Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
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
Automatic performance analysis of hybrid MPI/OpenMP applications
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Evolutions in parallel distributed and network-based processing
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
Problem diagnosis in large-scale computing environments
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Scalable compression and replay of communication traces in massively parallel environments
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Incremental call-path profiling: Research Articles
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Open MPI: a flexible high performance MPI
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
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
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To develop an efficient parallel application is not an easy task. Applications rarely achieve a good performance immediately therefore, a careful performance analysis and optimization are crucial. These tasks are difficult and require a thorough understanding of the program's behavior. In this paper, we propose an on-line performance modeling technique, which enables the automated discovery of causal execution flows, composed of communication and computational activities, in MPI parallel programs. Our model reflects an application behavior and is made up of elements correlated with high-level program structures, such as loops and communication operations. Moreover, our approach enables an assortment of on-line diagnosis techniques which may further automate the performance understanding process.