'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Directive-drive programming models, such as OpenMP, are one solution for exploiting the potential of multi-core architectures, and enable developers to accelerate software applications by adding annotations on for-type loops and other code regions. However, manual parallelization of applications is known to be a non trivial and time consuming process, requiring parallel programming skills. Automatic parallelization approaches can reduce the burden on the application development side. This paper presents an OpenMP based automatic parallelization compiler, named AutoPar-Clava, for automatic identification and annotation of loops in C code. By using static analysis, parallelizable regions are detected, and a compilable OpenMP parallel code from the sequential version is produced. In order to reduce the accesses to shared memory by each thread, each variable is categorized into the proper OpenMP scoping. Also, AutoPar-Clava is able to support reduction on arrays, which is available since OpenMP 4.5. The effectiveness of AutoPar-Clava is evaluated by means of the Polyhedral Benchmark suite, and targeting a N-cores x86-based computing platform. The achieved results are very promising and compare favorably with closely related auto-parallelization compilers such as Intel C/C++ Compiler (i.e., icc), ROSE, TRACO, and Cetus
