Optimal scheduling of in-situ analysis for large-scale scientific simulations

Today's leadership computing facilities have enabled the execution of transformative simulations at unprecedented scales. However, analyzing the huge amount of output from these simulations remains a challenge. Most analyses of this output is performed in post-processing mode at the end of the...

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Vydané v:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis s. 1 - 11
Hlavní autori: Malakar, Preeti, Vishwanath, Venkatram, Munson, Todd, Knight, Christopher, Hereld, Mark, Leyffer, Sven, Papka, Michael E.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 15.11.2015
Edícia:ACM Conferences
Predmet:
Analytical models
Bandwidth
Computational modeling
Computer systems organization > Dependable and fault-tolerant systems and networks
Computing methodologies > Modeling and simulation > Simulation types and techniques
Data models
Data visualization
General and reference > Cross-computing tools and techniques > Performance
Kernel
Networks > Network performance evaluation
Optimization
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
scheduling
analysis
in-situ
optimization
simulation
ISBN:1450337236, 9781450337236
ISSN:2167-4337
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Popis
Shrnutí:Today's leadership computing facilities have enabled the execution of transformative simulations at unprecedented scales. However, analyzing the huge amount of output from these simulations remains a challenge. Most analyses of this output is performed in post-processing mode at the end of the simulation. The time to read the output for the analysis can be significantly high due to poor I/O bandwidth, which increases the end-to-end simulation-analysis time. Simulation-time analysis can reduce this end-to-end time. In this work, we present the scheduling of in-situ analysis as a numerical optimization problem to maximize the number of online analyses subject to resource constraints such as I/O bandwidth, network bandwidth, rate of computation and available memory. We demonstrate the effectiveness of our approach through two application case studies on the IBM Blue Gene/Q system.
ISBN:1450337236
9781450337236
ISSN:2167-4337
DOI:10.1145/2807591.2807656