Result: An efficient multi-algorithms sparse linear solver for GPUs

Title:
An efficient multi-algorithms sparse linear solver for GPUs
Authors:
Jost, Thomas, Contassot-Vivier, Sylvain, Vialle, Stéphane
Contributors:
Algorithms for the Grid (ALGORILLE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Frédéric Desprez
Source:
ParCo2009, Frédéric Desprez, Sep 2009, Lyon, France
Publisher Information:
CCSD, 2009.
Publication Year:
2009
Collection:
collection:SUPELEC
collection:CNRS
collection:INRIA
collection:INPL
collection:SUP_IMS
collection:INRIA-LORRAINE
collection:LORIA2
collection:INRIA-NANCY-GRAND-EST
collection:TESTALAIN1
collection:UNIV-LORRAINE
collection:INRIA2
collection:LORIA
collection:HALATHON-CS
collection:AM2I-UL
Subject Terms:
Parallelism, GPU Computing, Sparse Linear Solver, ACM: G.: Mathematics of Computing, G.4: MATHEMATICAL SOFTWARE, G.4.4: Parallel and vector implementations, G.4.0: Algorithm design and analysis, [INFO.INFO-DC]Computer Science [cs], Distributed, Parallel, and Cluster Computing [cs.DC]
Subject Geographic:
Lyon, France
Original Identifier:
HAL:
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
Availability:
https://inria.hal.science/inria-00430520
Accession Number:
edshal.inria.00430520v1
Database:
HAL

Further Information

We present a new sparse linear solver for GPUs. It is designed to work with structured sparse matrices where all the non-zeros are on a few diagonals. Several iterative algorithms are implemented, both on CPU and GPU. The GPU code is designed to be fast yet simple to read and understand. It aims to be as accurate as possible, even on chips that do not support double-precision floating-point arithmetic. Several benchmarks show that GPU algorithms are much faster than their CPU counterpart while their accuracy is satisfying.