Parallel Computing on Heterogeneous Networks
Parallel Computing on Heterogeneous Networks
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Scalability of heterogeneous parallel systems
Programming and Computing Software
A Parallel Computational Model for Heterogeneous Clusters
IEEE Transactions on Parallel and Distributed Systems
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
Algorithm-system scalability of heterogeneous computing
Journal of Parallel and Distributed Computing
A self-adaptive computing framework for parallel maximum likelihood evaluation
The Journal of Supercomputing
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Hi-index | 0.00 |
This work studies the problem of balancing the workload of iterative algorithms on heterogeneous multiprocessors. An approach, called ADITHE, is proposed and evaluated. Its main features are: (1) using a homogeneous distribution of the workload on the heterogeneous system, the speed of every node is estimated during the first iterations of the algorithm; (2) according to the speed of every node, a new workload distribution is carried out; (3) the remaining iterations of the algorithm are executed. The result of this workload redistribution is that the execution times for every iteration at every node are similar and, consequently, the penalties due to synchronization between nodes at every iteration are mostly eliminated. This approach is appropriate for iterative algorithms with similar workload at every iteration, and with a relevant number of iterations. The high portability of ADITHE is guaranteed because the estimation of speed of nodes is included in the execution of the parallel algorithm. There is a wide variety of iterative algorithms related to science and engineering which can take advantage of ADITHE. An example of this kind of algorithms (morphological processing of hyperspectral images) is considered in this work to evaluate its performance when ADITHE is applied. The analysis of the results shows that ADITHE significantly improves the performance of parallel iterative algorithms on heterogeneous platforms.