A parallel algorithm for gradient training of feedforward neural networks
Parallel Computing
Using the BSP cost model to optimise parallel neural network training
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
Parallel Scientific Computation: A Structured Approach Using BSP and MPI
Efficiency Analysis of Parallel Batch Pattern NN Training Algorithm on General-Purpose Supercomputer
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Study of neural net training methods in parallel and distributed architectures
Future Generation Computer Systems
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The development of a computational cost model of parallel batch pattern back propagation training algorithm of a multilayer perceptron is presented in this paper. The model is developed using Bulk Synchronous Parallelism approach. The concrete parameters of the computational cost model are obtained. The developed model is used for the theoretical prediction of a parallelization efficiency of the algorithm. The predicted and real parallelization efficiencies are compared for different parallelization scenarios on two parallel high performance systems.