GPU-Accelerated Computing Reaches Next Generation of Programmers With Python Support of NVIDIA CUDA

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
compiler infrastructure. 
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. 
About CUDA
 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
GPU-accelerated computing.  
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
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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
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detailed from time to time in the reports NVIDIA files with the
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for the fiscal period ended January 27, 2013. Copies of reports filed
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NVIDIA logo, CUDA and Tesla are trademarks and/or registered
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For further information, contact:
George Millington
NVIDIA Corporation
(408) 562-7226 
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