Today NVIDIA announced a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that enables even the largest companies to analyze massive amounts of data and make accurate business predictions at unprecedented speed. Data analytics and machine learning are the largest segments of the high performance computing market that have not been accelerated — until now,” said Jensen Huang, founder and CEO of NVIDIA, who revealed RAPIDS in his keynote address at the GPU Technology Conference. “The world’s largest industries run algorithms written by machine learning on a sea of servers to sense complex patterns in their market and environment, and make fast, accurate predictions that directly impact their bottom line. "RAPIDS open-source software gives data scientists a giant performance boost as they address highly complex business challenges, such as predicting credit card fraud, forecasting retail inventory and understanding customer buying behavior. Reflecting the growing consensus about the GPU’s importance in data analytics, an array of companies is supporting RAPIDS — from pioneers in the open-source community, such as Databricks and Anaconda, to tech leaders like Hewlett Packard Enterprise, IBM and Oracle." Learn more: https://insidehpc.com/2018/10/open-source-rapids-gpu-platform-accelerate-predictive-data-analytics/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

1. THE NEW
HPC MARKET $9B
2005
SCIENTIFIC
COMPUTING

2. INTERNET
DEEP
LEARNING
SCIENTIFIC
COMPUTING
THE NEW
HPC MARKET
SCIENTIFIC
COMPUTING
$9B
2005 2018
CERN — Switzerland CSCS — Switzerland
DFKI — Germany Técnico Lisboa — Portugal
CUDA

3. INTERNET
THE NEW
HPC MARKET
SCIENTIFIC
COMPUTING
$9B
2005 2018
MACHINE
LEARNING
SCIENTIFIC
COMPUTING
RETAIL
HEALTHCARE
FINANCIAL
SERVICES
LOGISTICS
TELECOM
AD TECH
$36B
HADOOP
NUMPY
SKL
PANDAS
DEEP
LEARNING

4. 1991 | Guido van Rossum
Interpreted language emphasizing readability
2006 | Travis Oliphant
Multi-dimensional arrays, math functions
2010 | Inria
Machine learning library
2008 | Wes McKinney
Data manipulation and analysis
NUMPY
PYTHON
PANDAS SKLEARN
THE DEFACTO DATA SCIENCE PLATFORM

5. Matthew Rocklin
Parallel processing in Python data analytics
Dynamic task scheduling
Collection of parallel arrays, data frames, lists
NUMPY
PYTHON
PANDAS SKLEARN
DASK
NUMPY
PYTHON
PANDAS SKLEARN
THE DEFACTO DATA SCIENCE PLATFORM

6. CUDA
PYTHON
CUDF
“PANDAS-LIKE”
CUML
“SKLEARN-LIKE”
ARROW
DASK
NUMPY
PYTHON
PANDAS SKLEARN
DASK
NUMPY
PYTHON
PANDAS SKLEARN 2016 | Wes McKinney
Cross-language platform for in-memory data
Columnar memory format
Vectorized execution engine
Zero-copy IPC
Designed with GPU in mind
ANNOUNCING RAPIDS — ACCELERATED DATA SCIENCE

7. CUDA
PYTHON
ARROW
DASK
NUMPY
PYTHON
PANDAS SKLEARN
DASK
NUMPY
PYTHON
PANDAS SKLEARN
DEEP LEARNING
FRAMEWORKS
CUDNN
RAPIDS
CUMLCUDF CUGRAPH
ANNOUNCING RAPIDS — ACCELERATED DATA SCIENCE

8. CUDA
PYTHON
ARROW
DASK
DEEP LEARNING
FRAMEWORKS
CUDNN
RAPIDS
CUMLCUDF CUGRAPH
ANNOUNCING RAPIDS — ACCELERATED DATA SCIENCE
2010 | Matei Zaharia, CTO, Databricks
Unified analytics cluster-computing framework for
big data and machine learning
Spark Streaming GPU Support
cuML and cuGraph with Spark 2.4
Batch cuDF with Spark 2.4

9. ENTERPRISE-SCALE
DATA SCIENCE
DGX-2
DGX-1
DGX STATION
RTX 8000
TESLA V100
512GB
256GB
128GB
96GB
32GB

10. NEW
NVIDIA DGX-2
THE LARGEST GPU EVER CREATED
2 PFLOPS
512GB HBM2
16 TB/sec Memory Bandwidth
10 kW | 160 kg

12. 0 3600 7200 10800
DGX-2
100 CPU Nodes
50 CPU Nodes
20 CPU Nodes
ETL ML
FASTER SPEEDS, REAL WORLD BENEFITS
ETL ML End-to-End
SECONDS
0 600 1200 1800 2400 3000 3600
DGX-2
100 CPU Nodes
50 CPU Nodes
20 CPU Nodes
SECONDSSECONDS
2 Hours 1 Hour 3 Hours
0 1800 3600 5400 7200
DGX-2
100 CPU Nodes
50 CPU Nodes
20 CPU Nodes

14. TRADITIONAL
DATA SCIENCE CLUSTER
300 Servers
$3M
180 kW

15. GPU-ACCELERATED
MACHINE LEARNING CLUSTER
DGX-2 AND RAPIDS FOR PREDICTIVE ANALYTICS
1 DGX-2
10 kW
1/8 the Cost 1/15 the Space 1/18 the Power

16. ACCELERATING MACHINE
LEARNING WITH RAPIDS
RAPIDS software has immensely improved how we use data —
enabling the most complex models to run at scale and deliver
even more accurate forecasting.”
- Jeremy King, EVP & CTO
“

17. BRINGING RAPIDS-OPTIMIZED
SYSTEMS TO ENTERPRISES
WORLDWIDE
We are excited to partner with NVIDIA on RAPIDS to accelerate
the application of data science and machine learning to help
our customers drive faster and more insightful outcomes.”
- Antonio Neri, CEO
“

18. INTEGRATING RAPIDS TO
ACCELERATE DATA SCIENCE AND
MACHINE LEARNING PLATFORMS
We look forward to extending our successful partnership with
NVIDIA, leveraging RAPIDS to provide new machine learning
tools for our clients.”
- Arvind Krishna, SVP, Hybrid Cloud and Director of IBM Research
“

19. ACCELERATING ORACLE CLOUD
INFRASTRUCTURE WITH NVIDIA
HGX-2 AND RAPIDS
RAPIDS software runs seamlessly on the Oracle Cloud, allowing
customers to support their HPC, AI and data science needs,
all while taking advantage of the portfolio of GPU instances
available on Oracle Cloud Infrastructure.”
- Clay Magouyrk, SVP, Software Development, Oracle Cloud Infrastructure
“

20. NVIDIA ACCELERATED HPC PLATFORM
DENSE HPC NVIDIA HPC ACCELERATION STACKS HYPERSCALE HPC
SCIENCE
CUDA
DL TRAINING
cuDNN
DL INFERENCE
New TRT Hyperscale
MACHINE LEARNING
New RAPIDS