砾石:细粒度gpu发起的网络消息

Marc S. Orr, Shuai Che, Bradford M. Beckmann, M. Oskin, S. Reinhardt, D. Wood
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引用次数: 6

摘要

分布式系统采用gpu是因为它们以一种节能的方式提供了大量的并行性。不幸的是,现有的编程模型很难路由由gpu发起的网络消息。传统的协处理器模型迫使程序员手动通过主机CPU路由消息。其他模型允许gpu发起通信,但对于小消息来说效率低下。为了在不同gpu上执行的线程之间实现细粒度pgas风格的通信,我们引入了砾石。gpu发起的消息通过gpu高效并发队列卸载到聚合器(使用CPU线程实现),聚合器将目标指向相同目的地的消息组合在一起。砾石利用分散的工作组级语义来分摊GPU的数据并行通道的同步。使用砾石,我们可以在8个gpu加速节点的集群上分发6个应用程序,每个应用程序都有频繁的小消息。与单个节点相比,这些应用程序的运行速度平均快5.3倍。此外,我们表明砾石是更可编程的,通常比以前的GPU网络模型表现更好。计算机方法→大规模并行算法;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gravel: Fine-Grain GPU-Initiated Network Messages
Distributed systems incorporate GPUs because they provide massive parallelism in an energy-efficient manner. Unfortunately, existing programming models make it difficult to route a GPU-initiated network message. The traditional coprocessor model forces programmers to manually route messages through the host CPU. Other models allow GPU-initiated communication, but are inefficient for small messages. To enable fine-grain PGAS-style communication between threads executing on different GPUs, we introduce Gravel. GPU-initiated messages are offloaded through a GPU-efficient concurrent queue to an aggregator (implemented with CPU threads), which combines messages targeting to the same destination. Gravel leverages diverged work-group-level semantics to amortize synchronization across the GPU’s data-parallel lanes. Using Gravel, we can distribute six applications, each with frequent small messages, across a cluster of eight GPU-accelerated nodes. Compared to one node, these applications run 5.3x faster, on average. Furthermore, we show Gravel is more programmable and usually performs better than prior GPU networking models. CCS CONCEPTS Computer methodologies→Massively parallel algorithms;
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