Despite the various research initiatives and proposed programming models, efficient solutions for parallel programming in HPC clusters still rely on a complex combination of different programming models (e.g., OpenMP and MPI), languages (e.g., C++ and CUDA), and specialized runtimes (e.g., Charm++ and Legion). On the other hand, task parallelism has shown to be an efficient and seamless programming model for clusters. This paper introduces OpenMP Cluster (OMPC), a task-parallel model that extends OpenMP for cluster programming. OMPC leverages OpenMP's offloading standard to distribute annotated regions of code across the nodes of a distributed system. To achieve that it hides MPI-based data distribution and load-balancing mechanisms behind OpenMP task dependencies. Given its compliance with OpenMP, OMPC allows applications to use the same programming model to exploit intra- and inter-node parallelism, thus simplifying the development process and maintenance. We evaluated OMPC using Task Bench, a synthetic benchmark focused on task parallelism, comparing its performance against other distributed runtimes. Experimental results show that OMPC can deliver up to 1.53x and 2.43x better performance than Charm++ on CCR and scalability experiments, respectively. Experiments also show that OMPC performance weakly scales for both Task Bench and a real-world seismic imaging application.
@article{arxiv.2207.05677,
title = {The OpenMP Cluster Programming Model},
author = {Hervé Yviquel and Marcio Pereira and Emílio Francesquini and Guilherme Valarini and Gustavo Leite and Pedro Rosso and Rodrigo Ceccato and Carla Cusihualpa and Vitoria Dias and Sandro Rigo and Alan Souza and Guido Araujo},
journal= {arXiv preprint arXiv:2207.05677},
year = {2022}
}
Comments
12 pages, 7 figures, 1 listing, to be published in the 51st International Conference on Parallel Processing Workshop Proceedings (ICPP Workshops 22)