BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T001500Z
DTEND:20121114T020000Z
LOCATION:East Entrance
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases an order of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine usability, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi).=0A=0AWe combine hardware performance counter data with machine learning and advanced analytics to create a power-performance efficiency model for modern GPU-based systems. Our performance counter based approach is general and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture).
SUMMARY:Three Steps to Model Power-Performance Efficiency for Emergent GPU-Based Parallel Systems
PRIORITY:3
END:VEVENT
END:VCALENDAR
BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20121114T001500Z
DTEND:20121114T020000Z
LOCATION:East Entrance
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases an order of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine usability, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi).=0A=0AWe combine hardware performance counter data with machine learning and advanced analytics to create a power-performance efficiency model for modern GPU-based systems. Our performance counter based approach is general and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture).
SUMMARY:Three Steps to Model Power-Performance Efficiency for Emergent GPU-Based Parallel Systems
PRIORITY:3
END:VEVENT
END:VCALENDAR