Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. Specialized heuristics developed as part of this work traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning. Finally we present a method that employs automated performance modeling to quickly predict application requirements for varying scales and problem sizes.
Following this approach, it is possible to determine future requirements of major scientific applications, derive an optimization strategy, and illustrate system design tradeoffs in the light of their requirements. This supports the co-design process by informing hardware acquisition decisions with the actual needs of the software.
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- On the parallel computation thesis | Logic Journal of the IGPL | Oxford Academic.
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The methods described in this work are implemented in the performance analysis tool Extra-P. Extra-P has been released as open source and has been successfully used to gain insight into the performance of numerous scientific applications from a large range of fields.
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Developers at both universities and research centers have used Extra-P to better understand the performance of their research codes. Tutorials on the use of Extra-P have been offered at international conferences such as EuroMPI and Supercomputing further demonstrating the effectiveness of this approach in making performance modeling available to developers without requiring expert knowledge of the topic.
Automatic Parallelization of Data-Driven JStar Programs
This work simplifies and streamlines the performance modeling process, offering insights into application behavior quickly and automatically and allowing the developer to focus on transforming these insights into tangible performance improvements. Thesis], Preview.
X10: Performance and Productivity at Scale
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Verification of program parallelization — University of Twente Research Information
Privacy Copyright. Skip to main content. FIU Digital Commons. Title Data-parallel programming with multiple inheritance on the connection machine.
Parallel computation thesis
Abstract The demand for computers is oriented toward faster computers and newer computers are being built with more than one CPU. Recommended Citation Girimaji, Sanjay, "Data-parallel programming with multiple inheritance on the connection machine" Included in Computer Sciences Commons.
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