Optimizing Sparse Matrix-vector Multiplication On Gpus

Furthermore the introduction of new APIs for general-purpose computations on GPUs namely CUDA from NVIDIA Stream SDK from AMD and OpenCL makes GPUs an attractive choice for high-performance. AbstractSparse Matrix-Vector multiplication SpMV is a key kernel for many applications in computational science and data analytics.


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Several efforts have addressed the optimization of SpMV on GPUs and a number of compact sparse-matrix representations have been considered for it.

Optimizing sparse matrix-vector multiplication on gpus. Sparse Matrix-Vector multiplication SpMV is one. Optimizing SpMM for the GPU 4 21 SpMM Given two sparse matrices A2Rm k and B2Rk n for kmn2N SpMM multiplication computes C AB. Furthermore the introduction of new APIs for general-purpose computations on GPUs namely CUDA from NVIDIA Stream SDK from AMD and OpenCL makes GPUs an attractive choice for high-performance numerical and scientific computing.

Optimizing sparse matrix-vector multiplication on GPUs using compile-time and run-time strategies. For example for the Nvidia Fermi GPU architec-ture each executable GPU kernel is launched with a xed. In this paper we propose an efficient data structure named AdELL for optimizing the SpMV kernel on GPUs focusing on performance bottlenecks of sparse computation.

Most of the existing studies dedicated to improving this kernel have been targeting just one type of processing units mainly multicore CPUs or graphics processing units GPUs and have not explored the potential of the recent rapidly emerging CPU-GPU heterogeneous platforms. 1 where C2Rm n. Published by Elsevier BV.

Sparse matrix-vector multiplication on GPUs requires im-plementations that are carefully optimized for the underly-ing graphics hardware of which the architecture is massively threaded and signi cantly di erent from general CPU archi-tectures. Performance on GPU is measured for CSR CSR-Vector CSR-Adaptive ELL COO SCOO HYB matrix formats. The matrix-vector product will be a vector y with length m.

The matrices were taken from the SuiteSparse Matrix Collection formerly the University of Florida Sparse Matrix Collection. The sparsity of Aand Bimplies that both input matrices are represented in a space-e cient format that avoids storing explicit zero values. In the final figure you could find results for single.

21 Matrix-Vector Multiplication Let A be an m n matrix and x be a vector of length n. We achieve this objective by proposing a novel SpMV algorithm based on the compressed sparse row CSR on the GPU. Sparse matrix-vector multiplication SpMV is an important operation in computational science and needs be accelerated because it often represents the dominant cost in many widely used iterative methods and eigenvalue problems.

It is necessary to use efficient data structure and optimized Large size S parse Matrix-Vector Multiplication S pMV is one of algorithm to store and manipulate sparse matrices. OPTIMIZING SPARSE MATRIX-MATRIX MULTIPLICATION ON A HETEROGENEOUS CPU-GPU PLATFORM by XIAOLONG WU Under the Direction of Sushil K. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science doi.

Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units GPU has been at the heart of numerous studies in both academia and industry. We are witnessing the emergence of Graphics Processor units GPUs as powerful massively parallel systems. Although most important operation in scientific and engineering computing.

This paper presents optimization techniques for SpMV for the Compressed Row Storage CRS format on NVIDIA Kepler architecture GPUs using CUDA. We denote nnzA as the number of nonzeros in sparse matrix A. Implementing SpGEMM e ciently on throughput-oriented processors such as the graphics processing unit GPU re-.

Implementing SpGEMM efficiently on throughput-oriented processors such as the graphics processing unit GPU requires the programmer to expose substantial fine-grained parallelism while conserving the limited off-chip memory bandwidth. Olsonx Abstract Sparse matrix-matrix multiplication SpGEMM is a key operation in numerous ar-eas from information to the physical sciences. Sparse matrix-vector multiplication SpMV is an important operation in scientific and engineering computing.

For an operation with the. The foundation of our work is an ELL-based adaptive format which copes with matrix irregularity using balanced warps composed using a parametrized warp-balancing heuristic. 101016jprocs201305196 International Conference on Computational Science ICCS 2013 An architecture-aware technique for optimizing sparse matrix-vector multiplication on GPUs Marco.

RC24704 W0812-047 December 8 2008 Computer Science IBM Research Report Optimizing Sparse Matrix-Vector Multiplication on GPUs Using Compile-time and Run-time Strategies Muthu Manikandan Baskaran Department of Computer Science and Engineering The Ohio. Prasad PhD ABSTRACT Sparse Matrix-Matrix multiplication SpMM is a fundamental operation over irregular data which is widely used in graph algorithms such as finding minimum spanning trees and shortest paths. Optimizing Sparse Matrix-Vector Multiplication on GPUs We are witnessing the emergence of Graphics Processor units GPUs as powerful massively parallel systems.

Implementation of S pMV has become complex SpMV is a prominent kernel used in many. It has been observed that the sparsity pattern of. Our implementation is based on an existing method proposed for the Fermi architecture an earlier generation of the GPU.

Sparse matrix--matrix multiplication SpGEMM is a key operation in numerous areas from information to the physical sciences. Optimizing Sparse Matrix-Matrix Multiplication for the GPU Steven Daltony Nathan Bellz Luke N. Sparse matrixvector multiplication SpMV kernel dominates the computing cost in numerous applications.

The multiplication y Ax is de ned as follows y i Xn j1 a ij x j 8i 1m Sparse matrix-vector multiplication Sparse matrices are stored in designated data structures. Here you can find some performance results for Sparse Matrix-Vector multiplication on CPU and GPU. Up to 10 cash back Abstract.

In this article we present a novel non-parametric self-tunable approach to data representation for computing this kernel particularly targeting sparse matrices.


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