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Fast synchronization‐free algorithms for parallel sparse triangular solves with multiple right‐hand sides
Author(s) -
Liu Weifeng,
Li Ang,
Hogg Jonathan D.,
Duff Iain S.,
Vinter Brian
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4244
Subject(s) - speedup , computer science , parallel computing , preprocessor , synchronization (alternating current) , partition (number theory) , algorithm , exploit , set (abstract data type) , mathematics , artificial intelligence , channel (broadcasting) , computer network , computer security , combinatorics , programming language
Summary The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on today's manycore platforms, such as GPUs, is not an easy task since computing a component of the solution may depend on previously computed components, enforcing a degree of sequential processing. As a consequence, most existing work introduces a preprocessing stage to partition the components into a group of level‐sets or colour‐sets so that components within a set are independent and can be processed simultaneously during the subsequent solution stage. However, this class of methods requires a long preprocessing time as well as significant runtime synchronization overheads between the sets. To address this, we propose in this paper novel approaches for SpTRSV and SpTRSM in which the ordering between components is naturally enforced within the solution stage. In this way, the cost for preprocessing can be greatly reduced, and the synchronizations between sets are completely eliminated. To further exploit the data‐parallelism, we also develop an adaptive scheme for efficiently processing multiple right‐hand sides in SpTRSM. A comparison with a state‐of‐the‐art library supplied by the GPU vendor, using 20 sparse matrices on the latest GPU device, shows that the proposed approach obtains an average speedup of over two for SpTRSV and up to an order of magnitude speedup for SpTRSM. In addition, our method is up to two orders of magnitude faster for the preprocessing stage than existing SpTRSV and SpTRSM methods.