TY - GEN
T1 - Understanding and improving the trust in results of numerical simulations and scientific data analytics
AU - Cappello, Franck
AU - Gupta, Rinku
AU - Di, Sheng
AU - Constantinescu, Emil
AU - Peterka, Thomas
AU - Wild, Stefan M.
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - With ever-increasing execution scale of parallel scientific simulations, potential unnoticed corruptions to scientific data during simulation make users more suspicious about the correctness of floating-point calculations than ever before. In this paper, we analyze the issue of the trust in results of numerical simulations and scientific data analytics. We first classify the corruptions into two categories, nonsystematic corruption and systematic corruption, and also discuss their origins. Then, we provide a formal definition of the trust in simulation and analytical results across multiple areas. We also discuss what kind of result accuracy would be expected from user’s perspective and how to build trust by existing techniques. We finally identify the current gap and discuss two potential research directions based on existing techniques. We believe that this paper will be interesting to the researchers who are working on the detection of potential unnoticed corruptions of scientific simulation and data analytics, in that not only does it provide a clear definition and classification of corruption as well as an in-depth survey on corruption sources, but we also discuss potential research directions/topics based on existing detection techniques.
AB - With ever-increasing execution scale of parallel scientific simulations, potential unnoticed corruptions to scientific data during simulation make users more suspicious about the correctness of floating-point calculations than ever before. In this paper, we analyze the issue of the trust in results of numerical simulations and scientific data analytics. We first classify the corruptions into two categories, nonsystematic corruption and systematic corruption, and also discuss their origins. Then, we provide a formal definition of the trust in simulation and analytical results across multiple areas. We also discuss what kind of result accuracy would be expected from user’s perspective and how to build trust by existing techniques. We finally identify the current gap and discuss two potential research directions based on existing techniques. We believe that this paper will be interesting to the researchers who are working on the detection of potential unnoticed corruptions of scientific simulation and data analytics, in that not only does it provide a clear definition and classification of corruption as well as an in-depth survey on corruption sources, but we also discuss potential research directions/topics based on existing detection techniques.
KW - Data analytics
KW - Numerical simulation
KW - Trust
UR - http://www.scopus.com/inward/record.url?scp=85042463798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042463798&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75178-8_44
DO - 10.1007/978-3-319-75178-8_44
M3 - Conference contribution
AN - SCOPUS:85042463798
SN - 9783319751771
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 545
EP - 556
BT - Euro-Par 2017
A2 - Heras, Dora B.
A2 - Bouge, Luc
PB - Springer
T2 - International Workshops on Parallel Processing, Euro-Par 2017
Y2 - 28 August 2017 through 29 August 2017
ER -