GPU-Accelerated Computing Reaches Next Generation of Programmers With Python
Support of NVIDIA CUDA
Python Productivity With GPU Performance Enables Coming Wave of HPC
and Data Analytics Applications
SAN JOSE, CA -- (Marketwire) -- 03/18/13 -- GTC 2013 -- The growing
ranks of programmers using the Python open-source language can now
take full advantage of GPU acceleration for their high performance
computing (HPC) and big data analytics applications by using the
NVIDIA(R) CUDA(R) parallel programming model, NVIDIA today announced.
Easy to learn and use, Python is among the top 10 programming
languages with more than three million users. It enables users to
write high-level software code that captures their algorithmic ideas
without delving deep into programming details. Python's extensive
libraries and advanced features make it ideal for a broad range of
HPC science, engineering and big data analytics applications.
Support for NVIDIA CUDA parallel programming comes from NumbaPro, a
Python compiler in the new Anaconda Accelerate product from Continuum
"Hundreds of thousands of Python programmers will now be able to
leverage GPU accelerators to improve performance on their
applications," said Travis Oliphant, co-founder and CEO at Continuum
Analytics. "With NumbaPro, programmers have the best of both worlds:
they can take advantage of the flexibility and high productivity of
Python with the high performance of NVIDIA GPUs."
Expanded Access to Accelerated Computing Via LLVM
This new support
for GPU-accelerated application development is the result of NVIDIA's
contribution of the CUDA compiler source code into the core and
parallel thread execution backend of LLVM, a widely used open source
Continuum Analytics' Python development environment uses LLVM and the
NVIDIA CUDA compiler software development kit to deliver
GPU-accelerated application capabilities to Python programmers.
The modularity of LLVM makes it easy for language and library
designers to add support for GPU acceleration to a wide range of
general-purpose languages like Python, as well as to domain-specific
programming languages. LLVM's efficient just-in-time compilation
capability lets developers compile dynamic languages like Python on
the fly for a variety of architectures.
"Our research group typically prototypes and iterates new ideas and
algorithms in Python and then rewrites the algorithm in C or C++ once
the algorithm is proven effective," said Vijay Pande, professor of
Chemistry and of Structural Biology and Computer Science at Stanford
University. "CUDA support in Python enables us to write performance
code while maintaining the productivity offered by Python."
Anaconda Accelerate is available for Continuum Analytics' Anaconda
Python offering, and as part of the Wakari browser-based data
exploration and code development environment.
CUDA is a parallel computing platform and programming
model developed by NVIDIA. It enables dramatic increases in computing
performance by harnessing the power of GPUs. With more than 1.7
million downloads, supporting more than 220 leading engineering,
scientific and commercial applications, the CUDA programming model is
the most popular way for developers to take advantage of
More information about NVIDIA CUDA GPUs is available at the NVIDIA
Tesla(R) GPU website. To learn more about CUDA or download the latest
version, visit the CUDA website.
Since 1993, NVIDIA (NASDAQ: NVDA) has pioneered the art
and science of visual computing. The company's technologies are
transforming a world of displays into a world of interactive
discovery -- for everyone from gamers to scientists, and consumers to
enterprise customers. More information at
http://nvidianews.nvidia.com and http://blogs.nvidia.com.
Certain statements in this press release including, but not limited
to, statements as to: the impact and benefits of GPU accelerators and
the NVIDIA CUDA parallel programming model; and the effects of the
company's patents on modern computing are forward-looking statements
that are subject to risks and uncertainties that could cause results
to be materially different than expectations. Important factors that
could cause actual results to differ materially include: global
economic conditions; our reliance on third parties to manufacture,
assemble, package and test our products; the impact of technological
development and competition; development of new products and
technologies or enhancements to our existing product and
technologies; market acceptance of our products or our partners'
products; design, manufacturing or software defects; changes in
consumer preferences or demands; changes in industry standards and
interfaces; unexpected loss of performance of our products or
technologies when integrated into systems; as well as other factors
detailed from time to time in the reports NVIDIA files with the
Securities and Exchange Commission, or SEC, including its Form 10-K
for the fiscal period ended January 27, 2013. Copies of reports filed
with the SEC are posted on the company's website and are available
from NVIDIA without charge. These forward-looking statements are not
guarantees of future performance and speak only as of the date
hereof, and, except as required by law, NVIDIA disclaims any
obligation to update these forward-looking statements to reflect
future events or circumstances.
Copyright 2013 NVIDIA Corporation. All rights reserved. NVIDIA, the
NVIDIA logo, CUDA and Tesla are trademarks and/or registered
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