CMU CS 15740 - Exceeding the Dataflow Limit via Value Prediction (12 pages)

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Exceeding the Dataflow Limit via Value Prediction



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Exceeding the Dataflow Limit via Value Prediction

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Pages:
12
School:
Carnegie Mellon University
Course:
Cs 15740 - Computer Architecture
Computer Architecture Documents

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Copyright 1996 IEEE Published in the Proceedings of the 29th Annual ACM IEEE International Symposium on Microarchitecture Dec 2 4 1996 Paris France Personal use of this material is permitted However permissions to reprint republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists or to reuse any copyrighted component of this work in other works must be obtained from the IEEE Contact Manager Copyright and Permissions IEEE Service Center 445 Hoes Lane P O Box 1331 Piscataway NJ 08855 1331 USA Telephone Intl 908 562 3966 Exceeding the Dataflow Limit via Value Prediction Mikko H Lipasti and John Paul Shen Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 mhl shen ece cmu edu Abstract For decades the serialization constraints imposed by true data dependences have been regarded as an absolute limit the dataflow limit on the parallel execution of serial programs This paper proposes a new technique value prediction for exceeding that limit that allows data dependent instructions to issue and execute in parallel without violating program semantics This technique is built on the concept of value locality which describes the likelihood of the recurrence of a previously seen value within a storage location inside a computer system Value prediction consists of predicting entire 32 and 64 bit register values based on previously seen values We find that such register values being written by machine instructions are frequently predictable Furthermore we show that simple microarchitectural enhancements to a modern microprocessor implementation based on the PowerPC 620 that enable value prediction can effectively exploit value locality to collapse true dependences reduce average result latency and provide performance gains of 4 5 23 depending on machine model by exceeding the dataflow limit 1 Motivation and Related Work There are two fundamental



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