Icfp: tolerating all-level cache misses in in-order processors
Abstract
Growing concerns about power have revived interest in in-order pipelines. In-order
pipelines sacrifice single-thread performance. Specifically, they do not allow execution
to flow freely around data cache misses. As a result, they have difficulties overlapping
independent misses with one another. Previously proposed techniques like Runahead
execution and Multipass pipelining have attacked this problem. In this paper, we go
a step further and introduce iCFP (in-order Continual Flow Pipeline), an adaptation
of the CFP concept to an in-order processor. When iCFP encounters a primary data cache
or L2 miss, it checkpoints the register file and transitions into an "advance" execution
mode. Miss-independent instructions execute as usual and even update register state.
Missdependent instructions are diverted into a slice buffer, un-blocking the pipeline
latches. When the miss returns, iCFP "rallies" and executes the contents of the slice
buffer, merging miss-dependent state with missindependent state along the way. An
enhanced register dependence tracking scheme and a novel store buffer design facilitate
the merging process. Cycle-level simulations show that iCFP out-performs Runahead,
Multipass, and SLTP, another non-blocking in-order pipeline design. © 2008 IEEE.
Type
ConferencePermalink
https://hdl.handle.net/10161/11636Published Version (Please cite this version)
10.1109/HPCA.2009.4798281Collections
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Show full item recordScholars@Duke
Andrew Douglas Hilton
Professor of the Practice in the Department of Electrical and Computer Engineering
Drew Hilton is an Associate Professor of the Practice in Electrical and Computer Engineering,
as well as Pratt’s Director of Innovation in Computing Education.
His main focus is on teaching professional-level programming skills to ECE’s master's
students to prepare them for software engineering careers.
Professor Hilton also teaches a 3-week introduction to Programming Python for Duke's
Master in Interdisciplinary Data Science, and Duke's Center for Computatio

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