Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can benefit from in-network computing: scientific workloads in high-performance computing. By analyzing canonical examples of HPC applications, we observe unique opportunities and challenges for exploiting in-network computing to accelerate scientific workloads. In particular, we find that the dynamic and demanding nature of scientific workloads is the major obstacle to the adoption of in-network approaches which are mostly open-loop and lack runtime feedback. In this paper, we present NSinC (Network-accelerated ScIeNtific Computing), an architecture for fully unleashing the potential benefits of in-network computing for scientific workloads by providing closed-loop runtime feedback to in-network acceleration services. We outline key challenges in realizing this vision and a preliminary design to enable acceleration for scientific applications.

中文翻译：

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在科学工作负载上释放网络内计算

最近的许多努力表明，网络内计算可以使各种数据中心应用程序受益。在本文中，我们探索了一个相对较少探索的领域，我们认为它可以从网络内计算中受益：高性能计算中的科学工作负载。通过分析 HPC 应用程序的规范示例，我们观察到利用网络内计算来加速科学工作负载的独特机遇和挑战。特别是，我们发现科学工作负载的动态和苛刻的性质是采用网络内方法的主要障碍，这些方法大多是开环的，缺乏运行时反馈。在本文中，我们介绍了 NSinC（网络加速科学计算），一种架构，通过向网络内加速服务提供闭环运行时反馈，充分释放网络内计算对科学工作负载的潜在优势。我们概述了实现这一愿景的关键挑战和初步设计，以加速科学应用。