When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of Accelerating Machine Learning using Coding
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In this paper we study the use of coding techniques to accelerate machine learning (ML). Coding techniques, such as prefix codes, have been extensively studied and used to accelerate low-level data processing primitives such as scans in a relational database system. However, there is little work on how to exploit them to accelerate ML algorithms. In fact, applying coding techniques for faster ML faces a unique challenge: one needs to consider both how the codes fit into the optimization algorithm used to train a model, and the interplay between the model structure and the coding scheme. Surprisingly and intriguingly, our study demonstrates that a slight variant of the classical Lempel-Ziv-Welch (LZW) coding scheme is a good fit for several popular ML algorithms, resulting in substantial runtime savings. Comprehensive experiments on several real-world datasets show that our LZW-based ML algorithms exhibit speedups of up to 31x compared to a popular and state-of-the-art ML library, with no changes to ML accuracy, even though the implementations of our LZW variants are not heavily tuned. Thus, our study reveals a new avenue for accelerating ML algorithms using coding techniques and we hope this opens up a new direction for more research.
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