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Type: Artigo de evento
Title: A Segmented Bloom Filter Algorithm For Efficient Predictors
Author: Breternitz M.
Loh G.H.
Black B.
Rupley J.
Sassone P.G.
Attrot W.
Wu Y.
Abstract: Bloom Filters are a technique to reduce the effects of conflicts/ interference in hash table-like structures. Conventional hash tables store information in a single location which is susceptible to destructive interference through hash conflicts. A Bloom Filter uses multiple hash functions to store information in several locations, and recombines the information through some voting mechanism. Many microarchitectural predictors use simple single-index hash tables to make binary 0/1 predictions, and Bloom Filters help improve predictor accuracy. However, implementing a true Bloom Filter requires k hash functions, which in turn implies a k-ported hash table, or k sequential accesses. Unfortunately, the area of a hardware table increases quadratically with the port count, increasing costs of area, latency and power consumption. We propose a simple but elegant modification to the Bloom Filter algorithm that uses banking combined with special hash functions that guarantee all hash indexes fall into non-conflicting banks. We evaluate several applications of our Banked Bloom Filter (BBF) prediction in processors: BBF branch prediction, BBF load hit/miss prediction, and BBF last-tag prediction. We show that BBF predictors can provide accurate predictions with substantially less cost than previous techniques. © 2008 IEEE.
Rights: fechado
Identifier DOI: 10.1109/SBAC-PAD.2008.24
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

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