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GPU accelerated method for performing batch processing of isomorphic sparse matrixes multiplied by full vectors

A sparse matrix and processing method technology, applied in processor architecture/configuration, complex mathematical operations, etc., can solve problems such as multiplication of full vectors by isomorphic sparse matrices that have not been batch-processed, and inability to fully utilize the advantages of GPUs in programs to achieve improved Parallelism, the effect of solving time-consuming and reducing calculation time

Active Publication Date: 2017-02-15
SOUTHEAST UNIV
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Problems solved by technology

Scholars at home and abroad have begun to study the method of GPU accelerated iterative solution of sparse linear equations, but they have not done special research on the accelerated solution of the important module sparse matrix multiplied by full vector, nor have they done batch processing isomorphic sparse The work of matrix multiplication full of vectors cannot make the program take full advantage of the GPU

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  • GPU accelerated method for performing batch processing of isomorphic sparse matrixes multiplied by full vectors
  • GPU accelerated method for performing batch processing of isomorphic sparse matrixes multiplied by full vectors
  • GPU accelerated method for performing batch processing of isomorphic sparse matrixes multiplied by full vectors

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Embodiment Construction

[0036] Such as image 3 As shown, a GPU-accelerated batch processing method for multiplying full vectors by a GPU-accelerated homogeneous sparse matrix, a large number of homogeneous sparse matrices A 1 ~A bs The multiplication vector operation: A 1 x 1 =b 1 ,...,A bs x bs =b bs , where x 1 ~x bs is the full vector to be multiplied, b 1 ~b bs Be the result full vector, bs is the matrix quantity of batch processing, described method comprises:

[0037] (1) Put all the matrices A in the CPU 1 ~A bs Stored in a row-compressed storage format, matrix A 1 ~A bs Share the same row offset array CSR_Row and column number array CSR_Col, the row offset array element CSR_Row[k] stores the total number of non-zero elements before the kth row of the matrix, and the value of k ranges from 1 to n+1; every The specific values ​​of each matrix are stored in their respective value arrays CSR_Val 1 ~CSR_Val bs In, the multiplied vector is stored in the array x 1 ~x bs , the res...

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Abstract

The invention discloses a GPU accelerated method for performing batch processing of isomorphic sparse matrixes multiplied by full vectors. The method comprises the following steps of: (1), storing all matrixes A<1>-A<bs> in a row compression storage format in a CPU; (2), transmitting data required by a GPU kernel function to a GPU by the CPU; (3), distributing full vector multiplication tasks of the matrixes A<1>-A<bs> to GPU threads, and optimizing a memory access mode; and (4), executing batch processing of the isomorphic sparse matrixes multiplied by a full vector kernel function spmv_batch in the GPU, and calling the kernel function to perform batch processing of parallel computing of the isomorphic sparse matrixes multiplied by the full vectors. In the method disclosed by the invention, the CPU is responsible for controlling the whole process of a program and preparing data; the GPU is responsible for computing intensive vector multiplication; the algorithm parallelism and the access efficiency are increased by utilization of a batching processing mode; and thus, the computing time of batch sparse matrixes multiplied by full vectors is greatly reduced.

Description

technical field [0001] The invention belongs to the application field of high-performance computing in power systems, and in particular relates to a GPU-accelerated batch processing method for multiplying full vectors by isomorphic sparse matrices. Background technique [0002] Power flow calculation is the most widely used, basic and important electrical calculation in power system. In the study of power system operation mode and planning scheme, power flow calculation is required to compare the feasibility, reliability and economy of the operation mode or planning power supply scheme. At the same time, in order to monitor the operating status of the power system in real time, a large number of fast power flow calculations are also required. Therefore, when planning and designing the system and arranging the operation mode of the system, the offline power flow calculation is used; in the real-time monitoring of the power system operating status, the online power flow calcu...

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Application Information

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IPC IPC(8): G06F17/16G06T1/20
CPCG06F17/16G06T1/20
Inventor 周赣孙立成秦成明张旭柏瑞冯燕钧傅萌
Owner SOUTHEAST UNIV
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