Scalable Frameworks for Reinforcement Learning for Control of Self-Assembling Materials and for Chemistry Design
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Welch, Paul Michael Jr.↗
Engineering topics
Ramakrishnaiah, Vinay Bharadwaj
Publications and source records attributed to Ramakrishnaiah, Vinay Bharadwaj.
Proxy App Suite Release (FY2020)
Version 4.0 of the ECP Proxy App Suite is practically unchanged from the previous release. The current set of proxies has proven useful for many aspects of benchmarking and co-design and we see little reason to alter the suite. Although there have been few changes to the ECP suite, the team has been hard at work in other areas. In the area of Machine Learning (ML) we have now created a separate proxy suite dedicated to this scientific applications of ML.
97 MATHEMATICS AND COMPUTING↗
Accelerate M-TIP on GPUs and deploy to Summit and NERSC-9 (against simulated data) WBS 2.2.4.05 ExaFEL, Milestone ADSE13-199
We evaluated the orientation matching step in the M-TIP SPI workflow for potential offloading to accelerators. We ported the code to GPUs, benchmarked it, optimized and down-selected the best versions. The accelerated version of the orientation matching code that was developed at LANL (LANL GPU v3) is 34-55X faster than sequential, 2.4-4.9X faster than the fastest OpenMP open source version we found (FAISS OpenMP) and 1.5-4X faster than the fastest GPU open source version we found (FAISS GPU). Summit single-node GPU versions were somewhat faster than Cori GPU. Image size plays a role; mid-range image sizes take more time. The LANL CUDA multi-node, multi-GPU implementation shows mostly linear strong scaling. I/O also plays a large role; splitting data into parts improves read time and burst buffers dramatically improve read times. This work will be integrated into the M-TIP workflow as part of the next milestone ADSE13-193.
97 MATHEMATICS AND COMPUTING↗