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GTC ON-DEMAND

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Abstract:
We'll present a novel GPU implementation for batched GBM inferencing. We'll also present detailed performance comparison of our implementation against the state-of-the-art libraries such as XGBoost and Treelite. We'll then compare inference performance on various real-world datasets.
We'll present a novel GPU implementation for batched GBM inferencing. We'll also present detailed performance comparison of our implementation against the state-of-the-art libraries such as XGBoost and Treelite. We'll then compare inference performance on various real-world datasets.  Back
 
Topics:
AI Application, Deployment & Inference, Accelerated Data Science, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8873
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Abstract:
We present summary of optimization work on GPU-based Correlator pipeline code. This is an on-going effort jointly between National Center for Radio Astrophysics (NCRA) and NVIDIA. The central goal of the effort is to upgrade the Giant Meterwave Radio Telescope (GMRT) receiver with wide-band GPU-based back-end and extending this design as a proposal for back-end for the LOW frequency array of the SKA Telescope. We look at various processing stages involved in pipeline for exploring optimization possibilities with some interesting results already achieved.
We present summary of optimization work on GPU-based Correlator pipeline code. This is an on-going effort jointly between National Center for Radio Astrophysics (NCRA) and NVIDIA. The central goal of the effort is to upgrade the Giant Meterwave Radio Telescope (GMRT) receiver with wide-band GPU-based back-end and extending this design as a proposal for back-end for the LOW frequency array of the SKA Telescope. We look at various processing stages involved in pipeline for exploring optimization possibilities with some interesting results already achieved.  Back
 
Topics:
Astronomy & Astrophysics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5302
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