Using shared-data localization to reduce the cost of inspector-execution in unified-parallel-C programs

Abstract

Programs written in the Unified Parallel C (UPC) language can access any location of the entire local and remote address space via read/write operations. However, UPC programs that contain fine-grained shared accesses can exhibit performance degradation. One solution is to use the inspector-executor technique to coalesce fine-grained shared accesses to larger remote access operations. A straightforward implementation of the inspectorexecutor transformation resultin excessive instrumentation that hinders performance.This paper addresses this issue and introduces various techniques that aim at reducing the generated instrumentation code: a shared-data localization transformation based on Constant-Stride Linear Memory Descriptors (CSLMADs) [32], the inlining of data locality checks and the usage of an index vector to aggregate the data. Finally, the paper introduces a lightweight * Corresponding author loop code motion transformation to privatize shared scalars that were propagated through the loop body. A performance evaluation, using up to 2048 cores of a POWER 775, explores the impact of each optimization and characterizes the overheads of UPC programs. It also shows that the presented optimizations increase performance of UPC programs up to 1.8× their UPC hand-optimized counterpart for applications with regular accesses and up to 6.3× for applications with irregular accesses.