Title: Heterogeneous computing with OpenMP and Hydra

Abstract

Summary High‐performance computing relies on accelerators (such as GPGPUs) to achieve fast execution of scientific applications. Traditionally, these accelerators have been programmed with specialized languages, such as CUDA or OpenCL. In recent years, OpenMP emerged as a promising alternative for supporting accelerators, providing advantages such as maintaining a single code base for the host and different accelerator types and providing a simple way to extend support for accelerators to existing application codes. Efficiently using this support requires solving several challenges, related to performance, work partitioning, and concurrent execution on multiple device types. In this article, we discuss our experiences with using OpenMP for accelerators and present performance guidelines. We also introduce a library, Hydra, that addresses several of the challenges of using OpenMP for such devices. We apply Hydra to a scientific application, PlasCom2, that has not previously been able to use accelerators. Experiments on three architectures show that Hydra results in performance gains of up to 10× compared with CPU‐only execution. Concurrent execution on the host and GPU resulted in additional gains of up to 20% compared to running on the GPU only.

Authors:

Diener, Matthias  ^[1];

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Kale, Laxmikant V. ^[1]; Bodony, Daniel J. ^[1]

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1. University of Illinois at Urbana‐Champaign Champaign Illinois USA

Publication Date:

Sat Mar 07 00:00:00 EST 2020

Sponsoring Org.:

USDOE

OSTI Identifier:

1603688

Resource Type:

Publisher's Accepted Manuscript

Journal Name:

Concurrency and Computation. Practice and Experience

Additional Journal Information:

Journal Name: Concurrency and Computation. Practice and Experience Journal Volume: 32 Journal Issue: 20; Journal ID: ISSN 1532-0626

Publisher:

Wiley Blackwell (John Wiley & Sons)

Country of Publication:

United Kingdom

Language:

English