TY - GEN
T1 - Improving the performance for the range search on metric spaces using a multi-GPU platform
AU - Uribe-Paredes, Roberto
AU - Arias, Enrique
AU - Sánchez, José L.
AU - Cazorla, Diego
AU - Valero-Lara, Pedro
PY - 2012
Y1 - 2012
N2 - Nowadays, similarity search is becoming a field of increasing interest because these kinds of methods can be applied to different areas in science and engineering, for instance, pattern recognition, information retrieval, etc. This search is carried out over metric indexes decreasing the number of distance evaluations during the search process, improving the efficiency of this process. However, for real applications, when processing large volumes of data, query response time can be quite high. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. Modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, we show a simple and fast implementation of similarity search method on a Multi-GPU platform. The main contributions are mainly the definition of a generic metric structure more suitable for GPU platforms, the efficient usage of GPU memory system and the implementation of the method in a Multi-GPU platform.
AB - Nowadays, similarity search is becoming a field of increasing interest because these kinds of methods can be applied to different areas in science and engineering, for instance, pattern recognition, information retrieval, etc. This search is carried out over metric indexes decreasing the number of distance evaluations during the search process, improving the efficiency of this process. However, for real applications, when processing large volumes of data, query response time can be quite high. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. Modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, we show a simple and fast implementation of similarity search method on a Multi-GPU platform. The main contributions are mainly the definition of a generic metric structure more suitable for GPU platforms, the efficient usage of GPU memory system and the implementation of the method in a Multi-GPU platform.
KW - Multi-GPU platforms
KW - Range queries
KW - metric spaces
KW - parallel processing
KW - similarity search
UR - http://www.scopus.com/inward/record.url?scp=84866052888&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32597-7_39
DO - 10.1007/978-3-642-32597-7_39
M3 - Conference contribution
AN - SCOPUS:84866052888
SN - 9783642325960
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 442
EP - 449
BT - Database and Expert Systems Applications - 23rd International Conference, DEXA 2012, Proceedings
T2 - 23rd International Conference on Database and Expert Systems Applications, DEXA 2012
Y2 - 3 September 2012 through 6 September 2012
ER -