BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
BEGIN:VEVENT
DTSTART:20141118T231500Z
DTEND:20141119T010000Z
LOCATION:New Orleans Theater Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: The performance and energy optimization of the 13 ``dwarfs'', proposed by UC-Berkeley in 2006, can have a tremendous impact on a vast number of scientific applications and existing computational libraries. To achieve this goal, scientists and software engineers need tools for=0Aanalyzing and modeling the performance-power-energy interactions of their kernels on real HPC systems.=0A=0AIn this poster we present systematic methods to derive reliable time-power-energy models for dense and sparse linear algebra operations. Our strategy is based on decomposing the kernels into sub-components (e.g., arithmetics and memory accesses) and identifying the critical features=0Athat drive their performance, power, and energy consumption. The proposed techniques provide tools for analyzing and reengineering algorithms for the desired power- and energy-efficiency as well as to reduce operational costs of HPC-supercomputers and cloud-systems with thousands of=0Aconcurrent users.
SUMMARY:Machine Learning Algorithms for the Performance and Energy-Aware Characterization of Linear Algebra Kernels on Multithreaded Architectures
PRIORITY:3
END:VEVENT
END:VCALENDAR