Journal ArticleDOI
An overview of the sparse basic linear algebra subprograms: The new standard from the BLAS technical forum
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TLDR
The interface design for the Sparse Basic Linear Algebra Subprograms (BLAS) is discussed, the kernels in the recent standard that are concerned with unstructured sparse matrices are discussed, and how this interface can shield one from concern over the specific storage scheme for the sparse matrix.Abstract:
We discuss the interface design for the Sparse Basic Linear Algebra Subprograms (BLAS), the kernels in the recent standard from the BLAS Technical Forum that are concerned with unstructured sparse matrices. The motivation for such a standard is to encourage portable programming while allowing for library-specific optimizations. In particular, we show how this interface can shield one from concern over the specific storage scheme for the sparse matrix. This design makes it easy to add further functionality to the sparse BLAS in the future.We illustrate the use of the Sparse BLAS with examples in the three supported programming languages, Fortran 95, Fortran 77, and C.read more
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Proceedings ArticleDOI
Cambricon-x: an accelerator for sparse neural networks
Zhang Shijin,Zidong Du,Lei Zhang,Lan Huiying,Liu Shaoli,Ling Li,Qi Guo,Tianshi Chen,Yunji Chen +8 more
TL;DR: A novel accelerator is proposed, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency and experimental results show that this accelerator achieves, on average, 7.23x speedup and 6.43x energy saving against the state-of-the-art NN accelerator.
Journal Article
An Updated Set of Basic Linear Algebra Subprograms (BLAS)
Susan Blackford,James Demmel,Jack Dongarra,Iain S. Duff,Sven Hammarling,Greg Henry,Michael A. Heroux,Linda Kaufman,Andrew Lumsdaine,A. Petitet,Roldan Pozo,Karin A Remington,Clint Whaley +12 more
TL;DR: In this paper, the authors present a list of the companies that have contributed to the development of the Numerical Algorithms Group (NALG), including Intel, Sandia National Laboratories, and IBM.
Journal ArticleDOI
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
TL;DR: An overview of OSKI is provided, which is based on research on automatically tuned sparse kernels for modern cache-based superscalar machines, and the primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine.
Journal ArticleDOI
Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators
TL;DR: It is shown that the sums of the form $\sum_{k=0}^p \varphi_k(A)u_k$ that arise in exponential integrators can be expressed in terms of a single exponential of a matrix of dimension $n+p$ built by augmenting $A$ with additional rows and columns, and the algorithm of this paper can be employed.
Journal ArticleDOI
The Combinatorial BLAS: design, implementation, and applications
Aydin Buluc,John R. Gilbert +1 more
TL;DR: The parallel Combinatorial BLAS is described, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications, and an extensible library interface and some guiding principles for future development are provided.
References
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Book
Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods
TL;DR: In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are introduced to meet the needs of both the traditional user and the high-performance specialist.
Journal ArticleDOI
A set of level 3 basic linear algebra subprograms
TL;DR: This paper describes an extension to the set of Basic Linear Algebra Subprograms targeted at matrix-vector operations that should provide for efficient and portable implementations of algorithms for high-performance computers.
MonographDOI
Direct methods for sparse matrices
TL;DR: This book aims to be suitable also for a student course, probably at MSc level, and the subject is intensely practical and this book is written with practicalities ever in mind.
Book
Numerical Methods for Large Eigenvalue Problems
TL;DR: This chapter discusses matrix theory and linear algebra techniques used in spectral approximation, including Krylov subspace methods, and some of the origins of matrix eigenvalue problems.