Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Program speedup in a heterogeneous computing network
Journal of Parallel and Distributed Computing - Special issue on heterogeneous processing
Journal of Parallel and Distributed Computing
Studies in Computational Science: Parallel Programming Paradigms
Studies in Computational Science: Parallel Programming Paradigms
Optimal Scheduling Algorithms for Communication Constrained Parallel Processing
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
An efficiency and scalability model for heterogeneous clusters.
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
Dynamic Load Balancing on Dedicated Heterogeneous Systems
Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
A framework for the application of metaheuristics to tasks-to-processors assignation problems
The Journal of Supercomputing
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We study the master-slave paradigm over heterogeneous systems. According to an analytical model, we develop a dynamic programming algorithm that allows to solve the optimal mapping for such paradigm. Our proposal considers heterogeneity due both to computation and also to communication. The optimization strategy used allows to obtain the set of processors for an optimal computation. The computational results show that considering heterogeneity also on the communication increases the performance of the parallel algorithm.