%0 Report
%D 2018
%T Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
%A Jack Dongarra
%A Iain Duff
%A Mark Gates
%A Azzam Haidar
%A Sven Hammarling
%A Nicholas J. Higham
%A Jonathan Hogg
%A Pedro Valero Lara
%A Piotr Luszczek
%A Mawussi Zounon
%A Samuel D. Relton
%A Stanimire Tomov
%A Timothy Costa
%A Sarah Knepper
%X This document describes an API for Batch Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). We focus on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The extensions beyond the original BLAS standard are considered that specify a programming interface not only for routines with uniformly-sized matrices and/or vectors but also for the situation where the sizes vary. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance manycore platforms. These include multicore and many-core CPU processors; GPUs and coprocessors; as well as other hardware accelerators with floating-point compute facility.
%8 2018-07
%G eng
%0 Journal Article
%J ACM Transactions on Mathematical Software
%D 2002
%T An Updated Set of Basic Linear Algebra Subprograms (BLAS)
%A Susan Blackford
%A James Demmel
%A Jack Dongarra
%A Iain Duff
%A Sven Hammarling
%A Greg Henry
%A Michael Heroux
%A Linda Kaufman
%A Andrew Lumsdaine
%A Antoine Petitet
%A Roldan Pozo
%A Karin Remington
%A Clint Whaley
%B ACM Transactions on Mathematical Software
%V 28
%P 135-151
%8 2002-12
%G eng
%R 10.1145/567806.567807
%0 Journal Article
%J (an update), submitted to ACM TOMS
%D 2001
%T Basic Linear Algebra Subprograms (BLAS)
%A Susan Blackford
%A James Demmel
%A Jack Dongarra
%A Iain Duff
%A Sven Hammarling
%A Greg Henry
%A Michael Heroux
%A Linda Kaufman
%A Andrew Lumsdaine
%A Antoine Petitet
%A Roldan Pozo
%A Karin Remington
%A Clint Whaley
%B (an update), submitted to ACM TOMS
%8 2001-02
%G eng
%0 Book
%B Software, Environments and Tools
%D 1998
%T Numerical Linear Algebra for High-Performance Computers
%A Jack Dongarra
%A Iain Duff
%A Danny Sorensen
%A Henk van der Vorst
%X This book presents a unified treatment of recently developed techniques and current understanding about solving systems of linear equations and large scale eigenvalue problems on high-performance computers. It provides a rapid introduction to the world of vector and parallel processing for these linear algebra applications. Topics include major elements of advanced-architecture computers and their performance, recent algorithmic development, and software for direct solution of dense matrix problems, direct solution of sparse systems of equations, iterative solution of sparse systems of equations, and solution of large sparse eigenvalue problems. This book supersedes the SIAM publication Solving Linear Systems on Vector and Shared Memory Computers, which appeared in 1990. The new book includes a considerable amount of new material in addition to incorporating a substantial revision of existing text.
%B Software, Environments and Tools
%I SIAM
%G eng
%R https://doi.org/10.1137/1.9780898719611