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Jason R. C. Patterson

dblp:59/904 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1995
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Processor architecture and microarchitecture · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization › compiler optimization
static branch prediction
0.011995
Accurate Static Branch Prediction by Value Range Propagation · PLDI 1995
Processor architecture and microarchitecture
branch prediction
0.011995
Accurate Static Branch Prediction by Value Range Propagation · PLDI 1995
YearPublicationVenuePosition
1995 Accurate Static Branch Prediction by Value Range Propagation
abstract
The ability to predict at compile time the likelihood of a particular branch being taken provides valuable information for several optimizations, including global instruction scheduling, code layout, function inlining, interprocedural register allocation and many high level optimizations. Previous attempts at static branch prediction have either used simple heuristics, which can be quite inaccurate, or put the burden onto the programmer by using execution profiling data or source code hints. This paper presents a new approach to static branch prediction called value range propagation. This method tracks the weighted value ranges of variables through a program, much like constant propagation. These value ranges may be either numeric or symbolic in nature. Branch prediction is then performed by simply consulting the value range of the appropriate variable. Heuristics are used as a fallback for cases where the value range of the variable cannot be determined statically. In the process, va...
Jason R. C. Patterson
PLDI1