NUMA-aware graph mining techniques for performance and energy efficiency
NUMA-aware graph mining techniques for performance and energy efficiency
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用于提高性能和能源效率的 NUMA 感知图挖掘技术
DOI：
10.5555/2388996.2389125
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
P. Raghavan
中科院分区：
文献类型：
--
作者：
Michael R. Frasca;Kamesh Madduri;P. Raghavan
我们研究了动态方法来改善现代多核系统上大型不规则应用程序的功率和性能配置。在此背景下，我们研究了一个大型稀疏图应用程序--居间中心性，并重点研究了随着核计数的增加而产生的内存行为。我们引入了新的技术来有效地将计算需求映射到非一致存储体系结构(NUMA)上。我们的动态设计适应了硬件拓扑结构，显著提高了能量和性能。核心计数越高，这些收益就越显著。我们实现了一种自适应数据布局方案，它根据观察到的并行访问模式重新组织图，并实现了一个动态任务调度器，它鼓励相邻核之间共享数据。我们在现代多核机器上对性能和能耗进行了测试，观察到平均执行时间减少了51.2%，能量减少了52.4%。
We investigate dynamic methods to improve the power and performance profiles of large irregular applications on modern multi-core systems. In this context, we study a large sparse graph application, Betweenness Centrality, and focus on memory behavior as core count scales. We introduce new techniques to efficiently map the computational demands onto non-uniform memory architectures (NUMA). Our dynamic design adapts to hardware topology and dramatically improves both energy and performance. These gains are more significant at higher core counts. We implement a scheme for adaptive data layout, which reorganizes the graph after observing parallel access patterns, and a dynamic task scheduler that encourages shared data between neighboring cores. We measure performance and energy consumption on a modern multi-core machine and observe that mean execution time is reduced by 51.2% and energy is reduced by 52.4%.