Improving Parallel Processing of Matrix-based Similarity Measures on Modern GPUs

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10301907" target="_blank" >RIV/00216208:11320/15:10301907 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-25087-8_27" target="_blank" >http://dx.doi.org/10.1007/978-3-319-25087-8_27</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-25087-8_27" target="_blank" >10.1007/978-3-319-25087-8_27</a>

Result language
angličtina
Original language name
Improving Parallel Processing of Matrix-based Similarity Measures on Modern GPUs
Original language description
Dynamic programming techniques are well-established and employed by various practical algorithms which are used as similarity measures, for instance the edit-distance algorithm or the dynamic time warping algorithm. These algorithms usually operate in iteration-based fashion where new values are computed from values of the previous iteration, thus they cannot be processed by simple data-parallel approaches. In this paper, we propose a way how to utilize computational power of massively parallel GPUs tocompute dynamic programming algorithms effectively and efficiently. We address both the problem of computing one distance on large inputs concurrently and the problem of computing large number of distances simultaneously (e.g., when a similarity query isbeing resolved).
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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