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Sridhar Chirravuri

dblp:48/4038 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

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

Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 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 · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
parallel algorithms
0.011996
PARODS - a study of parallel algorithms for ordering DNA sequences · Comput. Appl. Biosci. 1996
Parallel and multicore computing › parallel algorithms › parallel combinatorial optimization
parallel simulated annealing
0.011996
PARODS - a study of parallel algorithms for ordering DNA sequences · Comput. Appl. Biosci. 1996
Bioinformatics and computational biology › genomics
physical mapping
0.011996
PARODS - a study of parallel algorithms for ordering DNA sequences · Comput. Appl. Biosci. 1996

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

simulated annealing · 0.0markov chain decomposition · 0.0SIMD · 0.0MIMD · 0.0
YearPublicationVenuePosition
1998 Parallel Computing for Chromosome Reconstruction via Ordering of DNA Sequences
Suchendra M. Bhandarkar, Salem Machaka, Sridhar Chirravuri, Jonathan P. Arnold
Parallel Comput.3
1996 PARODS - a study of parallel algorithms for ordering DNA sequences
abstract
A suite of parallel algorithms for ordering DNA sequences (termed PARODS) is presented. The algorithms in PARODS are based on an earlier serial algorithm, ODS, which is a physical mapping algorithm based on simulated annealing. Parallel algorithms for simulated annealing based on Markov chain decomposition are proposed and applied to the problem of physical mapping. Perturbation methods and problem-specific annealing heuristics are proposed and described. Implementations of parallel Single Instruction Multiple Data (SIMD) algorithms on a 2048 processor MasPar MP-2 system and implementations of parallel Multiple Instruction Multiple Data (MIMD) algorithms on an 8 processor Intel iPSC/860 system are presented. The convergence, speedup and scalability characteristics of the aforementioned algorithms are analyzed and discussed. The best SIMD algorithm is shown to have a speedup of approximately 1000 on the 2048 processor MasPar MP-2 system, whereas the best MIMD algorithm is shown to have a speedup of approximately 5 on the 8 processor Intel iPSC/860 system.
Suchendra M. Bhandarkar, Sridhar Chirravuri, Jonathan P. Arnold
Comput. Appl. Biosci.2