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Epidemic failure detection and consensus for extreme parallelism

Katti, A., Di Fatta, G., Naughton, T. and Engelmann, C. (2018) Epidemic failure detection and consensus for extreme parallelism. International Journal of High Performance Computing Applications, 32 (5). pp. 729-743. ISSN 1094-3420

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To link to this item DOI: 10.1177/1094342017690910

Abstract/Summary

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum’s User Level Failure Mitigation proposal has introduced an operation, MPI Comm shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI Comm shrink operation requires a failure detection and consensus algorithm. This paper presents three novel failure detection and consensus algorithms using Gossiping. Stochastic pinging is used to quickly detect failures during the execution of the algorithm, failures are then disseminated to all the fault-free processes in the system and consensus on the failures is detected using the three consensus techniques. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that the stochastic pinging detects all the failures in the system. In all the algorithms, the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus. The third approach is a three-phase distributed failure detection and consensus algorithm and provides consistency guarantees even in very large and extreme-scale systems while at the same time being memory and bandwidth efficient.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:71175
Publisher:Sage

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