetterlein, S Zuckerman
April, 2011
62011
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
52019
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
42020
A case for asynchronous many task runtimes: a modeling approach for high performance computing and Big Data analytics
J Suetterlein
University of Delaware, 2017
32017
Lc-memento: A memory model for accelerated architectures
K Ranganath, J Firoz, J Suetterlein, J Manzano, A Marquez, M Raugas, ...
International Workshop on Languages and Compilers for Parallel Computing, 67-82, 2021
22021
Toward a unified hpc and big data runtime
J Suetterlein, J Landwehr, JF Manzano, A Marquez
STREAM Workshop, 2015
22015
Extending an asynchronous runtime system for high throughput applications: A case study
J Suetterlein, J Manzano, A Marquez, GR Gao
Journal of Parallel and Distributed Computing 163, 214-231, 2022
12022
Hardware Evaluation Analytical Modeling and Node Simulation: Benefits of Tighter GPU Integration
B Austin, R Bair, K Barker, A Cabrera, A Chien, N Ding, J Firoz, K Ibrahim, ...
12021
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