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Compared with the G-Jaya algorithm, three main procedures of the GPU-Jaya, the solution update, fitness value computation, and the best/worst solution selection are all computed in parallel on GPU via a compute unified device architecture (CUDA).
Since the mobile agents can overlap with each other their random walk steps could be computed in parallel in order to reduce the computation time (either on a multicore CPU or GPU [ 13, 47]).
One of the main advantages over a GPU implementation is the parallelism of the FPGA architecture: all different stages are computed in parallel, while the GPU performs all intermediate stages in sequence.
The M glass jet representative are computed in parallel.
In principle, each output pixel can be computed in parallel.
Its entire time evolution is computed in parallel.
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Figure 46 depicts the main part of the CPU code that could be accelerated by computing in parallel.
An algorithm is proposed for locating and computing in parallel and with certainty all the simple roots of any twice continuously differentiable function in any specific interval.
The effectiveness of explicit approximate inverse preconditioning schemes relies on the use of efficient preconditioners that are close approximants to the coefficient matrix and are fast to compute in parallel.
The domain decomposition of the wavefield solutions is used to compute in parallel the gradient of the objective function and the diagonal Hessian, this latter providing a suitable scaling of the gradient.
In total, computing in parallel the maximum folding scores for subsequences starting at every possible position i and ending at position j takes O(q * n + n/q) = O(q * n) time.
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