ScaLAPACK Users' Guide

Blackford, L. S.; Choi, J.; Cleary, A.; D’Azevedo, E.; Demmel, J.; Dhillon, Inderjit S.; Dongarra, Jack; Hammarling, Sven; Henry, Greg; Petitet, Antoine; Stanley, K.; Walker, D.; Whaley, R. Clint

doi:10.1137/1.9780898719642

Cited by 1,138 publications

(1,012 citation statements)

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“…The BLAS, LAPACK, and ScaLAPACK [5] projects place backward compatibility as a high priority, which hinders progress towards adopting modern software engineering principles such as object abstraction. libflame is built around opaque structures that hide implementation details of matrices, such as data layout, and exports object-based programming interfaces to operate upon these structures [4].…”

Section: What Is Differentmentioning

confidence: 99%

Introducing: The Libflame Library for Dense Matrix Computations

et al. 2019

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As part of the FLAME project, we have been dilligently developing new methodologies for analyzing, designing, and implementing linear algebra libraries. While we did not know it when we started, these techniques appear to solve many of the programmability problems that now face us with the advent of multicore and many-core architectures. These efforts have culminated in a new library, libflame, which strives to replace similar libraries that date back to the late 20th century. With this paper, we introduce the scientific computing community to this library. Why a New LibraryHow do we convince people that in programming simplicity and clarity -in short: what mathematicians call "elegance" -are not a dispensable luxury, but a crucial matter that decides between success and failure?Edsger W. DijkstraDuring the past decade, the FLAME project, a collaborative effort between The University of Texas at Austin and Universidad Jaime I de Castellon, has developed a unique methodology, notation, tools, and set of application programming interfaces (APIs) for deriving and representing linear algebra libraries. In an effort to better promote the techniques characteristic to the FLAME project, we have implemented a functional library that demonstrates findings and insights from the last decade of research. We call this library libflame. The primary purpose of libflame is to provide the scientific and numerical computing communities with a modern, high-performance dense linear algebra library that is extensible, easy to use, and available under an open source license. Its developers have published two books, numerous papers, and even more working notes over the last decade documenting the challenges and motivations that led to the APIs and implementations present within the libflame library [11]. Seasoned users within scientific and numerical computing circles will quickly recognize the general set of functionality targeted by libflame. In short, in libflame we wish to provide not only a framework for developing dense linear algebra solutions, but also a ready-made library that is, by almost any metric, easier to use and offers competitive (and in many cases superior) real-world performance when compared to the more traditional Basic Linear Algebra Subprograms (BLAS) [8,9,16] and Linear Algebra PACKage (LAPACK) libraries [1].The purpose of this article is to briefly introduce both the philosophy that underlies the library and the library itself. Evidence of how easily it can be retargeted to what are often considered "hostile" environments is presented in form of performance results on different architectures.

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Section: What Is Differentmentioning

confidence: 99%

Introducing: The Libflame Library for Dense Matrix Computations

et al. 2019

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“…A blockcyclic distribution generalizes blocked and cyclic distributions, by distributing contiguous blocks of any size b cyclically among processors. Cyclic decompositions are commonly used in parallel numerical linear algebra algorithms and frameworks such as ScaLAPACK (block-cyclic) [7] and Elemental (cyclic) [40]. Our method extends this decomposition to tensors.…”

Section: Cyclic Tensor Blockingmentioning

confidence: 99%

A massively parallel tensor contraction framework for coupled-cluster computations

Solomonik

Matthews

Hammond

et al. 2014

Journal of Parallel and Distributed Computing

175

164

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“…There are four basic steps involved in calling a ScaLAPACK routine. The reader is directed to the ScaLAPACK users' guide [15] for more details.…”

Section: Inputsmentioning

confidence: 99%

“…It is observed that for a process arrangement, execution times are the same no matter what algorithmic blocking factor is used. However to ensure efficient data distribution, ScaLAPACK [15] recommends that any blocking factor between 32 to 64 be used to distribute the rows and the columns of the matrices involved in the computation of the linear algebra kernel. The tool uses a value of 64; 3.…”

Section: Porting Issuesmentioning

confidence: 99%

HeteroMPI+ScaLAPACK: Towards a ScaLAPACK (Dense Linear Solvers) on Heterogeneous Networks of Computers

Reddy¹,

Lastovetsky

2006

High Performance Computing - HiPC 2006

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Abstract. The paper presents a tool that ports ScaLAPACK programs designed to run on massively parallel processors to Heterogeneous Networks of Computers. The tool converts ScaLAPACK programs to HeteroMPI programs. The resulting HeteroMPI programs do not aim to extract the maximum performance from a Heterogeneous Networks of Computers but provide an easy and simple way to execute the ScaLAPACK programs on such networks with good performance improvements. We demonstrate the efficiency of the resulting HeteroMPI programs by performing experiments with a matrix multiplication application on a local network of heterogeneous computers.

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ScaLAPACK Users' Guide

Cited by 1,138 publications

References 0 publications

Introducing: The Libflame Library for Dense Matrix Computations

Introducing: The Libflame Library for Dense Matrix Computations

A massively parallel tensor contraction framework for coupled-cluster computations

HeteroMPI+ScaLAPACK: Towards a ScaLAPACK (Dense Linear Solvers) on Heterogeneous Networks of Computers

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