Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation

Handle URI:
http://hdl.handle.net/10754/597699
Title:
Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation
Authors:
Murarasu, Alin; Weidendorfer, Josef
Abstract:
The well-known power wall resulting in multi-cores requires special techniques for speeding up applications. In this sense, parallelization plays a crucial role. Besides standard serial optimizations, techniques such as input specialization can also bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation is an inherently hierarchical method of interpolation employed for example in computational steering applications for decompressing highdimensional simulation data. In this context, improving the speedup is essential for real-time visualization. Using input specialization, we report a speedup of up to 9x over the nonspecialized version. The paper covers the steps we took to reach this speedup by means of input adaptivity. Our algorithms will be integrated in fastsg, a library for fast sparse grid interpolation. © 2012 IEEE.
Citation:
Murarasu A, Weidendorfer J (2012) Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation. 2012 IEEE 15th International Conference on Computational Science and Engineering. Available: http://dx.doi.org/10.1109/ICCSE.2012.11.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 IEEE 15th International Conference on Computational Science and Engineering
KAUST Grant Number:
UK-C0020
Issue Date:
Dec-2012
DOI:
10.1109/ICCSE.2012.11
Type:
Conference Paper
Sponsors:
This publication is based on work supported by Award No.UK-C0020, made by King Abdullah University of Science andTechnology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMurarasu, Alinen
dc.contributor.authorWeidendorfer, Josefen
dc.date.accessioned2016-02-25T12:44:39Zen
dc.date.available2016-02-25T12:44:39Zen
dc.date.issued2012-12en
dc.identifier.citationMurarasu A, Weidendorfer J (2012) Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation. 2012 IEEE 15th International Conference on Computational Science and Engineering. Available: http://dx.doi.org/10.1109/ICCSE.2012.11.en
dc.identifier.doi10.1109/ICCSE.2012.11en
dc.identifier.urihttp://hdl.handle.net/10754/597699en
dc.description.abstractThe well-known power wall resulting in multi-cores requires special techniques for speeding up applications. In this sense, parallelization plays a crucial role. Besides standard serial optimizations, techniques such as input specialization can also bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation is an inherently hierarchical method of interpolation employed for example in computational steering applications for decompressing highdimensional simulation data. In this context, improving the speedup is essential for real-time visualization. Using input specialization, we report a speedup of up to 9x over the nonspecialized version. The paper covers the steps we took to reach this speedup by means of input adaptivity. Our algorithms will be integrated in fastsg, a library for fast sparse grid interpolation. © 2012 IEEE.en
dc.description.sponsorshipThis publication is based on work supported by Award No.UK-C0020, made by King Abdullah University of Science andTechnology (KAUST).en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleBuilding Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolationen
dc.typeConference Paperen
dc.identifier.journal2012 IEEE 15th International Conference on Computational Science and Engineeringen
dc.contributor.institutionTechnische Universitat Munchen, Munich, Germanyen
kaust.grant.numberUK-C0020en
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