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• W2580495852 abstract "The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve cluster-scale problems on a single commodity multicore machine." @default.
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• W2580495852 date "2017-01-26" @default.
• W2580495852 modified "2023-10-11" @default.
• W2580495852 title "A Multicore Path to Connectomics-on-Demand" @default.
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• W2580495852 doi "https://doi.org/10.1145/3018743.3018766" @default.
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