A bridging model for parallel computation
Communications of the ACM
NAS parallel benchmark results
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Using the SimOS machine simulator to study complex computer systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Analyzing scheduling policies using Dimemas
Parallel Computing - Special double issue on environment and tools for parallel scientific computing
Automatic Performance Prediction of Parallel Programs
Automatic Performance Prediction of Parallel Programs
The Tool-Set - An Integrated Tool Envrionment for PVM
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
DiP: A Parallel Program Development Environment
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
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
Adaptive execution: improving performance through the runtime adaptation of performance parameters
Adaptive execution: improving performance through the runtime adaptation of performance parameters
Simulating and optimizing a peer-to-peer computing framework
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
An idiom-finding tool for increasing productivity of accelerators
Proceedings of the international conference on Supercomputing
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The Networks of Workstations (NoW) are becoming real distributed execution platforms for scientific applications. Nevertheless, the heterogeneity of these platforms makes complex the design and the optimization of distributed applications. To overcome this problem, we have developed a performance prediction tool called ChronosMix, which can predict the execution time of a distributed algorithm on parallel or distributed architecture. In this article we present the performance prediction of an NAS Benchmark program with the ChronosMix environment. This study aims at emphasizing the contribution of our environment in the performance prediction process.