BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121115T210000Z
DTEND:20121115T213000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE: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.=0A=0AWe 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%.
SUMMARY:NUMA-Aware Graph Mining Techniques for Performance and Energy Efficiency
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121115T210000Z
DTEND:20121115T213000Z
LOCATION:255-BC
DESCRIPTION;ENCODING=QUOTED-PRINTABLE: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.=0A=0AWe 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%.
SUMMARY:NUMA-Aware Graph Mining Techniques for Performance and Energy Efficiency
PRIORITY:3
END:VEVENT
END:VCALENDAR