SCHEDULE: NOV 10-16, 2012
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NUMA-Aware Graph Mining Techniques for Performance and Energy Efficiency
SESSION: Performance Optimization
EVENT TYPE: Papers
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Padma Raghavan
AUTHOR(S):Michael R. Frasca, Kamesh Madduri, Padma Raghavan
ROOM:255-BC
ABSTRACT:
We investigate dynamic methods to improve power and performance 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%.
Chair/Author Details:
Padma Raghavan (Chair) - Pennsylvania State University
Michael R. Frasca - Pennsylvania State University
Kamesh Madduri - Pennsylvania State University
Padma Raghavan - Pennsylvania State University
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NUMA-Aware Graph Mining Techniques for Performance and Energy Efficiency
SESSION: Performance Optimization
EVENT TYPE:
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Padma Raghavan
AUTHOR(S):Michael R. Frasca, Kamesh Madduri, Padma Raghavan
ROOM:255-BC
ABSTRACT:
We investigate dynamic methods to improve power and performance 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%.
Chair/Author Details:
Padma Raghavan (Chair) - Pennsylvania State University
Michael R. Frasca - Pennsylvania State University
Kamesh Madduri - Pennsylvania State University
Padma Raghavan - Pennsylvania State University
Click here to download .ics calendar file