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DTSTAMP:20210402T160555Z
LOCATION:Track 8
DTSTART;TZID=America/New_York:20201112T124000
DTEND;TZID=America/New_York:20201112T130500
UID:submissions.supercomputing.org_SC20_sess214_ws_lasalss101@linklings.co
 m
SUMMARY:Two-Stage Asynchronous Iterative Solvers for Multi-GPU Clusters
DESCRIPTION:Workshop\n\nTwo-Stage Asynchronous Iterative Solvers for Multi
 -GPU Clusters\n\nNayak, Cojean, Anzt\n\nGiven the trend of supercomputers 
 accumulating much of their compute power in \nGPU accelerators composed 
 of thousands of cores and operating in streaming\nmode, global synchroni
 zation points become a bottleneck, severely confining \nthe performance 
 of applications. In consequence, asynchronous methods \nbreaking \nup 
 the bulk-synchronous programming model are becoming increasingly \nattra
 ctive. In this paper, we study a GPU-focused asynchronous version of the \
 n  Restricted Additive Schwarz (RAS) method that employs preconditioned Kr
 ylov \nsubspace methods as subdomain solvers. We analyze the method for 
 various \nparameters such as local solver\ntolerance and iteration cou
 nts. Leveraging the multi-GPU architecture on\nSummit, we show that thes
 e two-stage methods are more memory and time \nefficient than asynchrono
 us RAS using direct solvers. We also \ndemonstrate the superiority over 
 synchronous counterparts, and present \nresults using one-sided CUDA-awa
 re MPI on up to 36 NVIDIA V100 GPUs.\n\nTag: Algorithms, Extreme Scale Com
 puting, Performance/Productivity Measurement and Evaluation, Scalable Comp
 uting, Scientific Computing\n\nRegistration Category: Workshop Reg Pass
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