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Kalpana Ramakrishnan

dblp:34/5235 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

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

Systems, architecture and hardware · 1

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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 50% Processor architecture and microarchitecture · 50%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › task scheduling
dynamic scheduling
0.012001
Register Renaming and Scheduling for Dynamic Execution of Predicated Code · HPCA 2001
Processor architecture and microarchitecture › instruction-level parallelism
predicated execution
0.012001
Register Renaming and Scheduling for Dynamic Execution of Predicated Code · HPCA 2001
Compilers and program optimization › instruction scheduling
instruction-level parallelism
0.012001
Register Renaming and Scheduling for Dynamic Execution of Predicated Code · HPCA 2001
Compilers and program optimization
predicated compilation
0.012001
Register Renaming and Scheduling for Dynamic Execution of Predicated Code · HPCA 2001

Methods — techniques the papers use, named apart from their topics

performance evaluation · 0.1microarchitecture simulation · 0.1
YearPublicationVenuePosition
2001 Register Renaming and Scheduling for Dynamic Execution of Predicated Code
abstract
To achieve higher processor performance requires greater synergy between advanced hardware features and innovative compiler techniques. Recent advancement in compilation techniques for predicated execution has provided significant opportunity in exploiting instruction level parallelism. However, little research has been done on how to efficiently execute predicated code in a dynamic microarchitecture. In this paper, we evaluate hardware optimizations for executing predicated code on a dynamically scheduled microarchitecture. We provide two novel ideas to improve the efficiency of executing predicated code. On a generic Intel Itanium processor pipeline model, we demonstrate that, with some microarchitecture enhancements, a dynamic execution processor can achieve about 16% performance improvement over an equivalent static execution processor.
Perry H. Wang, Hong Wang 0003, Ralph-Michael Kling, Kalpana Ramakrishnan, John Paul Shen
HPCA4