ACM Transactions on Programming Languages and Systems (TOPLAS)
MPI-SIM: using parallel simulation to evaluate MPI programs
Proceedings of the 30th conference on Winter simulation
Architectural requirements and scalability of the NAS parallel benchmarks
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
An empirical performance evaluation of scalable scientific applications
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
NIC-based offload of dynamic user-defined modules for Myrinet clusters
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
Performance profiling overhead compensation for MPI programs
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Network-theoretic classification of parallel computation patterns
International Journal of High Performance Computing Applications
Simulating application resilience at exascale
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
Multiclass classification of distributed memory parallel computations
Pattern Recognition Letters
Task mapping in rectangular twisted tori
Proceedings of the High Performance Computing Symposium
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Parallel applications have message-passing patterns that are important to understand. Network topology, routing decisions, and connection and buffer management need to match the communication patterns of an application for it to run efficiently and scale well. These patterns are not easily discerned from the source code of an application, and even when the data is available it is not easy to categorize it appropriately such that meaningful knowledge emerges. We describe a novel system to gather the information we need to discover an application's communication pattern. We create five categories that help us analyze that data and explain how information from each category can be useful in the design of networking hardware and software. We use the NAS parallel benchmarks as examples on how to apply our techniques.