Paper Info
Title
An Extended Polyhedral Model for SPMD Programs and Its Use in Static Data Race Detection.
Abstract
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.
Year
DOI
Venue
2016
10.1007/978-3-319-52709-3_10
Lecture Notes in Computer Science
Keywords
Field
DocType
SPMD parallelism,Data race detection,Polyhedral model,Phase mapping,Space mapping,May happen in parallel relations
SPMD,Multiple data,Static data,Suite,Computer science,CUDA,Parallel computing,Implementation,Thread (computing),Polytope model
Conference
Volume
ISSN
Citations 
10136
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Prasanth Chatarasi1363.35
Jun Shirako243334.56
Martin Kong3896.18
Vivek Sarkar44318409.41