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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Recursive Hypergraph Bipartitioning Framework for Reducing Bandwidth and Latency Costs Simultaneously
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A Recursive Hypergraph Bipartitioning Framework for Reducing Bandwidth and Latency Costs Simultaneously

机译：同时减少带宽和延迟成本的递归超图双向划分框架

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摘要

Intelligent partitioning models are commonly used for efficient parallelization of irregular applications on distributed systems. These models usually aim to minimize a single communication cost metric, which is either related to communication volume or message count. However, both volume- and message-related metrics should be taken into account during partitioning for a more efficient parallelization. There are only a few works that consider both of them and they usually address each in separate phases of a two-phase approach. In this work, we propose a recursive hypergraph bipartitioning framework that reduces the total volume and total message count in a single phase. In this framework, the standard hypergraph models, nets of which already capture the bandwidth cost, are augmented with message nets. The message nets encode the message count so that minimizing conventional cutsize captures the minimization of bandwidth and latency costs together. Our model provides a more accurate representation of the overall communication cost by incorporating both the bandwidth and the latency components into the partitioning objective. The use of the widely-adopted successful recursive bipartitioning framework provides the flexibility of using any existing hypergraph partitioner. The experiments on instances from different domains show that our model on the average achieves up to 52 percent reduction in total message count and hence results in 29 percent reduction in parallel running time compared to the model that considers only the total volume.
机译：智能分区模型通常用于在分布式系统上对不规则应用程序进行有效的并行化。这些模型通常旨在最小化与通信量或消息数量有关的单个通信成本度量。但是，在进行分区时，应同时考虑与卷和邮件相关的指标，以实现更有效的并行化。只有很少的作品同时考虑到了两者，并且它们通常在两阶段方法的不同阶段中进行处理。在这项工作中，我们提出了一个递归超图双向划分框架，该框架可减少单个阶段的总数量和总消息数。在此框架中，标准超图模型（已捕获带宽成本的网络）将通过消息网进行扩展。消息网络对消息计数进行编码，以便使常规的cutsize最小化可以将带宽和延迟成本的最小化结合在一起。我们的模型通过将带宽和延迟组件都纳入分区目标中，从而提供了更准确的整体通信成本表示。广泛采用的成功递归双向划分框架的使用提供了使用任何现有超图分区器的灵活性。对来自不同域的实例进行的实验表明，与仅考虑总容量的模型相比，我们的模型平均将总邮件数减少了52％，因此并行运行时间减少了29％。

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