Computer Science ›› 2020, Vol. 47 ›› Issue (4): 6-12.doi: 10.11896/jsjkx.191000009

• Computer Architecture • Previous Articles     Next Articles

Efficient Implementation of Generalized Dense Symmetric Eigenproblem StandardizationAlgorithm on GPU Cluster

LIU Shi-fang1,2, ZHAO Yong-hua1, YU Tian-yu1,2, HUANG Rong-feng1,2   

  1. 1 Computer Network Information Center,Chinese Academy of Sciences,Beijing 100080,China;
    2 University of Chinese Academy of Sciences,Beijing 100080,China
  • Received:2019-10-08 Online:2020-04-15 Published:2020-04-15
  • Contact: ZHAO Yong-hua,born in 1966,Ph.D,professor,Ph.D supervisor.His main research interests include parallel algorithm and software,parallel programming model,spectral clustering data analysis and so on.
  • About author:LIU Shi-fang,born in 1994,Ph.D.Her main research interests include numerical parallel algorithm library implementation and optimization of parallel algorithm library under heterogeneous parallel machine.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China(2017YFB02022) and Strategic Priority Research Program of Chinese Academy of Sciences (XDC01040000).

Abstract: The solution of the generalized dense symmetric eigenproblem is the main task of many applied sciences and enginee-ring,and is an important part in the calculation of electromagnetics,electronic structures,finite element models and quantum che-mistry.Transforming generalized symmetric eigenproblem into a standard symmetric eigenproblem is an important computational step for solving the generalized dense symmetric eigenproblem.For the GPU cluster,the generalized blocked algorithm for gene-ralized dense symmetric eigenproblem was presented based on MPI+CUDA on GPU cluster.In order to adapt to the architecture of the GPU cluster,the generalized symmetric eigenproblem standardization algorithm presented in this paper adopts the method of combining the Cholesky decomposition of the positive definite matrix with the traditional standardized blocked algorithm,which reduces the unnecessary communication overhead in the standardized algorithm and increases the parallelism of the algorithm.Moreover,In the MPI+CUDA based generalized symmetric eigenproblem standardization algorithm,the data transferoperation between the GPU and the CPU is utilized to mask the data copy operation in the GPU,which eliminates the time spent on copying,thereby improving the performance of the program.At the same time,a fully parallel point-to-point transposition algo-rithm between the row communication domain and the column communication domain in the two-dimensional communication grid was presented.In addition,a parallel blocked algorithm based on MPI+CUDA for the solution of the triangular matrix equation BX=A with multiple right-end terms was also given.On the supercomputer system “Era” of the Computer Network Information Center of Chinese Academy of Sciences,each compute node is configured with 2 Nvidia Tesla K20 GPGPU cards and 2 Intel E5-2680 V2 processors.This paper tested different scale matrices using up to 32GPUs.The implementation performance of the ge-neralized symmetric eigenproblem standardization algorithm based on MPI+CUDA has achieved better acceleration and perfor-mance,and have good scalability.When tested with 50000×50000-order matrix using 32GPUs,the peak performance reach approximately 9.21 Tflops.

Key words: Generalized symmetric eigenproblem standardization blocked algorithm, GPU cluster, Cholesky decomposition, Transpose algorithm, Triangular matrix equations

CLC Number: 

  • TP301
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