Computer Science ›› 2015, Vol. 42 ›› Issue (12): 13-17.

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Quantitative Performance Analysis Model of Matrix Multiplication Based on GPU

YIN Meng-jia, XU Xian-bin, XIONG Zeng-gang and ZHANG Tao   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Performance evaluation and optimization are indispensable work when designing efficient parallel program,and the performance of storage system directly affects the performance of the processor.We used GPGPU-Sim to simulate the storage hierarchy of GPU,and found out optimal quantity allocation relationship between SM and storage controller in GPU.Matrix multiplication is an essential part in the field of scientific computing,as a representative application with both computation and memory access intensiveness,and its performance is an important indicator of GPU high-performance computing.Performance model is a new technology solution as parallel systems performance evaluation,which has many advantages.In order to improve the performance of matrix multiplication,this paper proposed a quantitative performance model based on GPU.The model quantitatively analyzes instruction pipeline,shared memory access and global memory access,establishes the performance model,finds the performance bottlenecks and improves the execution speed.The experiment proves the model has practicability,and effectively realizes the optimization of the matrix multiplication algorithm.

Key words: GPU,GPGPU-Sim,Matrix multiplication,Quantitative performance analysis model,Instruction pipeline,Shared memory access,Global memory access

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