While RISC-V-based accelerators were initially designed with artificial intelligence applications in mind, they are increasingly being recognized as promising platforms for high performance scientific computing. In this work, we present three strategies for scaling an $N$-body code across multiple Tenstorrent Wormhole accelerators based on the RISC-V architecture. We assess the performance of these approaches by measuring both the execution time and the energy consumption required to complete a representative simulation, ultimately identifying the configuration that offers the most favorable balance between efficiency and performance.

翻译：虽然基于RISC-V的加速器最初是为人工智能应用而设计的，但它们正日益被视为高性能科学计算的有前景平台。本文提出了三种策略，用于在基于RISC-V架构的多个Tenstorrent Wormhole加速器上扩展$N$体代码。我们通过测量完成一次代表性模拟所需的执行时间和能耗来评估这些方法的性能，最终识别出在效率与性能之间达到最有利平衡的配置。