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Inhalt des Dokuments

Starbench parallel benchmark suite

Description

In recent years a multitude of parallel programming models have been introduced to ease parallel programming. Each programming model brings its own concepts and semantics, which makes it hard to see their impact on performance. Starbench is a benchmark suite that allows comparing different parallel programming models for embedded and consumer applications. Starbench consist of C/C++ benchmarks and currently covers video coding, image compression, image processing, hashing, artificial intelligence, computer vision, and compression. For each of the benchmark an optimized Pthreads version has been developed to serve as baseline. The suite has been succesfully used to evaluate the versatility and efficiency of OmpSs, a task-based programming model developed in the Encore project. While the main target is to provide a means to evaluate programming models and runtime improvements for E&C applications, Starbench and/or its individual benchmarks are also very suitable for other field of research such as computer architecture which require state-of-the-art parallel applications.

If you have any questions regarding Starbench, please write a mail to: .

Download

The Starbench source can be downloaded here (GZ, 630,1 KB).

The Starbench input can be downloaded here. (GZ, 1,006,7 MB)

People invovled

  • Michael Andersch
  • Chi Ching Chi
  • Prof. Dr. Ben Juurlink

Funding

This project receives funding from the European Community's Seventh Framework Programme [FP7/2007-2013] under the ENCORE Project (www.encore-project.eu), grant agreement n° 248647.

Publications

Michael Andersch and Ben Juurlink and Chi Ching Chi (2011). A Benchmark Suite for Evaluating Parallel Programming Models. Proceedings 24th Workshop on Parallel Systems and Algorithms


Michael Andersch and Chi Ching Chi and Ben Juurlink (2012). Programming Parallel Embedded and Consumer Applications in OpenMP Superscalar. Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming. ACM, 281–282.


Michael Andersch and Chi Ching Chi and Ben Juurlink (2012). Using OpenMP Superscalar for Parallelization of Embedded and Consumer Applications. Proceedings of the International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation


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