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首页> 外文期刊>Journal of Parallel and Distributed Computing >Optimizing the steady-state throughput of scatter and reduce operations on heterogeneous platforms
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Optimizing the steady-state throughput of scatter and reduce operations on heterogeneous platforms

机译：优化分散的稳态吞吐量并减少异构平台上的操作

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摘要

In this paper, we consider the communications involved by the execution of a complex application, deployed on a heterogeneous large-scale distributed platform. Such applications intensively use collective macro-communication schemes, such as scatters, personalized all-to-alls or gather/reduce operations. Rather than aiming at minimizing the execution time of a single macro-communication, we focus on the steady-state operation. We assume that there is a large number of macro-communications to perform in pipeline fashion, and we aim at maximizing the throughput, i.e., the (rational) number of macro-communications which can be initiated every time-step. We target heterogeneous platforms, modeled by a graph where resources have different communication and computation speeds. The situation is simpler for series of scatters or personalized all-to-alls than for series of reduces operations, because of the possibility of combining various partial reductions of the local values, and of interleaving computations with communications. In all cases, we show how to determine the optimal throughput, and how to exhibit a concrete periodic schedule that achieves this throughput.
机译：在本文中，我们考虑了部署在异构大型分布式平台上的复杂应用程序执行所涉及的通信。这样的应用程序集中使用集体宏通信方案，例如散布，个性化的全部到所有人或收集/减少操作。与其着眼于最小化单个宏通信的执行时间，我们关注的是稳态操作。我们假设有大量的宏通信以流水线方式执行，并且我们的目标是使吞吐量最大化，即可以在每个时间步启动的（合理的）数量的宏通信。我们针对异构平台，以图建模，其中资源具有不同的通信和计算速度。对于一系列散​​点图或个性化的全包数据，与一系列归约操作相比，这种情况更为简单，因为可以将局部值的各种部分归约组合在一起，并且可以将计算与通信进行交织。在所有情况下，我们都将展示如何确定最佳吞吐量，以及如何展示实现此吞吐量的具体定期计划。

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