Evaluating the jaccard-tanimoto index on multi-core architectures. Sachdeva, V., Freimuth, D., M., & Mueller, C. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5544 LNCS(PART 1):944-953, 2009. ![Evaluating the jaccard-tanimoto index on multi-core architectures [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The Jaccard/Tanimoto coefficient is an important workload, used in a large variety of problems including drug design fingerprinting, clustering analysis, similarity web searching and image segmentation. This paper evaluates the Jaccard coefficient on three platforms: the Cell Broadband Engine TMprocessor Intel ®Xeon ®dual-core platform and Nvidia ®8800 GTX GPU. In our work, we have developed a novel parallel algorithm specially suited for the Cell/B.E. architecture for all-to-all Jaccard comparisons, that minimizes DMA transfers and reuses data in the local store. We show that our implementation on Cell/B.E. outperforms the implementations on comparable Intel platforms by 6-20X with full accuracy, and from 10-50X in reduced accuracy mode, depending on the size of the data, and by more than 60X compared to Nvidia 8800 GTX. In addition to performance, we also discuss in detail our efforts to optimize our workload on these architectures and explain how avenues for optimization on each architecture are very different and vary from one architecture to another for our workload. Our work shows that the algorithms or kernels employed for the Jaccard coefficient calculation are heavily dependent on the traits of the target hardware. © 2009 Springer Berlin Heidelberg.
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abstract = {The Jaccard/Tanimoto coefficient is an important workload, used in a large variety of problems including drug design fingerprinting, clustering analysis, similarity web searching and image segmentation. This paper evaluates the Jaccard coefficient on three platforms: the Cell Broadband Engine TMprocessor Intel ®Xeon ®dual-core platform and Nvidia ®8800 GTX GPU. In our work, we have developed a novel parallel algorithm specially suited for the Cell/B.E. architecture for all-to-all Jaccard comparisons, that minimizes DMA transfers and reuses data in the local store. We show that our implementation on Cell/B.E. outperforms the implementations on comparable Intel platforms by 6-20X with full accuracy, and from 10-50X in reduced accuracy mode, depending on the size of the data, and by more than 60X compared to Nvidia 8800 GTX. In addition to performance, we also discuss in detail our efforts to optimize our workload on these architectures and explain how avenues for optimization on each architecture are very different and vary from one architecture to another for our workload. Our work shows that the algorithms or kernels employed for the Jaccard coefficient calculation are heavily dependent on the traits of the target hardware. © 2009 Springer Berlin Heidelberg.},
bibtype = {article},
author = {Sachdeva, V and Freimuth, D M and Mueller, C},
doi = {10.1007/978-3-642-01970-8_95},
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