Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations for Exascale Computing SystemsShow others and affiliations
2026 (English)In: 26th International Conference on Computational Science, Hamburg, Germany, 29 June - 1 July, 2026, Part I, LNCS 16783, Springer Nature, 2026, Vol. 16783, p. 32-47Conference paper, Published paper (Refereed)
Abstract [en]
Particle-in-Cell (PIC) Monte Carlo (MC) simulations are central to plasma physics but face increasing challenges on heterogeneous HPC systems due to excessive data movement, synchronization overheads, and inefficient utilization of multiple accelerators. In this work, we present a portable, multi-GPU hybrid MPI+OpenMP implementation of BIT1 that enables scalable execution on both Nvidia and AMD accelerators through OpenMP target tasks with explicit dependencies to overlap computation and communication across devices. Portability is achieved through persistent device-resident memory, an optimized contiguous one-dimensional data layout, and a transition from unified to pinned host memory to improve large data-transfer efficiency, together with GPU Direct Memory Access (DMA) and runtime interoperability for direct device-pointer access. Standardized and scalable I/O is provided using openPMD and ADIOS2, supporting high-performance file I/O, in-memory data streaming, and in-situ analysis and visualization. Performance results on pre-exascale and exascale systems, including Frontier (OLCF-5) for up to 16,000 GPUs, demonstrate significant improvements in run time, scalability, and resource utilization for large-scale PIC MC simulations.
Place, publisher, year, edition, pages
Springer Nature, 2026. Vol. 16783, p. 32-47
Keywords [en]
Heterogeneous Computing, Hybrid MPI+OpenMP, BIT1, Nvidia, AMD, Persistent GPU Memory, Asynchronous Multi-GPU Execution, Plasma Edge Modeling, Large-Scale PIC MC Simulations
National Category
Computer Systems Fusion, Plasma and Space Physics Networked, Parallel and Distributed Computing Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-378910DOI: 10.1007/978-3-032-29921-5_3Scopus ID: 2-s2.0-105043326505OAI: oai:DiVA.org:kth-378910DiVA, id: diva2:2049582
Conference
26th International Conference on Computational Science, Hamburg, Germany, 29 June - 1 July, 2026
Note
QC 20260330
2026-03-302026-03-302026-07-09Bibliographically approved