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THE PREMIER AI CONFERENCE
NVIDIA’s GPU Technology Conference (GTC) is a global
conference series providing training, insights, and
direct access to experts on the hottest topics in
LEADING INSIGHTS IN DATA SCIENCE
AND MACHINE LEARNING
SEE THE TOP 5 SESSIONS ON
DATA SCIENCE FROM GTC.
TOP 5 DATA SCIENCE SESSIONS
Gartner identifies AI as top megatrend
Deep learning is changing the world of sports
Opinion: “I was worried about AI until it saved
PayPal uses AI to protect payments and performance
AI predicts melting of sea ice to save communities
#1 RAPIDS CUDA DATAFRAME INTERNALS FOR C++ DEVELOPERS
#2 HOW WALMART IMPROVES FORECAST ACCURACY WITH NVIDIA
#3 CONTEXT-AWARE NETWORK MAPPING AND ASSET
CLASSIFICATION IN CYBER SECURITY
#4 END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL DATASETS IN
#5 ACCELERATING GRAPH ALGORITHMS WITH RAPIDS
RAPIDS CUDA DATAFRAME INTERNALS
FOR C++ DEVELOPERS
The core of RAPIDS is CUDA DataFrame (cuDF), a
library that provides Pandas-like DataFrame (a
columnar data structure) functionality with GPU
acceleration. cuDF provides a Python interface for
use in existing data science workflows, and
underneath cuDF is libcuDF, an open-source CUDA
C++ library that provides a column data structure
and algorithms to operate on these columns, such
as filtering, selection, sorting, joining, and groupby.
If you are interested in using GPU DataFrames via
libcuDFs C/C++ interface, or if you are interested in
contributing to the cuDF / libcuDF open source
project, then this talk is for you.
HOW WALMART IMPROVES FORECAST ACCURACY
WITH NVIDIA GPUS
In this talk we will show how GPU computing has
enabled us to significantly improve forecast
accuracy, and highlight the key bottlenecks that we
have been able to overcome. We will provide
runtime comparisons of CPU vs GPU-based
algorithms on our real-world problems, and
describe how GPU-based development works for us
(hint: its easy to do.) We will also describe our
collaboration with NVIDIA, who have been
extremely helpful, continuously refining their
algorithms and tools to better meet the needs of
industry, and what tools and capabilities we see
being especially useful for our path forward.
CONTEXT-AWARE NETWORK MAPPING AND ASSET
CLASSIFICATION IN CYBER SECURITY
Cybersecurity present unique challenges and
need for fast iteration and quick exploration.
We'll show how to leverage RAPIDS and GPU-
accelerated data science to learn a network
mapping from passively generated logs. We'll
explain how near real-time ingest and
processing capabilities allow us to visualize
the network quickly and provide context to
the security professional in a timely manner.
END-TO-END ANALYSIS OF LARGE 3D GEOSPATIAL
DATASETS IN RAPIDS
Location intelligence is key to understanding
areas such as property insights, environmental
monitoring, disaster management and
prevention, traffic flows, and customer behavior.
We'll describe how we used RAPIDS and cover our
entire process, from processing raw data,
merging sources, generating and labeling
colorized voxel cubes for training, to model
building, inference, and final application.
ACCELERATING GRAPH ALGORITHMS WITH
Graphs are a ubiquitous part of technology
we use daily in systems like GPS graphs help
find the shortest path between two points
and in social networks, which use them to
help users find friends. We'll explain why
analyzing these vast networks with possibly
billions of entries requires the computing
power of GPUs. RAPIDS version 0.6 includes
the first official release of cuGraph.
STAY CURRENT ON THE
LATEST INNOVATIONS IN
DATA SCIENCE AND RAPIDS
RAPIDS DATA SCIENCE