Asynchronous Iterative Solvers for Extreme-Scale Computing

Technical Report · Thu Feb 04 04:00:00 EST 2021

DOI:https://doi.org/10.2172/1764239· OSTI ID:1764239

Dongarra, Jack ^[1]; Tomov, Stanimire ^[2]; Anzt, Hartwig ^[2]

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1. Univ. of Tennessee, Knoxville, TN (United States); University of Tennessee
2. Univ. of Tennessee, Knoxville, TN (United States)

The Asynchronous Iterative Solvers for Extreme-Scale Computing (AsyncIS) project aims to explore more efficient numerical algorithms by decreasing their overhead. AsyncIS does this by replacing the outer Krylov subspace solver with an asynchronous optimized Schwarz method, thereby removing the global synchronization and bulk synchronous operations typically used in numerical codes. AsyncIS—a U.S. Department of Energy (DOE)-funded collaboration between Georgia Tech, the University of Tennessee, Knoxville, Temple University, and Sandia National Laboratories—also focuses on the development and optimization of asynchronous preconditioners (i.e., preconditioners that are generated and/or applied in an asynchronous fashion). The novel preconditioning algorithms that provide fine-grained parallelism enable preconditioned Krylov solvers to run efficiently on large-scale distributed systems and manycore accelerators like GPUs.

• Research Organization: Univ. of Tennessee, Knoxville, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• DOE Contract Number: SC0016513
• OSTI ID: 1764239
• Report Number(s): DOE-UTK-DE-SC0016513-1
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
GPU
heterogeneous computing
high performance computing
krylov solvers
numerical linear algebra
optimized Schwarz method