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Article overview
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End-to-end Optimization of Machine Learning Prediction Queries | | Kwanghyun Park
; Karla Saur
; Dalitso Banda
; Rathijit Sen
; Matteo Interlandi
; Konstantinos Karanasos
; | | Date: |
1 Jun 2022 | | Abstract: | Prediction queries are widely used across industries to perform advanced
analytics and draw insights from data. They include a data processing part
(e.g., for joining, filtering, cleaning, featurizing the datasets) and a
machine learning (ML) part invoking one or more trained models to perform
predictions. These parts have so far been optimized in isolation, leaving
significant opportunities for optimization unexplored. We present Raven, a
production-ready system for optimizing prediction queries. Raven follows the
enterprise architectural trend of collocating data and ML runtimes. It relies
on a unified intermediate representation that captures both data and ML
operators in a single graph structure to unlock two families of optimizations.
First, it employs logical optimizations that pass information between the data
part (and the properties of the underlying data) and the ML part to optimize
each other. Second, it introduces logical-to-physical transformations that
allow operators to be executed on different runtimes (relational, ML, and DNN)
and hardware (CPU, GPU). Novel data-driven optimizations determine the runtime
to be used for each part of the query to achieve optimal performance. Our
evaluation shows that Raven improves performance of prediction queries on
Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex
models where GPU acceleration is beneficial, Raven provides up to 8x speedup
compared to state-of-the-art systems. | | Source: | arXiv, 2206.00136 | | Services: | Forum | Review | PDF | Favorites | |
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