Performance Characterization and Optimizations of Traditional ML Applications

TL;DR

This paper analyzes the performance of traditional machine learning applications with large datasets, identifies bottlenecks, and demonstrates how cache and memory optimizations can significantly improve their efficiency.

Contribution

It provides a detailed performance characterization of traditional ML methods and introduces practical optimizations in scikit-learn that enhance performance on real systems.

Findings

Performance improvements of 5.2%-27.1% with software prefetching.

Data layout and reordering yield 6.16%-28.0% performance gains.

Insights into bottlenecks in traditional ML applications with large datasets.

Abstract

Even in the era of Deep Learning based methods, traditional machine learning methods with large data sets continue to attract significant attention. However, we find an apparent lack of a detailed performance characterization of these methods in the context of large training datasets. In this work, we study the system's behavior of a number of traditional ML methods as implemented in popular free software libraries/modules to identify critical performance bottlenecks experienced by these applications. The performance characterization study reveals several interesting insights on the performance of these applications. Then we evaluate the performance benefits of applying some well-known optimizations at the levels of caches and the main memory. More specifically, we test the usefulness of optimizations such as (i) software prefetching to improve cache performance and (ii) data layout and computation reordering optimizations to improve locality in DRAM accesses. These optimizations are implemented as modifications to the well-known scikit-learn library, and hence can be easily leveraged by application programmers. We evaluate the impact of the proposed optimizations using a combination of simulation and execution on a real system. The software prefetching optimization results in performance benefits varying from 5.2%-27.1% on different ML applications while the data layout and computation reordering approaches yield 6.16%-28.0% performance improvement.