AutOMP: An Automatic OpenMP Parallelization Generator for Variable-Oriented High-Performance Scientific Codes

TL;DR

AutOMP is an automatic tool that generates OpenMP parallelization directives for scientific codes by analyzing variable usage within loops, simplifying the process of modernizing legacy parallel applications.

Contribution

It introduces an automatic pre-processor that identifies variable privatization, synchronization, and reduction needs in scientific code loops for OpenMP parallelization.

Findings

Automates variable privatization and synchronization detection.

Reduces manual effort in parallelizing legacy scientific codes.

Provides ready-to-use OpenMP directives for loop parallelization.

Abstract

OpenMP is a cross-platform API that extends C, C++ and Fortran and provides shared-memory parallelism platform for those languages. The use of many cores and HPC technologies for scientific computing has been spread since the 1990s, and now takes part in many fields of research. The relative ease of implementing OpenMP, along with the development of multi-core shared memory processors (such as Intel Xeon Phi) makes OpenMP a favorable method for parallelization in the process of modernizing a legacy codes. Legacy scientific codes are usually holding large number of physical arrays which being used and updated by the code routines. In most of the cases the parallelization of such code focuses on loop parallelization. A key step in this parallelization is deciding which of the variables in the parallelized scope should be private (so each thread will hold a copy of them), and which variables should be shared across the threads. Other important step is finding which variables should be synchronized after the loop execution. In this work we present an automatic pre-processor that preforms these stages - AutOMP (Automatic OpenMP). AutOMP recognize all the variables assignments inside a loop. These variables will be private unless the assignment is of an array element which depend on the loop index variable. Afterwards, AutOMP finds the places where threads synchronization is needed, and which reduction operator is to be used. At last, the program provides the parallelization command to be used for parallelizing the loop.