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Y. Pu

dblp:33/3679 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1999
—ORCID · none

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

Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 67% High-performance computing · 33%

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

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › parallel programming models › task parallelism
master-slave tasking
0.011999
Distributed Processing for Cinematic Holographic Particle Image Velocimetry · HPDC 1999
Parallel and multicore computing › parallel algorithms
parallel algorithm design
0.011999
Distributed Processing for Cinematic Holographic Particle Image Velocimetry · HPDC 1999
High-performance computing
scientific computing systems
0.011999
Distributed Processing for Cinematic Holographic Particle Image Velocimetry · HPDC 1999

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

concise cross correlation · 0.0MPI · 0.0
YearPublicationVenuePosition
1999 Distributed Processing for Cinematic Holographic Particle Image Velocimetry
abstract
Recently the GEMINI Holographic Particle Image Velocimetry (HPIV) system developed in the Laser Flow Diagnostics (LFD) lab at Kansas State University has been successfully applied in volumetric 3D flow velocity measurement. Due to the 3D nature of this application, very large computation and communication requirements are imposed. An innovation algorithm, the Concise Cross Correlation (CCC), is employed in the system to extract velocity field from the hologram of the test flows. With CCC we achieved a compression ratio of 10/sup 4/ and a processing speed 1000 times faster than with traditional 3D FFT-based correlation. To further accelerate the processing speed for fully time- and space-resolved measurement, parallel processing is necessary. We present our design for a distributed system supporting this previously unparallelized application, and comment on our experiences implementing a master-slave distributed version of CCC utilizing MPI. Brief experimental results on Gigabit Ethernet and multiprocessor Pentium Xeon systems are given.
Y. Pu, Daniel Andresen
HPDC1