NUMA-aware graph mining techniques for performance and energy efficiency

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%.