SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations

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SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations. / Орачев, Егор Станиславович; Карпенко, Мария; Алимов, Павел Геннадьевич ; Григорьев, Семен Вячеславович.

In: The Journal of Open Source Software, Vol. 7, No. 76, 76, 20.08.2022, p. 1.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{5934c47c79bf4f17b388f886c1d74c9f,

title = "SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations",

abstract = "SPbLA is a sparse Boolean linear algebra primitives and operations for GPGPU computations. It comes as a stand-alone self-sufficient library with C API for high-performance computing with multiple backends for Nvidia Cuda, OpenCL and CPU-only platforms. The library has PyPI pyspbla package for work within a Python runtime. The primary library primitive is a sparse matrix of Boolean values. The library provides the most popular operations for matrix manipulation, such as construction from values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix element-wise addition, multiplication and Kronecker product.",

keywords = "c, c++, python, sparse matrix, linear algebra, graph analysis, graph algorithms, nvidia cuda, OpenCL, C, C++, python, sparse matrix, graph analysis, graph algorithms, linear algebra, nvidia cuda, OpenCL",

author = "Орачев, {Егор Станиславович} and Мария Карпенко and Алимов, {Павел Геннадьевич} and Григорьев, {Семен Вячеславович}",

year = "2022",

month = aug,

day = "20",

doi = "10.21105/joss.03743",

language = "English",

volume = "7",

pages = "1",

journal = "The Journal of Open Source Software",

issn = "2475-9066",

number = "76",

}

RIS

TY - JOUR

T1 - SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations

AU - Орачев, Егор Станиславович

AU - Карпенко, Мария

AU - Алимов, Павел Геннадьевич

AU - Григорьев, Семен Вячеславович

PY - 2022/8/20

Y1 - 2022/8/20

N2 - SPbLA is a sparse Boolean linear algebra primitives and operations for GPGPU computations. It comes as a stand-alone self-sufficient library with C API for high-performance computing with multiple backends for Nvidia Cuda, OpenCL and CPU-only platforms. The library has PyPI pyspbla package for work within a Python runtime. The primary library primitive is a sparse matrix of Boolean values. The library provides the most popular operations for matrix manipulation, such as construction from values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix element-wise addition, multiplication and Kronecker product.

AB - SPbLA is a sparse Boolean linear algebra primitives and operations for GPGPU computations. It comes as a stand-alone self-sufficient library with C API for high-performance computing with multiple backends for Nvidia Cuda, OpenCL and CPU-only platforms. The library has PyPI pyspbla package for work within a Python runtime. The primary library primitive is a sparse matrix of Boolean values. The library provides the most popular operations for matrix manipulation, such as construction from values, transpose, sub-matrix extraction, matrix-to-vector reduce, matrix-matrix element-wise addition, multiplication and Kronecker product.

KW - c

KW - c++

KW - python

KW - sparse matrix

KW - linear algebra

KW - graph analysis

KW - graph algorithms

KW - nvidia cuda

KW - OpenCL

KW - C

KW - C++

KW - python

KW - sparse matrix

KW - graph analysis

KW - graph algorithms

KW - linear algebra

KW - nvidia cuda

KW - OpenCL

UR - https://www.mendeley.com/catalogue/ec743c73-5611-3a4a-b501-fc66c3d69e04/

U2 - 10.21105/joss.03743

DO - 10.21105/joss.03743

M3 - Article

VL - 7

SP - 1

JO - The Journal of Open Source Software

JF - The Journal of Open Source Software

SN - 2475-9066

IS - 76

M1 - 76

ER -

ID: 97999105