Adaptive sparse matrix representation for efficient matrix-vector multiplication

Zardoshti, Pantea; Khunjush, Farshad; Sarbazi-Azad, Hamid

doi:10.1016/b978-0-12-803738-6.00014-8

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…This option fits some parts of the CI sparse matrix that are easy to recreate. It's worth mentioning that CPUs are faster than the GPUs when calculating the elements on the fly since CPUs have more complex chips than GPUs [14]. GPUs do branch prediction in a slower fashion than CPUs.…”

Section: The Proposed Workmentioning

confidence: 99%

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Mahmoud¹,

Hoffmann²,

Reza³

2018

Computation

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Sparse matrix-vector multiplication (SpMV) can be used to solve diverse-scaled linear systems and eigenvalue problems that exist in numerous, and varying scientific applications. One of the scientific applications that SpMV is involved in is known as Configuration Interaction (CI). CI is a linear method for solving the nonrelativistic Schrödinger equation for quantum chemical multi-electron systems, and it can deal with the ground state as well as multiple excited states. In this paper, we have developed a hybrid approach in order to deal with CI sparse matrices. The proposed model includes a newly-developed hybrid format for storing CI sparse matrices on the Graphics Processing Unit (GPU). In addition to the new developed format, the proposed model includes the SpMV kernel for multiplying the CI matrix (proposed format) by a vector using the C language and the Compute Unified Device Architecture (CUDA) platform. The proposed SpMV kernel is a vector kernel that uses the warp approach. We have gauged the newly developed model in terms of two primary factors, memory usage and performance. Our proposed kernel was compared to the cuSPARSE library and the CSR5 (Compressed Sparse Row 5) format and already outperformed both.

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Section: The Proposed Workmentioning

confidence: 99%

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Mahmoud¹,

Hoffmann²,

Reza³

2018

Computation

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“…By using a model based on the GPU's native instruction set, it predicts the performance within 5%-15% error range for blocked ELLPACK format. Zardoshti et al [24] developed an adaptive run-time approach to identify the best format among four basic formats. It executes a small portion of the input matrix and tune it with GPU architectural parameters and chooses the best performing matrix based on the portion.…”

Section: Related Workmentioning

confidence: 99%

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs

Nisa

Siegel

Sukumaran-Rajam

et al. 2018

EasyChair Preprints

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Abstract-Sparse Matrix-Vector multiplication (SpMV) is a key kernel for many applications in computational science and data analytics. Several efforts have addressed the optimization of SpMV on GPUs, and a number of compact sparse-matrix representations have been considered for it. It has been observed that the sparsity pattern of non-zero elements in a sparse matrix has a significant impact on achieving SpMV performance. Further, no single sparse-matrix format is consistently the best across the range of sparse matrices encountered in practice. In this paper, we perform a comprehensive study that explores the use of Machine Learning to answer two questions: 1) Given an unseen sparse matrix, can we effectively predict the best format for SpMV on GPUs? 2) Can SpMV execution time for that matrix be predicted, for different matrix formats?By identifying a small set of sparse matrix features to use in training the ML models, we demonstrate that efficient prediction of the best format with ≈ 88% accuracy can be achieved when selecting between six well known sparsematrix formats on two GPU architectures (NVIDIA Pascal and Kepler), and ≈ 10% relative mean error (RME) with execution time prediction.

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Adaptive sparse matrix representation for efficient matrix-vector multiplication

Cited by 2 publications

References 12 publications

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Effective Machine Learning Based Format Selection and Performance Modeling for SpMV on GPUs

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