An Extended Polyhedral Model for SPMD Programs and Its Use in Static Data Race Detection

被引：21
|
作者：
Chatarasi, Prasanth [1 ]
Shirako, Jun [1 ]
Kong, Martin [1 ]
Sarkar, Vivek [1 ]
机构：
[1] Rice Univ, Houston, TX 77005 USA
来源：
LANGUAGES AND COMPILERS FOR PARALLEL COMPUTING, LCPC 2016 | 2017年 / 10136卷
基金：
美国国家科学基金会;
关键词：
SPMD parallelism; Data race detection; Polyhedral model; Phase mapping; Space mapping; May happen in parallel relations; CONCURRENCY ANALYSIS; BARRIERS;
D O I：
10.1007/978-3-319-52709-3_10
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Despite its age, SPMD (Single Program Multiple Data) parallelism continues to be one of the most popular parallel execution models in use today, as exemplified by OpenMP for multicore systems and CUDA and OpenCL for accelerator systems. The basic idea behind the SPMD model, which makes it different from task-parallel models, is that all logical processors (worker threads) execute the same program with sequential code executed redundantly and parallel code executed cooperatively. In this paper, we extend the polyhedral model to enable analysis of explicitly parallel SPMD programs and provide a new approach for static detection of data races in SPMD programs using the extended polyhedral model. We evaluate our approach using 34 OpenMP programs from the OmpSCR and PolyBench-ACC (PolyBench-ACC derives from the PolyBench benchmark suite and provides OpenMP, OpenACC, CUDA, OpenCL and HMPP implementations.) benchmark suites.
引用
收藏
页码：106 / 120
页数：15
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