A Case for NOW (Networks of Workstations)
IEEE Micro
A First Implementation of In-Transit Buffers on Myrinet GM Software
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Prototype of AM3: Active Mapper and Monitoring Module for Myrinet Environments
LCN '02 Proceedings of the 27th Annual IEEE Conference on Local Computer Networks
SKaMPI: A Detailed, Accurate MPI Benchmark
Proceedings of the 5th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Using Multirail Networks in High-Performance Clusters
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Supermon: A High-Speed Cluster Monitoring System
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
PARSE: A Tool for Parallel Application Run Time Sensitivity Evaluation
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Network performance variability in NOW clusters
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Power and environment aware control of Beowulf clusters
Cluster Computing
A network performance sensitivity metric for parallel applications
International Journal of High Performance Computing and Networking
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Excessive run time variability of parallel application codes on commodity clusters is a significant challenge. To gain insight into this problem our earlier work developed a tools to emulate parallel applications (PACE) by simulating computation and using the cluster's interconnection network for communication, and further study parallel application run time effects (PARSE). This work expands our previous efforts by presenting a metric derived from PARSE test results conducted on several widely used parallel benchmarks and application code fragments. The metric suggests that a parallel application's sensitivity to network performance variation can be quantified relative to its behavior in optimal network performance conditions. Ideas on how this metric can be useful to parallel application development, cluster system performance management and system administration are also presented.