In this paper, we introduce UPLIFT, a framework for ParalleLIzing Feature Transformations. UPLIFT constructs a fine-grained task graph for a set of transformations, optimizes the plan according to data characteristics, and executes this plan in a cache-conscious manner. We show that the resulting framework is applicable to a wide range of transformations. Furthermore, we propose the FTBench benchmark with transformations and datasets from various domains. On this benchmark, UPLIFT yields speedups of up to 31.6x (9.27x on average) compared to state-of-the-art ML systems.
By: arnab phani
Title: UPLIFT: Parallelization Strategies for Feature Transformations in ML Workloads. VLDB 2022.
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