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<p>Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators. Traditionally, many simulation codes have relied on central processing unit (CPU)-based methods, tracking on a single CPU core, or parallelizing the computation across multiple cores via the message passing interface (MPI). Although these approaches work well for single-bunch tracking, scaling them to multiple bunches significantly increases the computational load, which often necessitates the use of a dedicated multi-CPU cluster. To address this challenge, alternative methods leveraging General-Purpose computing on Graphics Processing Units (GPGPU) have been proposed, enabling tracking studies on a standalone desktop personal computer (PC). However, frequent CPU-GPU interactions, including data transfers and synchronization operations during tracking, can introduce communication overheads, potentially reducing the overall effectiveness of GPU-based computations. In this study, we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU, thereby enabling the simultaneous processing of all bunches and their macro-particles. Specifically, we introduce MBTRACK2-CUDA, a Compute Unified Device Architecture (CUDA) ported version of MBTRACK2, which facilitates efficient tracking of single- and multi-bunch collective effects by leveraging the full GPU-resident computation.</p>

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CUDA-based GPU-only computation for efficient tracking simulation of single and multi-bunch collective effects

  • 期刊论文

  • 发表日期：

    2025年12月6日

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  • Keon Hee Kim,
  • Eun-San Kim

摘要

Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators. Traditionally, many simulation codes have relied on central processing unit (CPU)-based methods, tracking on a single CPU core, or parallelizing the computation across multiple cores via the message passing interface (MPI). Although these approaches work well for single-bunch tracking, scaling them to multiple bunches significantly increases the computational load, which often necessitates the use of a dedicated multi-CPU cluster. To address this challenge, alternative methods leveraging General-Purpose computing on Graphics Processing Units (GPGPU) have been proposed, enabling tracking studies on a standalone desktop personal computer (PC). However, frequent CPU-GPU interactions, including data transfers and synchronization operations during tracking, can introduce communication overheads, potentially reducing the overall effectiveness of GPU-based computations. In this study, we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU, thereby enabling the simultaneous processing of all bunches and their macro-particles. Specifically, we introduce MBTRACK2-CUDA, a Compute Unified Device Architecture (CUDA) ported version of MBTRACK2, which facilitates efficient tracking of single- and multi-bunch collective effects by leveraging the full GPU-resident computation.