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BLAS-on-flash: An Efficient Alternative for Large Scale ML Training and Inference?
2019
Symposium on Networked Systems Design and Implementation
Many large scale machine learning training and inference tasks are memory-bound rather than compute-bound. That is, on large data sets, the working set of these algorithms does not fit in memory for jobs that could run overnight on a few multi-core processors. This often forces an expensive redesign of the algorithm for distributed platforms such as parameter servers and Spark. We propose an inexpensive and efficient alternative based on the observation that many ML tasks admit algorithms that
dblp:conf/nsdi/SubramanyaSGKB19
fatcat:bji26e4w4fahrivcab7myzfd5q