Parallel Sparse Matrix Multiplication for Preconditioning and SSTA on a Many-Core Architecture

Zhang, Keliang; Wu, Bowen

doi:10.1109/nas.2012.11

Cited by 4 publications

(4 citation statements)

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“…Several approaches have been developed for representing sparse matrices aiming at improving the efficiency of memory usage and the computation of arithmetic operations. In [60] the focus was on reducing the number of accesses for particular matrix operations and defining a more natural mapping between the indices in the physical value matrix and the logical sparse coefficient matrix. In [20], the authors proposed an approach to customise the matrix representation according to the specific sparseness characteristics of matrices and the target machine by performing register and cache level optimisations.…”

Section: Advances In Sparse Matricesmentioning

confidence: 99%

“…Thus, the development and advances on computer hardware have thoroughly influenced the development of linear algebra algorithms. In this context, the parallel processing of matrix operations in distributed memory architectures arises as an important field of study [60,20,38]. In particular, operations with dense matrices have been the subject of intensive research [38,8,9,23,59,10], whereas the problem of operating with sparse matrices has comparatively received less attention.…”

Section: Introductionmentioning

confidence: 99%

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A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

et al. 2016

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Matrix computations are both fundamental and ubiquitous in computational science, and as a result they are frequently used in numerous disciplines of scientific computing and engineering. Due to the highcomputational complexity of matrix operations, which makes them critical to the performance of a large number of applications, their efficient execution in distributed environments becomes a crucial issue. This work proposes a novel approach for distributing sparse-matrix arithmetic operations on computer clusters aiming at speeding-up the processing of high-dimensional matrices. The approach focuses on how to split such operations into independent parallel tasks by considering the intrinsic characteristics that distinguish each type of operation and the particular matrices involved. The approach was applied to the most commonly-used arithmetic operations between matrices. The performance of the presented approach was evaluated considering a high-dimensional text feature selection approach and two real-world datasets. Experimental evaluation showed that the proposed approach helped to significantly reduce the computing times of big-scale matrix operations, when compared to serial and multi-thread implementations as well as several linear-algebra software libraries.

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Section: Advances In Sparse Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

et al. 2016

View full text Add to dashboard Cite

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“…In [4] the focus is on reducing the number of accesses for particular matrix operations and defining a more natural mapping between the indices in the physical value matrix and the logical sparse coefficient matrix. In [5] the authors propose an approach to customise the matrix representation according to the specific sparseness characteristics of matrices and the target machine by performing register and cache level optimisations, for example by adding explicit zeros to improve the memory system behaviour.…”

Section: Related Workmentioning

confidence: 99%

“…Single-processor architectures have evolved into multi-core architectures and, in recent years, Graphics Processing Units (GPUs) have emerged as co-processors capable of handling large amount of calculations. In this context, the parallel processing of matrix operations in distributed memory architectures arises as an important field of study [4]- [6]. In particular, the operations with dense matrices have been the subject of intensive research.…”

Section: Introductionmentioning

confidence: 99%

Sparse-matrix arithmetic operations in computer clusters: A text feature selection application

Tommasel¹,

Mateos²,

Godoy³

et al. 2014

2014 IEEE Biennial Congress of Argentina (ARGENCON)

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Arithmetic operations on matrices are frequently used in scientific computing areas. They usually become a performance bottleneck due to their high complexity. In this context, the parallel processing of matrix operations in distributed environments arises as an important field of study. This work presents several strategies for distributing sparse matrix arithmetic operations on computer clusters, focusing on the intrinsic characteristics of the operations and the matrices involved. The performance of the proposed strategies for determining the number of parallel tasks to be executed on the computer cluster was evaluated considering a high-dimensional feature selection approach. Additionally, the performance of two alternatives for efficiently representing bigscale sparse matrices was tested. Experimental results showed that the proposed strategies significantly reduce the computing time of matrix operations, outperforming computations based on serial and multi-thread implementations.Resumen-Las operaciones aritméticas matriciales son frecuentemente utilizadas en muchas áreas de la computación científica. Usualmente, son un cuello de botella debido a su alta complejidad computacional. En este contexto, el procesamiento paralelo de operaciones matriciales en ambientes distribuidos surge como un importante tópico de estudio. Este trabajo presenta diversas estrategias para la distribución de operaciones aritméticas entre matrices ralas en clusters de computadoras. Dichas estrategias se basan en las características intrínsecas de las operaciones y las matrices involucradas. El desempeño de las estrategias propuestas para determinar la cantidad de tareas paralelas a ejecutar, fue evaluado considerando un enfoque de selección de atributos multidimensional. Además, se evaluó el desempeño de dos alternativas para la representación eficiente de matrices ralas a gran escala. Los resultados experimentales mostraron que las estrategias propuestas redujeron los tiempos de cómputo de las operaciones, mejorando así significativamente los tiempos de las implementaciones seriales y multi-thread.

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Task Scheduling for GPU Heterogeneous Cluster

Zhang

2012

2012 IEEE International Conference on Cluster Computing Workshops

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Parallel Sparse Matrix Multiplication for Preconditioning and SSTA on a Many-Core Architecture

Cited by 4 publications

References 20 publications

A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

A distributed approach for accelerating sparse matrix arithmetic operations for high-dimensional feature selection

Sparse-matrix arithmetic operations in computer clusters: A text feature selection application

Task Scheduling for GPU Heterogeneous Cluster

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