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EagerMap: A Task Mapping Algorithm to Improve Communication and Load Balancing in Clusters of Multicore Systems

机译:EagerMap:一种任务映射算法,用于改善多核系统集群中的通信和负载平衡

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Communication between tasks and load imbalance have been identified as a major challenge for the performance and energy efficiency of parallel applications. A common way to improve communication is to increase its locality, that is, to reduce the distances of data transfers, prioritizing the usage of faster and more efficient local interconnections over remote ones. Regarding load imbalance, cores should execute a similar amount of work. An important problem to be solved in this context is how to determine an optimized mapping of tasks to cluster nodes and cores that increases the overall locality and load balancing. In this article, we propose the EagerMap algorithm to determine task mappings, which is based on a greedy heuristic to match application communication patterns to hardware hierarchies and which can also consider the task load. Compared to previous algorithms, EagerMap is faster, scales better, and supports more types of computer systems, while maintaining the same or better quality of the determined task mapping. EagerMap is therefore an interesting choice for task mapping on a variety of modern parallel architectures.
机译:任务和负载不平衡之间的通信已被确定为并行应用程序的性能和能效的主要挑战。改善通信的一种常见方法是增加其本地性,即减少数据传输的距离,优先使用更快,更有效的本地互连,而不是远程互连。关于负载不平衡,核心应执行类似的工作量。在这种情况下要解决的一个重要问题是如何确定任务到群集节点和核心的最佳映射,从而增加整体位置和负载平衡。在本文中,我们提出了一种EagerMap算法来确定任务映射,该算法基于贪婪启发式算法,以将应用程序通信模式与硬件层次结构进行匹配,并且还可以考虑任务负载。与以前的算法相比,EagerMap更快,可扩展性更好,并且支持更多类型的计算机系统,同时保持确定的任务映射的质量相同或更好。因此,EagerMap是在各种现代并行体系结构上进行任务映射的有趣选择。

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