Standard

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

в: The Journal of Open Source Software, Том 7, № 76, 76, 20.08.2022, стр. 1.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Орачев, ЕС, Карпенко, М, Алимов, ПГ & Григорьев, СВ 2022, 'SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations', The Journal of Open Source Software, Том. 7, № 76, 76, стр. 1. https://doi.org/10.21105/joss.03743

APA

Орачев, Е. С., Карпенко, М., Алимов, П. Г., & Григорьев, С. В. (2022). SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations. The Journal of Open Source Software, 7(76), 1. [76]. https://doi.org/10.21105/joss.03743

Vancouver

Орачев ЕС, Карпенко М, Алимов ПГ, Григорьев СВ. SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations. The Journal of Open Source Software. 2022 Авг. 20;7(76):1. 76. https://doi.org/10.21105/joss.03743

Author

Орачев, Егор Станиславович ; Карпенко, Мария ; Алимов, Павел Геннадьевич ; Григорьев, Семен Вячеславович. / SPbLA: The Library of GPGPU-powered Sparse Boolean Linear Algebra Operations. в: The Journal of Open Source Software. 2022 ; Том 7, № 76. стр. 1.

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