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Dead-block prediction & dead-block correlating prefetchers
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Source International Symposium on Computer Architecture archive
Proceedings of the 28th annual international symposium on Computer architecture table of contents
Göteborg, Sweden
Pages: 144 - 154  
Year of Publication: 2001
ISBN:0-7695-1162-7
Also published in ...
Authors
An-Chow Lai  Electrical & Computer Engineering, Purdue University, West Lafayette, IN
Cem Fide  Sun Microsystems, 901 San Antonio Rd, Palo Alto, CA
Babak Falsafi  Electrical & Computer Engineering, Carnegie Mellon University, Pittsburgh, PA
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\TCCA : TC on Computer Arhitecture
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 28,   Downloads (12 Months): 107,   Citation Count: 37
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ABSTRACT

Effective data prefetching requires accurate mechanisms to predict both “which” cache blocks to prefetch and “when” to prefetch them. This paper proposes the Dead-Block Predictors (DBPs), trace-based predictors that accurately identify “when” an Ll data cache block becomes evictable or “dead”. Predicting a dead block significantly enhances prefetching lookahead and opportunity, and enables placing data directly into Ll, obviating the need for auxiliary prefetch buffers. This paper also proposes Dead-Block Correlating Prefetchers (DBCPs), that use address correlation to predict “which” subsequent block to prefetch when a block becomes evictable. A DBCP enables effective data prefetching in a wide spectrum of pointer-intensive, integer, and floating-point applications.

We use cycle-accurate simulation of an out-of-order superscalar processor and memory-intensive benchmarks to show that: (1) dead-block prediction enhances prefetching lookahead at least by an order of magnitude as compared to previous techniques, (2) a DBP can predict dead blocks on average with a coverage of 90% only mispredicting 4% of the time, (3) a DBCP offers an address prediction coverage of 86% only mispredicting 3% of the time, and (4) DBCPs improve performance by 62% on average and 282% at best in the benchmarks we studied.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Jean-Loup Baer and Tien-Fu Chen. Dynamic improvements of locality in virtual memory systems, IEEE Transactions on Software Engineering, March 1976.
 
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Mark J. Charney and Anthony P. Reeves. Generalized correlation-based hardware prefetching. Technical Report EE- CEG-95-1, School of Electrical Engineering, Cornell University, February 1995.
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Abraham Mendelson, Dominique Thi'ebaut, and Dhiraj Pradhan. Modeling live and dead lines in cache memory systems. Technical Report TR-90-CSE- 14, Department of Electrical and Computer Engineering, University of Massachusetts, 1990.
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Ravi Nair. Dynamic path-based branch prediction. In Proceedings of the 29th Annual IEEE/A CM International Symposium on Microarchitecture (MICRO 29), pages 142-1521, December 1996.
 
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CITED BY  38

Collaborative Colleagues:
An-Chow Lai: colleagues
Cem Fide: colleagues
Babak Falsafi: colleagues