Published April 12, 2017
| Version v1
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Supplementary Materials for "On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective"
Authors/Creators
- 1. Helmholtz-Zentrum Dresden – Rossendorf, Technische Universität Dresden
- 2. Helmholtz-Zentrum Dresden – Rossendorf
- 3. NVIDIA ARC GmbH, Technische Universität Dresden
- 4. Oak Ridge National Laboratory
Description
Supplementary materials with all used benchmark scripts, plot scripts, benchmark results and PIConGPU example data for the submission to "The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)" held in conjunction with ISC 2017 in Frankfurt, Germany.
Notes
This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
Files
results.zip
Additional details
Related works
- Has part
- 10.5281/zenodo.1000736 (DOI)
- Is referenced by
- 10.1007/978-3-319-67630-2_2 (DOI)
References
- Axel Huebl, René Widera, Felix Schmitt, Alexander Matthes, Norbert Podhorszki, Jong Youl Choi, Scott Klasky, and Michael Bussmann (2017). On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective, DOI:10.1007/978-3-319-67630-2_2