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Functional languages move toward the mainstream and reframe how we approach parallelism.
Also, two parallel approaches (intrinsic parallelism and data parallelism) for the computation of the distributions are proposed, implemented and assessed by using a parallel DSP-based system.
This fact can be largely taken advantage of in order to achieve parallelism by existing workflow management approaches emphasizing parallelization.
Analysis of the method is given within a parallel environment: efficiency, scaling, and a two-tiered approach to parallelism with the algorithm are discussed.
Data-flow is a natural approach to parallelism.
Language-based approaches to parallelism have been incorporated into the Fortran standard.
I review the success of data-parallelism, including SQL and MapReduce, and speculate on the future of data-centric approaches to parallelism.
It is shown that traditional approaches to parallelism including message passing and scatter-gather can be improved upon in terms of speed-up and memory management.
Key features are linear elements, a direct approach to parallelism and node overtaking (avoiding penalty functions), rapid inversion of the mass matrix by preconditioned conjugate gradients, and explicit Euler time stepping.
The first approach is task parallelism using multicore computer and MATLAB® parallel computing toolbox.
We demonstrate the effectiveness of our approach with GPU parallelism on a number of representative examples.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com