A bridging model for parallel computation
Communications of the ACM
Global Optimization for Mapping Parallel Image Processing Tasks on Distributed Memory Machines
Global Optimization for Mapping Parallel Image Processing Tasks on Distributed Memory Machines
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Mapping data-parallel tasks onto partially reconfigurable hybrid processor architectures
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An Experimental Study on How to Build Efficient Multi-core Clusters for High Performance Computing
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Methodology for modelling SPMD hybrid parallel computation
Concurrency and Computation: Practice & Experience
A Bridging Model for Multi-core Computing
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
An efficient load-balancing algorithm for image processing applications on multicore processors
IFMT '08 Proceedings of the 1st international forum on Next-generation multicore/manycore technologies
Multicore challenges and benefits for high performance scientific computing
Scientific Programming - Complexity in Scalable Computing
Adaptive scheduling of parallel computations for SPMD tasks
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
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The need to efficiently execute applications in heterogeneous environments is a current challenge for parallel computing programmers. The communication heterogeneities found in multicore clusters need to be addressed to improve efficiency and speedup. This work presents a methodology developed for SPMD applications, which is centered on managing communication heterogeneities and improving system efficiency on multicore clusters. The methodology is composed of three phases: characterization, mapping strategy, and scheduling policy. We focus on SPMD applications which are designed through a message-passing library for communication, and selected according to their synchronicity and communications volume. The novel contribution of this methodology is it determines the approximate number of cores necessary to achieve a suitable solution with a good execution time, while the efficiency level is maintained over a threshold defined by users. Applying this methodology gave results showing a maximum improvement in efficiency of around 43% in the SPMD applications tested.