Efficient GPU-accelerated parallel cross-correlation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511504" target="_blank" >RIV/00216208:11320/25:10511504 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kYWMlQP8Gy" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kYWMlQP8Gy</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jpdc.2025.105054" target="_blank" >10.1016/j.jpdc.2025.105054</a>
Alternative languages
Result language
angličtina
Original language name
Efficient GPU-accelerated parallel cross-correlation
Original language description
Cross-correlation is a data analysis method widely employed in various signal processing and similarity-search applications. Our objective is to design a highly optimized GPU-accelerated implementation that will speed up the applications and also improve energy efficiency since GPUs are more efficient than CPUs in data-parallel tasks. There are two rudimentary ways to compute cross-correlation - a definition-based algorithm that tries all possible overlaps and an algorithm based on the Fourier transform, which is much more complex but has better asymptotical time complexity. We have focused mainly on the definition-based approach which is better suited for smaller input data and we have implemented multiple CUDA-enabled algorithms with multiple optimization options. The algorithms were evaluated on various scenarios, including the most typical types of multi-signal correlations, and we provide empirically verified optimal solutions for each of the studied scenarios.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Parallel and Distributed Computing
ISSN
0743-7315
e-ISSN
1096-0848
Volume of the periodical
199
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
105054
UT code for WoS article
001427916300001
EID of the result in the Scopus database
2-s2.0-85217804375