Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th International Conferences on Pervasive Patterns and Applications |
| Publisher | International Academy, Research, and Industry Association (IARIA) |
| ISBN (Print) | 9781612085340 |
| Publication status | Published - 28 Feb 2017 |
| Event | PATTERNS 2017: The Ninth International Conferences on Pervasive Patterns and Applications - Athens, Greece Duration: 19 Feb 2016 → 23 Feb 2016 |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 2308-3557 |
Conference
| Conference | PATTERNS 2017 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 19/02/16 → 23/02/16 |
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Trie compression for GPU accelerated multi-pattern matching. / Bellekens, Xavier; Seeam, Amar; Tachtatzis, Christos; Atkinson, Robert.
Proceedings of the 9th International Conferences on Pervasive Patterns and Applications. International Academy, Research, and Industry Association (IARIA), 2017.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Trie compression for GPU accelerated multi-pattern matching
AU - Bellekens, Xavier
AU - Seeam, Amar
AU - Tachtatzis, Christos
AU - Atkinson, Robert
PY - 2017/2/28
Y1 - 2017/2/28
N2 - Graphics Processing Units (GPU) allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory. In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less Aho-Corasick, whilst demonstrating over 22 Gbps throughput. The algorithm presented takes advantage of compressed row storage matrices as well as shared and texture memory on the GPU.
AB - Graphics Processing Units (GPU) allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory. In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less Aho-Corasick, whilst demonstrating over 22 Gbps throughput. The algorithm presented takes advantage of compressed row storage matrices as well as shared and texture memory on the GPU.
M3 - Conference contribution
SN - 9781612085340
BT - Proceedings of the 9th International Conferences on Pervasive Patterns and Applications
PB - International Academy, Research, and Industry Association (IARIA)
ER -