Publications

Version: 1.0 Approved

LDO-ID 2021-06-09-01

Title Practical Parallelization of Scientific Applications with OpenMP, OpenACC and MPI

Abstract

This work aims at distilling a systematic methodology to modernize existing sequential scienti c codes with a little re-designing e ort, turning an old codebase into modern code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scienti c applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate that the same methodology works for the parallelization in the shared memory model (via OpenMP), message passing model (via MPI), and General Purpose Computing on GPU model (via OpenACC). The method is demonstrated parallelizing four real-world sequential codes in the domain of physics and material science. The methodology itself has been distilled in collaboration with MSc students of the Parallel Computing course at the University of Torino, that applied it for the  rst time to the project works that they presented for the  nal exam of the course. Every year the course hosts some special lectures from industry representatives, who present how they use parallel computing and o er codes to be parallelized.

Authors Aldinucci Marco, Cesare Valentina, Colonnelli Iacopo, Martinelli Alberto Riccardo, Mittone Gianluca, Cantalupo Barbara, Cavazzoni Carlo, Drocco Maurizio

Type Paper for Specialistic Magazine

Media Journal of Parallel and Distributed Computing (Elsevier B.V., Volume 157, November 2021)

Web site

https://www.sciencedirect.com/science/article/abs/pii/S0743731521001295

Anno 2021