Treffer: sKokkos: enabling Kokkos with transparent device selection on heterogeneous systems using OpenACC

Title:
sKokkos: enabling Kokkos with transparent device selection on heterogeneous systems using OpenACC
Contributors:
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. PM - Programming Models
Publisher Information:
Association for Computing Machinery (ACM)
Publication Year:
2024
Collection:
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Document Type:
Konferenz conference object
File Description:
12 p.; application/pdf
Language:
English
DOI:
10.1145/3635035.3635043
Rights:
Open Access
Accession Number:
edsbas.C81C5E60
Database:
BASE

Weitere Informationen

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. ; 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-SC20-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 ...