Perceptron-Based Prefetch Filtering

Hardware prefetching is an effective technique for hiding cache miss latencies in modern processor designs. Prefetcher performance can be characterized by two main metrics that are generally at odds with one another: coverage, the fraction of baseline cache misses which the prefetcher brings into th...

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Published in:2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA) pp. 1 - 13
Main Authors: Bhatia, Eshan, Chacon, Gino, Pugsley, Seth, Teran, Elvira, Gratz, Paul V., Jimenez, Daniel A.
Format: Conference Proceeding
Language:English
Published: ACM 01.06.2019
Subjects:
Accuracy
Benchmark testing
Computer architecture
Costs
Engines
Filters
Hardware
Measurement
Prefetching
Tuning
ISSN:2575-713X
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Abstract Hardware prefetching is an effective technique for hiding cache miss latencies in modern processor designs. Prefetcher performance can be characterized by two main metrics that are generally at odds with one another: coverage, the fraction of baseline cache misses which the prefetcher brings into the cache; and accuracy, the fraction of prefetches which are ultimately used. An overly aggressive prefetcher may improve coverage at the cost of reduced accuracy. Thus, performance may be harmed by this over-aggressiveness because many resources are wasted, including cache capacity and bandwidth. An ideal prefetcher would have both high coverage and accuracy. In this paper, we introduce Perceptron-based Prefetch Filtering (PPF) as a way to increase the coverage of the prefetches generated by an underlying prefetcher without negatively impacting accuracy. PPF enables more aggressive tuning of the underlying prefetcher, leading to increased coverage by filtering out the growing numbers of inaccurate prefetches such an aggressive tuning implies. We also explore a range of features to use to train PPF's perceptron layer to identify inaccurate prefetches. PPF improves performance on a memory-intensive subset of the SPEC CPU 2017 benchmarks by 3.78% for a single-core configuration, and by 11.4% for a 4-core configuration, compared to the underlying prefetcher alone.
AbstractList Hardware prefetching is an effective technique for hiding cache miss latencies in modern processor designs. Prefetcher performance can be characterized by two main metrics that are generally at odds with one another: coverage, the fraction of baseline cache misses which the prefetcher brings into the cache; and accuracy, the fraction of prefetches which are ultimately used. An overly aggressive prefetcher may improve coverage at the cost of reduced accuracy. Thus, performance may be harmed by this over-aggressiveness because many resources are wasted, including cache capacity and bandwidth. An ideal prefetcher would have both high coverage and accuracy. In this paper, we introduce Perceptron-based Prefetch Filtering (PPF) as a way to increase the coverage of the prefetches generated by an underlying prefetcher without negatively impacting accuracy. PPF enables more aggressive tuning of the underlying prefetcher, leading to increased coverage by filtering out the growing numbers of inaccurate prefetches such an aggressive tuning implies. We also explore a range of features to use to train PPF's perceptron layer to identify inaccurate prefetches. PPF improves performance on a memory-intensive subset of the SPEC CPU 2017 benchmarks by 3.78% for a single-core configuration, and by 11.4% for a 4-core configuration, compared to the underlying prefetcher alone.
Author Gratz, Paul V.
Teran, Elvira
Pugsley, Seth
Jimenez, Daniel A.
Bhatia, Eshan
Chacon, Gino
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  organization: Texas A&M International University
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  organization: Texas A&M University
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  givenname: Daniel A.
  surname: Jimenez
  fullname: Jimenez, Daniel A.
  organization: Texas A&M University and Barcelona Supercomputing Center
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Snippet Hardware prefetching is an effective technique for hiding cache miss latencies in modern processor designs. Prefetcher performance can be characterized by two...
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SubjectTerms Accuracy
Benchmark testing
Computer architecture
Costs
Engines
Filters
Hardware
Measurement
Prefetching
Tuning
Title Perceptron-Based Prefetch Filtering
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