Automatic task-based parallelization of C++ applications by source-to-source transformations

TL;DR

This paper introduces a compiler-independent, source-to-source transformation approach that automatically converts sequential C++ code into task-based parallel code using annotations, enhancing parallelization without requiring expert knowledge.

Contribution

The authors propose a novel method to automatically parallelize C++ applications by source-to-source transformations with annotations, compatible with OpenMP 4.0, and demonstrate its potential through preliminary results.

Findings

Method can generate task-based parallel code from sequential C++.

Approach is compatible with OpenMP 4.0 and compiler-independent.

Preliminary results show promising performance improvements.

Abstract

Currently, multi/many-core CPUs are considered standard in most types of computers including, mobile phones, PCs or supercomputers. However, the parallelization of applications as well as refactoring/design of applications for efficient hardware usage remains restricted to experts who have advanced technical knowledge and who can invest time tuning their software. In this context, the compilation community has proposed different methods for automatic parallelization, but their focus is traditionally on loops and nested loops with the support of polyhedral techniques. In this study, we propose a new approach to transform sequential C++ source code into a task-based parallel one by inserting annotations. We explain the different mechanisms we used to create tasks at each function/method call, and how we can limit the number of tasks. Our method can be implemented on top of the OpenMP 4.0 standard. It is compiler-independent and can rely on external well-optimized OpenMP libraries. Finally, we provide preliminary performance results that illustrate the potential of our method.