Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118288
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Type: Journal article
Title: Complex scientific applications made fault-tolerant with the sparse grid combination technique
Author: Ali, M.
Strazdins, P.
Harding, B.
Hegland, M.
Citation: International Journal of High Performance Computing Applications, 2016; 30(3):335-359
Publisher: SAGE Publications
Issue Date: 2016
ISSN: 1094-3420
1741-2846
Statement of
Responsibility: 
Md Mohsin Ali, Peter E Strazdins, Brendan Harding, Markus Hegland
Abstract: Ultra-large–scale simulations via solving partial differential equations (PDEs) require very large computational systems for their timely solution. Studies shown the rate of failure grows with the system size, and these trends are likely to worsen in future machines. Thus, as systems, and the problems solved on them, continue to grow, the ability to survive failures is becoming a critical aspect of algorithm development. The sparse grid combination technique (SGCT) which is a cost-effective method for solving higher dimensional PDEs can be easily modified to provide algorithm-based fault tolerance. In this article, we describe how the SGCT can produce fault-tolerant versions of the Gyrokinetic Electromagnetic Numerical Experiment plasma application, Taxila Lattice Boltzmann Method application, and Solid Fuel Ignition application. We use an alternate component grid combination formula by adding some redundancy on the SGCT to recover data from lost processes. User-level failure mitigation (ULFM) message passing interface (MPI) is used to recover the processes, and our implementation is robust over multiple failures and recovery (processes and nodes). An acceptable degree of modification of the applications is required. Results using the 2-D SGCT show competitive execution times with acceptable error (within 0.1% to 1.0%), compared to the same simulation with a single full resolution grid. The benefits improve when the 3-D SGCT is used. Experiments show the applications ability to successfully recover from multiple failures, and applying multiple SGCT reduces the computed solution error. Process recovery via ULFM MPI increases from approximately 1.5 sec at 64 cores to approximately 5 sec at 2048 cores for a one-off failure. This compares applications’ built-in checkpointing with job restart in conjunction with the classical SGCT on failure, which have overheads four times as large for a single failure, excluding the recomputation overhead. An analysis for a long-running application considering recomputation times indicates a reduction in overhead of over an order of magnitude.
Keywords: Fault tolerance; ULFM; process failure recovery; PDE solver; sparse grid combination; approximation error; gyrokinetic plasma; Taxila Lattice Boltzmann Method; Solid Fuel Ignition
Rights: © The Author(s) 2016
RMID: 0030106409
DOI: 10.1177/1094342015628056
Grant ID: http://purl.org/au-research/grants/arc/LP110200410
Appears in Collections:Computer Science publications

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