Parallel Auto-Scheduling of Counting Queries in Machine Learning Applications on HPC Systems
摘要
We introduce a parallel mechanism for auto-scheduling data access queries in machine learning applications. Our solution combines the advantages of three individual strategies to reduce the time of query stream execution. Using bayesian network learning as a use case, we achieve several times speedup compared to the best possible strategy on two different computing servers.