Machine learning-based prefetch optimization for data center applications

• Published in: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis pp. 1 - 10
• Main Authors: Liao, Shih-wei, Hung, Tzu-Han, Nguyen, Donald, Chou, Chinyen, Tu, Chiaheng, Zhou, Hucheng
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 14.11.2009
• Series: ACM Conferences
• Subjects: Computer systems organization > Architectures > Parallel architectures > Multiple instruction, multiple data; Computing methodologies > Machine learning; Data centers; General and reference > Cross-computing tools and techniques > Design; General and reference > Cross-computing tools and techniques > Performance; Hardware; Machine learning; Machine learning algorithms; Optimization; Prediction algorithms; Predictive models; Prefetching; Training; Tuning
• ISBN: 1605587443; 9781605587448
• ISSN: 2167-4329
• DOI: 10.1145/1654059.1654116

Performance tuning for data centers is essential and complicated. It is important since a data center comprises thousands of machines and thus a single-digit performance improvement can significantly reduce cost and power consumption. Unfortunately, it is extremely difficult as data centers are dynamic environments where applications are frequently released and servers are continually upgraded. In this paper, we study the effectiveness of different processor prefetch configurations, which can greatly influence the performance of memory system and the overall data center. We observe a wide performance gap when comparing the worst and best configurations, from 1.4% to 75.1%, for 11 important data center applications. We then develop a tuning framework which attempts to predict the optimal configuration based on hardware performance counters. The framework achieves performance within 1% of the best performance of any single configuration for the same set of applications.