In this paper we propose a procedure to enable the training of several independent Multilayer Perceptron Neural Networks with different number neurons and activation functions in parallel (ParallelMLPs) by exploring principle locality parallelization capabilities modern CPUs GPUs. The core idea technique is represent sub-networks as single large network use Modified Matrix Multiplication that r...

A scalable algorithm for solving compact banded linear systems on distributed memory architectures is presented. The proposed method factorizes the original system into two levels of hierarchies, and solves it using parallel cyclic reduction both shared memory. This has a lower communication footprint across partitions compared to conventional algorithms involving data transposes or re-partitio...