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2018 GTC San Jose

S8418 - GPU-Accelerated Semantic Similarity Search at Scale

Session Speakers
Session Description

Learn how to compute a high-quality approximation of the state-of-the-art text-similarity measure "Word Mover's Distance" on massive datasets using a novel algorithm developed at IBM Research - Zurich. Our algorithm has linear time complexity, requires a limited amount of working memory, and maps well into standard dense and sparse linear algebra routines. Therefore, it is very suitable for GPU acceleration! In addition, the algorithm is data parallel and exhibits a perfect weak scaling or strong scaling behavior when distributed across several GPUs. In practice, our algorithm renders the high-quality semantic-search results offered by Word Mover's Distance applicable to massive datasets. We'll also demonstrate applications of our algorithm in clustering, classification, and querying of news entries that are collected in real time from various data sources.


Additional Information
Accelerated Analytics, Speech and Language Processing
Financial Services Industry, Healthcare & Life Sciences
Intermediate technical
Talk
50 minutes
Session Schedule