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A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation...

by Joseph N Huber, Weile Wei, Giorgis Georgakoudis, Johannes Doerfert, Oscar R Hernandez Mendoza
Publication Type
Conference Paper
Book Title
OpenMP: Enabling Massive Node-Level Parallelism
Publication Date
Page Numbers
142 to 155
LNCS 12870
Publisher Location
Cham, Switzerland
Conference Name
17th International Workshop on OpenMP, IWOMP 2021
Conference Location
Online, United Kingdom
Conference Sponsor
Univ. of Bristol
Conference Date

This paper presents a methodology for using LLVM-based tools to tune the DCA++ (dynamical cluster approximation) application that targets the new ARM A64FX processor. The goal is to describe the changes required for the new architecture and generate efficient single instruction/multiple data (SIMD) instructions that target the new Scalable Vector Extension instruction set. During manual tuning, the authors used the LLVM tools to improve code parallelization by using OpenMP SIMD, refactored the code and applied transformation that enabled SIMD optimizations, and ensured that the correct libraries were used to achieve optimal performance. By applying these code changes, code speed was increased by 1.98× and 78 GFlops were achieved on the A64FX processor. The authors aim to automatize parts of the efforts in the OpenMP Advisor tool, which is built on top of existing and newly introduced LLVM tooling.