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In this context, techniques for reducing computing time using parallel processors are discussed.
Blocking is useful for reducing computing time, but may decrease the sensitivity if blocking variables are measured with error.
In this regard, high-performance computing offers a markedly competitive edge, not only in reducing computing time but also in tuning optimal models for whole-genome prediction.
To retain the accuracy of BayesR while reducing computing time, we propose here an EM algorithm, termed emBayesR, for genomic prediction, as an alternative to the MCMC implementation of BayesR.
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As well as an approach that finds all overlaps then implements transitive reduction to produce a string graph, we show how to output directly only the irreducible overlaps, significantly shrinking memory requirements and reducing compute time to O(N), independent of depth.
The use of parallel processing can also reduce computing time.
Response surface approximation was used to approximate the objective function to reduce computing time.
Recently many culling methods are studied and contribute to reduce computing time of CCD between deformable models [19],[19].
To reduce computing time, the method [5] proposes an algorithm to extract RPs from a single low-resolution image.
Not only will this reduce computing time, but will do so while minimizing the effects of the two evil forces of chip design power consumption and heat.
A new algorithm for computing results for a seasonal model, which reduces computing time by an order of magnitude for monthly time steps is presented.
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