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GTC ON-DEMAND

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Abstract:
We'll talk about how we built a GPU-Accelerated system for real-time information retrieval from large datasets in life sciences. Unstructured textual data is full of phrases and words that have multiple meanings, making it difficult for current information-retrieval algorithms to find relevant documents. We'll describe our knowledge graph-based filtering mechanism for more precise real-time information retrieval. We outline how we accelerated the embedding generation process, treating it as an optimization problem and running it on NVIDIA Tesla V100 GPU cores. We'll also cover how we reduced the latency in distance computation using TensorRT.
We'll talk about how we built a GPU-Accelerated system for real-time information retrieval from large datasets in life sciences. Unstructured textual data is full of phrases and words that have multiple meanings, making it difficult for current information-retrieval algorithms to find relevant documents. We'll describe our knowledge graph-based filtering mechanism for more precise real-time information retrieval. We outline how we accelerated the embedding generation process, treating it as an optimization problem and running it on NVIDIA Tesla V100 GPU cores. We'll also cover how we reduced the latency in distance computation using TensorRT.  Back
 
Topics:
AI Application, Deployment & Inference, Speech & Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9359
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