The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed /visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU’s and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.
Bethel, EW, et al. "In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report." Computer Graphics Forum, vol. 35, no. 3, Jun. 2016. https://doi.org/10.1111/cgf.12930
Bethel, EW, Bauer, A, Abbasi, H, et al., "In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report," Computer Graphics Forum 35, no. 3 (2016), https://doi.org/10.1111/cgf.12930
@article{osti_1439995,
author = {Bethel, EW and Bauer, A and Abbasi, H and Ahrens, J and Childs, H and Geveci, B and Klasky, S and Moreland, K and O'Leary, P and Vishwanath, V and others},
title = {In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report},
annote = {The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed /visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU’s and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitioners using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.},
doi = {10.1111/cgf.12930},
url = {https://www.osti.gov/biblio/1439995},
journal = {Computer Graphics Forum},
issn = {ISSN 0167-7055},
number = {3},
volume = {35},
place = {United States},
publisher = {Wiley},
year = {2016},
month = {06}}
Kitware, Inc., Clifton Park, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
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