SCHEDULE: NOV 10-16, 2012
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Collective Tuning: Novel Extensible Methodology, Framework and Public Repository to Collaboratively Address Exascale Challenges
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Grigori Fursin
ROOM:East Entrance
ABSTRACT:
Designing and optimizing novel computing systems became intolerably complex, ad-hoc, costly and error prone due to an unprecedented number of available tuning choices, and complex interactions between all software and hardware components. In this poster, we present a novel methodology, extensible infrastructure and public repository to overcome the rising complexity of computer systems by distributing their characterization and optimization among multiple users. Our technology effectively combines auto-tuning, run-time adaptation, data mining and predictive modeling to collaboratively analyze thousands of codelets and datasets, explore large optimization spaces and detect abnormal behavior. It extrapolates collected knowledge to suggest program optimizations, run-time adaptation scenarios or architecture designs to balance performance, power consumption and other characteristics. This technology has been recently successfully validated and extended in several academic and industrial projects with NCAR, Intel Exascale Lab, Google, IBM and CAPS Entreprise and we believe that it will be vital for developing future Exascale systems.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Grigori Fursin - INRIA
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Collective Tuning: Novel Extensible Methodology, Framework and Public Repository to Collaboratively Address Exascale Challenges
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Grigori Fursin
ROOM:East Entrance
ABSTRACT:
Designing and optimizing novel computing systems became intolerably complex, ad-hoc, costly and error prone due to an unprecedented number of available tuning choices, and complex interactions between all software and hardware components. In this poster, we present a novel methodology, extensible infrastructure and public repository to overcome the rising complexity of computer systems by distributing their characterization and optimization among multiple users. Our technology effectively combines auto-tuning, run-time adaptation, data mining and predictive modeling to collaboratively analyze thousands of codelets and datasets, explore large optimization spaces and detect abnormal behavior. It extrapolates collected knowledge to suggest program optimizations, run-time adaptation scenarios or architecture designs to balance performance, power consumption and other characteristics. This technology has been recently successfully validated and extended in several academic and industrial projects with NCAR, Intel Exascale Lab, Google, IBM and CAPS Entreprise and we believe that it will be vital for developing future Exascale systems.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Grigori Fursin - INRIA
Click here to download .ics calendar file