TRAM: Optimizing Fine-Grained Communication with Topological Routing and Aggregation of Messages
TRAM: Optimizing Fine-Grained Communication with Topological Routing and Aggregation of Messages
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TRAM：通过拓扑路由和消息聚合来优化细粒度通信
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发表时间：
2014
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通讯作者：
L. Kalé
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作者：
Lukasz Wesolowski;Ramprasad Venkataraman;Abhishek K. Gupta;Jae;K. Bisset;Yanhua Sun;Pritish Jetley;T. Quinn;L. Kalé
在超级计算应用中，细粒度通信通常会因通信开销高和网络带宽利用率低而限制性能。提出了一种拓扑路由聚合模块(TRAM)，它通过对短消息进行路由和动态组合来优化细粒度通信性能。TRAM从应用程序收集细粒度通信单元，并将它们组合成具有公共中间目的地的聚合消息。它沿着映射到网络的物理拓扑上的虚拟网状拓扑来路由这些消息。TRAM提高了网络带宽利用率，降低了通信开销。它在优化具有全球通信和大消息数量的模式方面特别有效，例如多对多和多对多，以及稀疏、不规则、动态或数据相关的模式。我们通过使用基准测试和科学应用的理论分析和实验验证，展示了TRAM如何提高性能。我们给出了在PB级系统上通信基准的加速比为6倍，应用的加速比高达4倍。
Fine-grained communication in supercomputing applications often limits performance through high communication overhead and poor utilization of network bandwidth. This paper presents Topological Routing and Aggregation Module (TRAM), a library that optimizes fine-grained communication performance by routing and dynamically combining short messages. TRAM collects units of fine-grained communication from the application and combines them into aggregated messages with a common intermediate destination. It routes these messages along a virtual mesh topology mapped onto the physical topology of the network. TRAM improves network bandwidth utilization and reduces communication overhead. It is particularly effective in optimizing patterns with global communication and large message counts, such as all-to-all and many-to-many, as well as sparse, irregular, dynamic or data dependent patterns. We demonstrate how TRAM improves performance through theoretical analysis and experimental verification using benchmarks and scientific applications. We present speedups on petascale systems of 6x for communication benchmarks and up to 4x for applications.