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Design and Optimization Strategies of a High-Performance Vented Box
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BLAS3 optimization for the Godson-3B1500.

Ming Zhang1, Naijie Gu1, Kaixin Ren1

  • 1School of Computer Science and Technology, University of Science and Technology of China, 508, Elec-3(Diansan) Building, West Campus of USTC, Huang Shan Road, Hefei, Anhui Province China.

Springerplus
|December 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a performance model and optimized algorithms for general matrix multiplication (GEMM) on decoupled access/execute (DAE) architectures. The approach significantly boosts GEMM performance on the Godson-3B1500, achieving near-peak efficiency.

Keywords:
BLASDAEGodson-3B1500Performance optimization

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Area of Science:

  • High-performance computing
  • Computer architecture
  • Parallel processing

Background:

  • General Matrix Multiplication (GEMM) is a fundamental operation in scientific computing.
  • Decoupled Access/Execute (DAE) architectures present unique challenges for optimizing GEMM performance.
  • Existing performance models may not fully capture the nuances of DAE platforms.

Purpose of the Study:

  • To develop a performance model for GEMM on DAE architectures, specifically targeting the Godson-3B1500.
  • To propose optimized GEMM algorithms that leverage DAE features and minimize overhead.
  • To guide and improve GEMM performance on the Godson-3B1500 platform.

Main Methods:

  • Development of a performance model focusing on Access Processors (APs) and Execute Processors (EPs).
  • Introduction of a Synchronization Module Selection Mechanism (SMSM) to reduce synchronization overhead.
  • Design of two optimized GEMM algorithms utilizing Single Instruction Multiple Data (SIMD) vector instructions and AP-EP overlap.

Main Results:

  • The proposed algorithms achieved high efficiency on the Godson-3B1500.
  • Double-precision GEMM (dGEMM) reached 91.9% of theoretical peak performance.
  • Complex double-precision GEMM (zGEMM) reached 93% of theoretical peak performance.

Conclusions:

  • The developed performance model and optimized algorithms effectively enhance GEMM performance on DAE architectures.
  • The SMSM successfully reduces synchronization overhead, contributing to higher efficiency.
  • The results demonstrate the viability of the proposed methods for achieving near-peak performance in scientific computations on specialized hardware.