一种将通信操作有效卸载到Bluefield smartnic的新框架

K. Suresh, Benjamin Michalowicz, B. Ramesh, Nicholas Contini, Jinghan Yao, Shulei Xu, A. Shafi, H. Subramoni, D. Panda
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引用次数: 2

摘要

智能网络接口卡(smartnic),如NVIDIA的BlueField数据处理单元(dpu)提供先进的网络功能和处理器核心,使复杂的操作从主机上卸载。在MPI的背景下,之前的工作已经探索了使用dpu卸载非阻塞集体操作。当前最先进的方法有两个局限性:它们只适用于一组预定义的算法/通信模式,并且由于在DPU和主机之间暂存数据而降低了通信延迟。在本文中,我们提出了一个框架,该框架支持将任何通信模式卸载到DPU,同时实现低通信延迟和完美重叠。为了实现这一点,我们首先研究了高级编程模型(如MPI)在向DPU表达复杂通信模式的卸载方面的局限性。我们提供了一组新的api来减轻这些缺点,并支持任何通用的通信模式。然后,我们分析了将通信操作卸载到DPU所涉及的瓶颈,并提出了一些候选通信模式的有效设计。据我们所知,这是第一个为DPU提供高效和通用通信卸载的框架。我们提出的框架在Alltoall微基准测试中比最先进的基于阶段的卸载解决方案高出47%,在应用程序级别,我们看到P3DFFT的改进高达60%,512进程的HPL的改进高达15%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Framework for Efficient Offloading of Communication Operations to Bluefield SmartNICs
Smart Network Interface Cards (SmartNICs) such as NVIDIA’s BlueField Data Processing Units (DPUs) provide advanced networking capabilities and processor cores, enabling the offload of complex operations away from the host. In the context of MPI, prior work has explored the use of DPUs to offload non-blocking collective operations. The limitations of current state-of-the-art approaches are twofold: They only work for a pre-defined set of algorithms/communication patterns and have degraded communication latency due to staging data between the DPU and the host. In this paper, we propose a framework that supports the offload of any communication pattern to the DPU while achieving low communication latency with perfect overlap. To achieve this, we first study the limitations of higher-level programming models such as MPI in expressing the offload of complex communication patterns to the DPU. We present a new set of APIs to alleviate these shortcomings and support any generic communication pattern. Then, we analyze the bottlenecks involved in offloading communication operations to the DPU and propose efficient designs for a few candidate communication patterns. To the best of our knowledge, this is the first framework providing both efficient and generic communication offload to the DPU. Our proposed framework outperforms state-of-the-art staging-based offload solutions by 47% in Alltoall micro-benchmarks, and at the application level, we see improvements up to 60% in P3DFFT and 15% in HPL on 512 processes.
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