TY - GEN
T1 - Implementing a GPU-Portable Field Line Tracing Application with OpenMP Offload
AU - Jiménez, Diego
AU - Herrera-Mora, Javier
AU - Rampp, Markus
AU - Laure, Erwin
AU - Meneses, Esteban
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Accelerated computing is becoming more diverse as new vendors and architectures come into play. Although platform-specific programming models promise ease of development and better control over performance, they still restrict the portability of scientific applications. As the OpenMP offloading specification becomes adopted by more compilers, this programming model stands out as a vendor-neutral portable approach to heterogeneous programming. In this study, we port a plasma physics oriented field line tracing code from a CPU-based MPI+OpenMP approach to a GPU accelerated version, using OpenMP’s offloading capabilities. We analyze GPU performance across different vendors with respect to the original CPU version and test both prescriptive and descriptive approaches to accelerator programming. A maximum acceleration over the CPU implementation was achieved using OpenMP’s high-level offloading directives. In addition, we demonstrate portability across three different vendor GPUs with no code modifications.
AB - Accelerated computing is becoming more diverse as new vendors and architectures come into play. Although platform-specific programming models promise ease of development and better control over performance, they still restrict the portability of scientific applications. As the OpenMP offloading specification becomes adopted by more compilers, this programming model stands out as a vendor-neutral portable approach to heterogeneous programming. In this study, we port a plasma physics oriented field line tracing code from a CPU-based MPI+OpenMP approach to a GPU accelerated version, using OpenMP’s offloading capabilities. We analyze GPU performance across different vendors with respect to the original CPU version and test both prescriptive and descriptive approaches to accelerator programming. A maximum acceleration over the CPU implementation was achieved using OpenMP’s high-level offloading directives. In addition, we demonstrate portability across three different vendor GPUs with no code modifications.
KW - Computational plasma physics
KW - High performance computing
KW - OpenMP GPU offload
UR - http://www.scopus.com/inward/record.url?scp=85146697077&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23821-5_3
DO - 10.1007/978-3-031-23821-5_3
M3 - Contribución a la conferencia
AN - SCOPUS:85146697077
SN - 9783031238208
T3 - Communications in Computer and Information Science
SP - 31
EP - 46
BT - High Performance Computing - 9th Latin American Conference, CARLA 2022, Revised Selected Papers
A2 - Navaux, Philippe
A2 - Barrios H., Carlos J.
A2 - Osthoff, Carla
A2 - Guerrero, Ginés
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Latin American High Performance Computing Conference, CARLA 2022
Y2 - 26 September 2022 through 30 September 2022
ER -