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切換: 標籤 | MARC模式 | ISBD

Express Misprediction Recovery.

Kothinti Naresh, Vignyan Reddy.

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Express Misprediction Recovery.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Express Misprediction Recovery./
作者:	Kothinti Naresh, Vignyan Reddy.
面頁冊數:	143 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3635514
ISBN:	9781321166101

Express Misprediction Recovery.
Kothinti Naresh, Vignyan Reddy.

Express Misprediction Recovery. - 143 p.

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2014.

Continuing advances in branch prediction provide a promising avenue for mitigating the impact of control dependences on extracting instruction-level parallelism in high performance processors. However, the rate of improvement in prediction rates has slowed significantly, and may be approaching an asymptotic upper bound, particularly once practical constraints on the predictor's cycle time, energy, and area are taken into consideration. To reach higher levels of performance, future processors must not just reduce the number of mispredictions, but should employ mechanisms that reduce the performance penalty of each misprediction.

ISBN: 9781321166101Subjects--Topical Terms:

1669061
Engineering, Computer.

Express Misprediction Recovery.
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500 $a Adviser: Mikko H. Lipasti.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2014.
520 $a Continuing advances in branch prediction provide a promising avenue for mitigating the impact of control dependences on extracting instruction-level parallelism in high performance processors. However, the rate of improvement in prediction rates has slowed significantly, and may be approaching an asymptotic upper bound, particularly once practical constraints on the predictor's cycle time, energy, and area are taken into consideration. To reach higher levels of performance, future processors must not just reduce the number of mispredictions, but should employ mechanisms that reduce the performance penalty of each misprediction.
520 $a This thesis presents EMR --- an approach that boosts performance by reducing the misprediction penalty and by amplifying instruction delivery bandwidth to the execution core. EMR implements a novel in-place misprediction recovery that minimizes latency to activate instructions from the correct control path, while also utilizing control independence to avoid unnecessary re-execution of instructions from beyond control flow joins. Performance analysis shows that EMR outperforms the baseline by up to 81% with a mean performance gain of 23% in CINT2006. Additionally, using EMR speeds up MiBench, Graph500 and CFP2006 by 20%, 10.5% and 4% respectively. Energy analysis shows that EMR consumes 16% lower energy than the baseline in CINT2006. As we scale up the window sizes, EMR can, with its amplified instruction delivery, commit up to seven muops per cycle without increasing front end bandwidth.
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