PEARC20 has ended
Welcome to PEARC20!
PEARC20’s theme is “Catch the Wave.” This year’s theme embodies the spirit of the community’s drive to stay on pace and in front of all the new waves in technology, analytics, and a globally connected and diverse workforce. We look forward to this year’s PEARC20 virtual meeting, where we can share scientific discovery and craft the future infrastructure.

The conference will be held in Pacific Time (PT) and the times listed below are in Pacific Time.

The connection information for all PEARC20 workshops, tutorials, plenaries, track presentations, BOFs, Posters, Visualization Showcase, and other affiliated events, are in the PEARC20 virtual conference platform, Brella. If you have issues joining Brella, please email pearcinfo@googlegroups.com.
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Friday, July 31 • 8:00am - 12:00pm
Fundamentals of Accelerated Data Science with RAPIDS

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The open source RAPIDS project allows data scientists to GPU-accelerate end-to-end data science workflows and analytics applications, and leverage multiple GPUs for larger than memory datasets. Learn how to GPU-accelerate your data science applications by: Using cuDF (GPU-enabled Pandas-like dataframes) and Dask-cuDF to ingest and manipulate large datasets directly on the GPU in preparation for a variety of machine learning algorithms Utilizing GPU-accelerated machine learning algorithms (cuML) such as K-means and logistic regression, as well as cuGraph for graph analytics Understanding key differences between CPU-driven and GPU-driven data science, including API specifics and best practices for refactoring, learning techniques and approaches for end-to-end data science on GPUs Upon completion, you'll be able to refactor existing CPU-only data science workloads to run much faster on GPUs and perform a wide variety of end-to-end data science tasks using large datasets


Friday July 31, 2020 8:00am - 12:00pm PDT