Invention Grant
- Patent Title: Symmetric block sparse matrix-vector multiplication
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Application No.: US15795091Application Date: 2017-10-26
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Publication No.: US10346507B2Publication Date: 2019-07-09
- Inventor: Steve Rennich
- Applicant: Nvidia Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Nvidia Corporation
- Current Assignee: Nvidia Corporation
- Current Assignee Address: US CA Santa Clara
- Main IPC: G06F17/16
- IPC: G06F17/16 ; G06F17/12 ; G06F7/78

Abstract:
Embodiments of the present invention are directed to methods and systems for performing block sparse matrix-vector multiplications with improved efficiency through the use of a specific re-ordering the matrix data such that matrix symmetry can be exploited while simultaneously avoiding atomic memory operations or the need for inefficient memory operations in general. One disclosed method includes reordering the matrix data such that, for any column of non-transpose data, and for any row of transpose data simultaneously processed within a single thread-block on a GPU, all matrix elements update independent elements of the output vector. Using the method, the amount of data required to represent the sparse matrix can be reduced by as much as 50%, thereby doubling the effective performance on the GPU, and doubling the size of the matrix that can be accelerated by the GPU.
Public/Granted literature
- US20180121388A1 SYMMETRIC BLOCK SPARSE MATRIX-VECTOR MULTIPLICATION Public/Granted day:2018-05-03
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