Abstract
Nowadays general-purpose computing on graphics processing units (GPGPUs) performs computations what were formerly handled by the CPU using hundreds of cores on GPUs. It often improves the performance of sequential computation when the running program is well-structured and formulated for massive threading. The CYK algorithm is a well-known algorithm for the context-free language membership test and has been used in many applications including grammar inferences, compilers and natural language processing. We revisit the CYK algorithm and its structural properties suitable for parallelization. Based on the discovered properties, we then parallelize the algorithm using different combinations of memory types and data allocation schemes using a GPU. We evaluate the algorithm based on real-world data and herein demonstrate the performance improvement compared with CPU-based computations.
| Original language | English |
|---|---|
| Title of host publication | Network and Parallel Computing - 11th IFIP WG 10.3 International Conference, NPC 2014, Proceedings |
| Publisher | Springer Verlag |
| Pages | 157-168 |
| Number of pages | 12 |
| ISBN (Print) | 9783662449165 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 11th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2014 - Ilan, Taiwan, Province of China Duration: 2014 Sep 18 → 2014 Sep 20 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8707 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 11th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2014 |
|---|---|
| Country | Taiwan, Province of China |
| City | Ilan |
| Period | 14/9/18 → 14/9/20 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Parallel CYK membership test on GPUs. / Kim, Kyoung Hwan; Choi, Sang Min; Lee, Hyein; Man, Ka Lok; Han, Yo Sub.
Network and Parallel Computing - 11th IFIP WG 10.3 International Conference, NPC 2014, Proceedings. Springer Verlag, 2014. p. 157-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8707 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Parallel CYK membership test on GPUs
AU - Kim, Kyoung Hwan
AU - Choi, Sang Min
AU - Lee, Hyein
AU - Man, Ka Lok
AU - Han, Yo Sub
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Nowadays general-purpose computing on graphics processing units (GPGPUs) performs computations what were formerly handled by the CPU using hundreds of cores on GPUs. It often improves the performance of sequential computation when the running program is well-structured and formulated for massive threading. The CYK algorithm is a well-known algorithm for the context-free language membership test and has been used in many applications including grammar inferences, compilers and natural language processing. We revisit the CYK algorithm and its structural properties suitable for parallelization. Based on the discovered properties, we then parallelize the algorithm using different combinations of memory types and data allocation schemes using a GPU. We evaluate the algorithm based on real-world data and herein demonstrate the performance improvement compared with CPU-based computations.
AB - Nowadays general-purpose computing on graphics processing units (GPGPUs) performs computations what were formerly handled by the CPU using hundreds of cores on GPUs. It often improves the performance of sequential computation when the running program is well-structured and formulated for massive threading. The CYK algorithm is a well-known algorithm for the context-free language membership test and has been used in many applications including grammar inferences, compilers and natural language processing. We revisit the CYK algorithm and its structural properties suitable for parallelization. Based on the discovered properties, we then parallelize the algorithm using different combinations of memory types and data allocation schemes using a GPU. We evaluate the algorithm based on real-world data and herein demonstrate the performance improvement compared with CPU-based computations.
UR - http://www.scopus.com/inward/record.url?scp=84906738074&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906738074&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-44917-2_14
DO - 10.1007/978-3-662-44917-2_14
M3 - Conference contribution
AN - SCOPUS:84906738074
SN - 9783662449165
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 168
BT - Network and Parallel Computing - 11th IFIP WG 10.3 International Conference, NPC 2014, Proceedings
PB - Springer Verlag
ER -