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DTSTART;TZID=America/Chicago:20221114T133000
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UID:submissions.supercomputing.org_SC22_sess454_ws_espm103@linklings.com
SUMMARY:A Selective Nesting Approach for the Sparse Multi-Threaded Cholesk
y Factorization
DESCRIPTION:Workshop\n\nA Selective Nesting Approach for the Sparse Multi-
Threaded Cholesky Factorization\n\nLe Fèvre, Usui, Casas\n\nSparse linear
algebra routines are fundamental building blocks of a large variety of sci
entific applications. Direct solvers, which are methods for solving linea
r systems via the factorization of matrices into products of triangular ma
trices, are commonly used in many contexts. The Cholesky factorization is
the fastest direct method for symmetric and definite positive matrices.\n
\nThis presentation presents selective nesting, a method to determine the
optimal task granularity for the parallel Cholesky factorization based on
the structure of sparse matrices. We propose the OPT-D algorithm, which au
tomatically and dynamically applies selective nesting. OPT-D leverages ma
trix sparsity to drive complex task-based parallel workloads in the contex
t of direct solvers. We run an extensive evaluation campaign considering
a heterogeneous set of 35 sparse matrices and a parallel machine featuring
the A64FX processor. OPT-D delivers an average performance speedup of 1.
46x with respect to the best state-of-the-art parallel method to run direc
t solvers.\n\nSession Format: Recorded\n\nTag: AI-HPC Convergence, Extreme
Scale Computing, Parallel Programming Languages and Models, Performance,
Runtime Systems\n\nRegistration Category: Workshop Reg Pass
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