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John Biddiscombe

Publications and source records attributed to John Biddiscombe.

3 recordsLinked to original sources

OPALX: A performance-portable framework for particle accelerator simulations

Accurate charged-particle beam simulations require self-consistent collective fields and realistic accelerator models, while their computational cost motivates efficient use of heterogeneous computing systems. We present OPALX, a performance-portable accelerator simulation framework that builds on the beam-dynamics capabilities of OPAL. Its modular architecture uses the Independent Parallel Particle Layer (IPPL) and Kokkos to support distributed-memory execution on multicore CPUs and GPUs from a common codebase. The framework combines relativistic particle tracking with prescribed accelerator fields, particle-in-cell space charge calculations, and spin tracking. We describe the physical and numerical models and their integration into the simulation workflow. The implementation is assessed using disorder-induced heating and frozen-spin test cases, together with comparisons of spin evolution with G4Beamline and beam transport with TraceWin. A three-dimensional space charge benchmark evaluates throughput on CPU and GPU systems, reaching approximately $10^9$ macroparticles per second on a single NVIDIA A100 GPU for this benchmark. These results provide initial verification of the implemented models and demonstrate CPU and GPU execution of self-consistent space charge calculations. OPALX provides a common foundation for extending accelerator models and numerical methods across heterogeneous computing platforms.

physics.acc-ph↗

HPX -- An open source C++ Standard Library for Parallelism and Concurrency

To achieve scalability with today's heterogeneous HPC resources, we need a dramatic shift in our thinking; MPI+X is not enough. Asynchronous Many Task (AMT) runtime systems break down the global barriers imposed by the Bulk Synchronous Programming model. HPX is an open-source, C++ Standards compliant AMT runtime system that is developed by a diverse international community of collaborators called The Ste||ar Group. HPX provides features which allow application developers to naturally use key design patterns, such as overlapping communication and computation, decentralizing of control flow, oversubscribing execution resources and sending work to data instead of data to work. The Ste||ar Group comprises physicists, engineers, and computer scientists; men and women from many different institutions and affiliations, and over a dozen different countries. We are committed to advancing the development of scalable parallel applications by providing a platform for collaborating and exchanging ideas. In this paper, we give a detailed description of the features HPX provides and how they help achieve scalability and programmability, a list of applications of HPX including two large NSF funded collaborations (STORM, for storm surge forecasting; and STAR (OctoTiger) an astro-physics project which runs at 96.8% parallel efficiency on 643,280 cores), and we end with a description of how HPX and the Ste||ar Group fit into the open source community.

cs.DC↗

From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions

We study the simulation of stellar mergers, which requires complex simulations with high computational demands. We have developed Octo-Tiger, a finite volume grid-based hydrodynamics simulation code with Adaptive Mesh Refinement which is unique in conserving both linear and angular momentum to machine precision. To face the challenge of increasingly complex, diverse, and heterogeneous HPC systems, Octo-Tiger relies on high-level programming abstractions. We use HPX with its futurization capabilities to ensure scalability both between nodes and within, and present first results replacing MPI with libfabric achieving up to a 2.8x speedup. We extend Octo-Tiger to heterogeneous GPU-accelerated supercomputers, demonstrating node-level performance and portability. We show scalability up to full system runs on Piz Daint. For the scenario's maximum resolution, the compute-critical parts (hydrodynamics and gravity) achieve 68.1% parallel efficiency at 2048 nodes.

cs.DC↗