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Abstract:
Learn about high-level GPU programming in NumbaPro to reduce development time and produce high-performance data-parallel code with the ease of Python. This tutorial is for beginning to intermediate CUDA programmers who already know Python. In this tutorial, audience will learn about (1) high-level Python decorators that turn simple Python functions into data-parallel GPU kernels without any knowledge of the CUDA architecture; (2) CUDA library bindings that can be used as a drop-in to speedup existing applications; and, (3) reuse existing CUDA-C/C++ code in Python with JIT Linking.
Learn about high-level GPU programming in NumbaPro to reduce development time and produce high-performance data-parallel code with the ease of Python. This tutorial is for beginning to intermediate CUDA programmers who already know Python. In this tutorial, audience will learn about (1) high-level Python decorators that turn simple Python functions into data-parallel GPU kernels without any knowledge of the CUDA architecture; (2) CUDA library bindings that can be used as a drop-in to speedup existing applications; and, (3) reuse existing CUDA-C/C++ code in Python with JIT Linking.  Back
 
Topics:
Programming Languages
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4413
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Abstract:

NumbaPro is a powerful compiler that takes high-level Python code directly to the GPU producing fast-code that is the equivalent of programming in a lower-level language. It contains an implementation of CUDA Python as well as higher-level constructs that make it easy to map array-oriented code to the parallel architecture of the GPU.

NumbaPro is a powerful compiler that takes high-level Python code directly to the GPU producing fast-code that is the equivalent of programming in a lower-level language. It contains an implementation of CUDA Python as well as higher-level constructs that make it easy to map array-oriented code to the parallel architecture of the GPU.

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Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2013
Session ID:
SC3121
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Abstract:

NumbaPro which is part of the Anaconda Python distribution from Continuum analytics provides support for programming the GPU from the high-level language Python. There are two APIs. The first provides a high-level functional approach wherein NumPy array expressions can be compiled automatically to execute in parallel on the GPU. This API can also vectorize scalar functions to operate on arrays stored on the GPU. The low-level API provides CUDA support in Python. This "CUDA-Python" dialect makes it easier to access shared-memory and synchronization primitives directly using a simplified Python syntax. Together NumbaPro provides an easier interface for unleashing the power of GPUs using Python with NumPy arrays. (Coauthored by Siu Kwan Lam).

NumbaPro which is part of the Anaconda Python distribution from Continuum analytics provides support for programming the GPU from the high-level language Python. There are two APIs. The first provides a high-level functional approach wherein NumPy array expressions can be compiled automatically to execute in parallel on the GPU. This API can also vectorize scalar functions to operate on arrays stored on the GPU. The low-level API provides CUDA support in Python. This "CUDA-Python" dialect makes it easier to access shared-memory and synchronization primitives directly using a simplified Python syntax. Together NumbaPro provides an easier interface for unleashing the power of GPUs using Python with NumPy arrays. (Coauthored by Siu Kwan Lam).

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Topics:
Programming Languages, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3462
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Abstract:

GPUs can offer orders of magnitude speed-ups for certain calculations, but programming the GPU remains difficult. Using NVIDIA's new support of LLVM, Continuum Analytics has built an array-oriented compiler for Python called Numba that can target the GPU. In this talk, I will demonstrate how Numba makes programming the GPU as easy as a one-line change to working Python code.

GPUs can offer orders of magnitude speed-ups for certain calculations, but programming the GPU remains difficult. Using NVIDIA's new support of LLVM, Continuum Analytics has built an array-oriented compiler for Python called Numba that can target the GPU. In this talk, I will demonstrate how Numba makes programming the GPU as easy as a one-line change to working Python code.

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Topics:
Programming Languages
Type:
Talk
Event:
Supercomputing
Year:
2012
Session ID:
SC2037
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