NDLI: Prefetch optimizations on large-scale applications via parameter value prediction

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Prefetch optimizations on large-scale applications via parameter value prediction

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Prefetch optimizations on large-scale applications via parameter value prediction

Content Provider	ACM Digital Library
Author	Liao, Shih-wei Chou, Chinyen Nguyen, Donald Hung, Tzu-Han Zhou, Hucheng Tu, Chiaheng
Abstract	A typical data center application requires the processor cycles of thousands of machines. Even a single-digit performance improvement can significantly reduce the cost and power consumption of a data center. Unfortunately, achieving sustained improvement, even if modest, is difficult. Data centers are dynamic environments where applications are frequently released and servers are continually upgraded. For maintainability and fault tolerance, the physical capabilities and configuration of the servers are abstracted from the application programmer. We study application performance under different processor prefetch configurations. These configurations are largely transparent to the programmer, yet we observe a wide range of performance when comparing the worst and best configurations, with relative performance improvement ranging from 1.4% to 75.1%. Alarmingly, one application that consumes many processor cycles has a 23.6% improvement. Default prefetch configurations favor aggressively prefetching memory, which benefits most applications, but some data center applications have highly tuned memory behavior and aggressive prefetching severely decreases performance. We develop a tuning framework which attempts to predict the optimal configuration based on hardware performance counters. It applies to a large number of performance-critical data center applications without modifying the source codeor binaries. The framework achieves performance within 1% of the best performance of a suite of important data center applications.
Starting Page	519
Ending Page	520
Page Count	2
File Format	PDF
ISBN	9781605584980
DOI	10.1145/1542275.1542359
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Machine learning Data center applications
Content Type	Text
Resource Type	Article

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