Scaling implicit parallelism via dynamic control replication

Author:

Bauer Michael1,Lee Wonchan1,Slaughter Elliott2,Jia Zhihao3,Di Renzo Mario4,Papadakis Manolis1,Shipman Galen5,McCormick Patrick5,Garland Michael1,Aiken Alex6

Affiliation:

1. NVIDIA

2. SLAC National Accelerator Laboratory

3. Carnegie Mellon University

4. Sapienza University of Rome

5. Los Alamos National Laboratory

6. Stanford University

Publisher

ACM

Reference52 articles.

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2. 2013. Safe Object Finalization in Python. https://www.python.org/dev/peps/pep-0442/. 2013. Safe Object Finalization in Python. https://www.python.org/dev/peps/pep-0442/.

3. 2019. CANDLE: Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer. https://candle.cels.anl.gov/. 2019. CANDLE: Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer. https://candle.cels.anl.gov/.

4. 2019. Uno: Predicting Tumor Dose Response across Multiple Data Sources. https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Uno. 2019. Uno: Predicting Tumor Dose Response across Multiple Data Sources. https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Uno.

5. 2020. June 2020 Top 500 Supercomputers. https://www.top500.org/lists/top500/2020/06/. 2020. June 2020 Top 500 Supercomputers. https://www.top500.org/lists/top500/2020/06/.

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