Name
Abstract | ||
|---|---|---|
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 to compute 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 is being resolved). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25087-8_27 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
GPU,CUDA,Dynamic programming,Edit distance,Dynamic time warping | Edit distance,Dynamic programming,Similarity query,Dynamic time warping,Computer science,Massively parallel,Matrix (mathematics),CUDA,Parallel computing,Parallel processing,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9371 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Krulis | 1 | 76 | 13.27 |
| David Bednárek | 2 | 43 | 10.89 |
| Michal Brabec | 3 | 1 | 1.03 |