Execution-based prediction using speculative slices

A relatively small set of static instructions has significant leverage on program execution performance. These problem instructions contribute a disproportionate number of cache misses and branch mispredictions because their behavior cannot be accurately anticipated using existing prefetching or bra...

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Vydáno v:28th IEEE International Symposium on Computer Architecture, 2001, Goteborg, Sweden s. 2 - 13
Hlavní autoři: Zilles, Craig, Sohi, Gurindar
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 01.01.2001
Edice:ACM Conferences
Témata:
Computer systems organization > Architectures > Parallel architectures > Very long instruction word
Computer systems organization > Architectures > Serial architectures > Complex instruction set computing
Computer systems organization > Architectures > Serial architectures > Reduced instruction set computing
Computer systems organization > Dependable and fault-tolerant systems and networks
General and reference > Cross-computing tools and techniques > Evaluation
General and reference > Cross-computing tools and techniques > Metrics
General and reference > Cross-computing tools and techniques > Performance
Networks > Network performance evaluation
ISBN:0769511627, 9780769511627
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Shrnutí:A relatively small set of static instructions has significant leverage on program execution performance. These problem instructions contribute a disproportionate number of cache misses and branch mispredictions because their behavior cannot be accurately anticipated using existing prefetching or branch prediction mechanisms. The behavior of many problem instructions can be predicted by executing a small code fragment called a speculative slice. If a speculative slice is executed before the corresponding problem instructions are fetched, then the problem instructions can move smoothly through the pipeline because the slice has tolerated the latency of the memory hierarchy (for loads) or the pipeline (for branches). This technique results in speedups up to 43 percent over an aggressive baseline machine. To benefit from branch predictions generated by speculative slices, the predictions must be bound to specific dynamic branch instances. We present a technique that invalidates predictions when it can be determined (by monitoring the program's execution path) that they will not be used. This enables the remaining predictions to be correctly correlated.
Bibliografie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0769511627
9780769511627
DOI:10.1145/379240.379246