A bridging model for parallel computation
Communications of the ACM
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Performance prediction and tuning of parallel programs
Performance prediction and tuning of parallel programs
Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Modeling Communication Overhead: MPI and MPL Performance on the IBM SP2
IEEE Parallel & Distributed Technology: Systems & Technology
Realistic Communication Model for Parallel Computing on Cluster
IWCC '99 Proceedings of the 1st IEEE Computer Society International Workshop on Cluster Computing
Modeling Performance of Parallel Programs
Modeling Performance of Parallel Programs
PEMPIs: A New Methodology for Modeling and Prediction of MPI Programs Performance
SBAC-PAD '04 Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing
Performance Characterisation of Intra-Cluster Collective Communications
SBAC-PAD '04 Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing
Using analytical models to load balancing in a heterogeneous network of computers
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
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The evaluation and prediction of parallel programs performance are becoming more and more important, so that they require appropriate techniques to identify the factors which influence the application execution time and also the way they interact. In this paper, we present some contributions of our research in this area by describing PEMPIs, a new methodology applied to the performance analysis and prediction of MPI programs. A new task graph helps us both to understand details of the application and to increase the accuracy of the prediction models. The proposed techniques are detailed and tested through the modeling of a complete application. PEMPIs efficiency has been proved by the results of this application modeling--most tests executed in a cluster of computers showed errors up to 10%.