NDLI: Bloom filtering cache misses for accurate data speculation and prefetching ( ICS '02 )

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Bloom filtering cache misses for accurate data speculation and prefetching ( ICS '02 )

Content Provider	ACM Digital Library
Author	Lai, Shih-Chang Peir, Jih-Kwon Lai, Konrad Lu, Shih-Lien Stark, Jared
Abstract	A processor must know a load instruction's latency to schedule the load's dependent instructions at the correct time. Unfortunately, modern processors do not know this latency until well after the dependent instructions should have been scheduled to avoid pipeline bubbles between themselves and the load. One solution to this problem is to predict the load's latency, by predicting whether the load will hit or miss in the data cache. Existing cache hit/miss predictors, however, can only correctly predict about 50% of cache misses.This paper introduces a new hit/miss predictor that uses a Bloom Filter to identify cache misses early in the pipeline. This early identification of cache misses allows the processor to more accurately schedule instructions that are dependent on loads and to more precisely prefetch data into the cache. Simulations using a modified SimpleScalar model show that the proposed Bloom Filter is nearly perfect, with a prediction accuracy greater than 99% for the SPECint2000 benchmarks. IPC (Instructions Per Cycle) performance improved by 19% over a processor that delayed the scheduling of instructions dependent on a load until the load latency was known, and by 6% and 7% over a processor that always predicted a load would hit the cache and with a counter-based hit/miss predictor respectively. This IPC reaches 99.7% of the IPC of a processor with perfect scheduling.
Starting Page	189
Ending Page	198
Page Count	10
File Format	PDF
ISBN	1581134835
DOI	10.1145/514191.514219
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data speculation Data prefetching Instruction scheduling Bloom filter Data cache
Content Type	Text
Resource Type	Article

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