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Index Terms
- An updated set of basic linear algebra subprograms (BLAS)
Recommendations
A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines
This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a ...
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-...
The BLAS API of BLASFEO: Optimizing Performance for Small Matrices
Basic Linear Algebra Subroutines For Embedded Optimization (BLASFEO) is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization and other applications targeting ...
Reviews
Jesse Louis Barlow
The basic linear algebra subroutines (BLAS) have become an essential part of the development of numerical software in Fortran, and in other languages that have versions of them. Their beauty has always been that computer manufacturers have been encouraged to implement the BLAS as efficiently as possible. The BLAS began with the simple observation that a number of vector operations (dot product, Euclidean norm, vector scale, and add) were commonly implemented in numerical software, and thus a set of Fortran subroutines for them with standardized names was proposed [1]. The linear algebraic equation package (LINPACK) project [2] was built around these routines for the sake of modularity and portability. The vector BLAS are now called level-1 BLAS. Level-2 BLAS (matrix-vector operations) [3], and level-3 BLAS (matrix-matrix operations) [4] were developed in tandem with another linear algebra package (LAPACK) project [5]. Conventional wisdom is that numerical algorithm design should take advantage of level-3 BLAS operations to the greatest extent possible, since this reduces the ratio of (costly) memory references to (cheap) arithmetic operations. This paper updates the ongoing BLAS effort, and summarizes what types of operations are now available. We now have flavors of BLAS for dense, banded, and sparse vector and matrix operations. Some BLAS are also supported in extended and mixed precision. That effort is carefully chronicled here, by some of the many contributors to the BLAS. Online Computing Reviews Service
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