Jih-Woei Huang

dblp:14/6120 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2008
—ORCID · none

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

Systems, architecture and hardware · 3 · 2 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 77% High-performance computing · 23%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

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

TopicWeightPapersLastEvidence papers
Compilers and program optimization
loop optimization
0.012004
Using Elementary Linear Algebra to Solve Data Alignment for Arrays with Linear or Quadratic References · IEEE Trans. Parallel Distributed Syst. 2004
Memory systems › data layout optimization
data alignment
0.012004
Using Elementary Linear Algebra to Solve Data Alignment for Arrays with Linear or Quadratic References · IEEE Trans. Parallel Distributed Syst. 2004
High-performance computing
distributed memory systems
0.012004
Using Elementary Linear Algebra to Solve Data Alignment for Arrays with Linear or Quadratic References · IEEE Trans. Parallel Distributed Syst. 2004

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

linear algebra · 0.1
YearPublicationVenuePosition
2008 A flexible processor mapping technique toward data localization for block-cyclic data redistribution
Jih-Woei Huang, Chih-Ping Chu
J. Supercomput.1
2006 An Efficient Communication Scheduling Method for the Processor Mapping Technique Applied Data Redistribution
Jih-Woei Huang, Chih-Ping Chu
J. Supercomput.1
2004 Using Elementary Linear Algebra to Solve Data Alignment for Arrays with Linear or Quadratic References
abstract
Data alignment that facilitates data locality so that the data access communication costs can be minimized, helps distributed memory parallel machines improve their throughput. Most data alignment methods are devised mainly to align the arrays referenced using linear subscripts or quadratic subscripts with few (one or two) loop index variables. We propose two communication-free alignment techniques to align the arrays referenced using linear subscripts or quadratic subscripts with multiple loop index variables. The experimental results from our techniques on vector loop and TRFD of the perfect benchmarks reveal that our techniques can improve the execution times of the subroutines in these benchmarks.
Weng-Long Chang, Jih-Woei Huang, Chih-Ping Chu
IEEE Trans. Parallel Distributed Syst.2