sKokkos: Enabling Kokkos with Transparent Device Selection on Heterogeneous Systems using OpenACC

Pedro Valero-Lara, Seyong Lee, Joel Denny, Keita Teranishi, Jeffrey Vetter, Marc Gonzalez-Tallada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper presents a new feature to enable Kokkos with transparent device selection. For application developers, it is not easy to identify which device is the most appropriate to use in a heterogeneous system, since this depends on the characteristics of both the application and the hardware. In Kokkos, a backend is associated with one specific programming model/hardware. Programmers decide which backend to use at compilation time. This new feature implemented on the OpenACC backend eliminates the burden of deciding which device to use, providing a highly productive programming solution for Kokkos applications. This work includes implementation details and a performance study conducted with a set of mini-benchmarks (i.e., AXPY and dot product), kernels (Lattice-Bolzmann method), and two mini-apps (LULESH and miniFE) on two heterogeneous systems with different hardware capabilities. This new Kokkos feature provides high accelerations of up to 35 × thanks to automatic and transparent device selection.

Original languageEnglish
Title of host publicationProceedings of the International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2024
PublisherAssociation for Computing Machinery
Pages23-34
Number of pages12
ISBN (Electronic)9798400708893
DOIs
StatePublished - Jan 18 2024
Event7th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2024 - Nagoya, Japan
Duration: Jan 25 2024 → Jan 27 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2024
Country/TerritoryJapan
CityNagoya
Period01/25/24 → 01/27/24

Funding

This research used resources of the Oak Ridge Leadership Computing Facility and the Experimental Computing Laboratory at the Oak Ridge National Laboratory, which is supported by DOE’s Office of Science under Contract No. DE-AC05-00OR22725. This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the DOE’s Office of Science and the National Nuclear Security Administration. This material is based upon work by the RAPIDS Institute, supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, and Scientific Discovery through Advanced Computing (SciDAC) program. This manuscript has been authored by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 with the DOE. The publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript or allow others to do so, for US Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U.S. Department of Energy
Office of Science17-SC-20-SC, DE-AC05-00OR22725
National Nuclear Security Administration
Advanced Scientific Computing Research
UT-Battelle

    Keywords

    • Auto-tuning
    • C++ Metaprogramming
    • CPU
    • GPU
    • Heterogeneous Systems
    • Kokkos
    • OpenACC
    • Parallel Programming Models

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