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GTC ON-DEMAND

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Abstract:
This poster presents the design of a CUDA-GPU based parallel processing framework for GNU Octave. Octave is a high-level interpreted language, primarily intended for numerical computations. GNU Octave being an open source alternative to Matlab, is widely used in academic and research institutes. The GPU framework allows Octave users to accelerate their software written in Octave high-level M language on GPUs with minimal code modifications. To my knowledge, this is the first attempt to build a GPU framework for Octave, contrary to previous attempts to provide GPU variants for a set of Octave functions.
This poster presents the design of a CUDA-GPU based parallel processing framework for GNU Octave. Octave is a high-level interpreted language, primarily intended for numerical computations. GNU Octave being an open source alternative to Matlab, is widely used in academic and research institutes. The GPU framework allows Octave users to accelerate their software written in Octave high-level M language on GPUs with minimal code modifications. To my knowledge, this is the first attempt to build a GPU framework for Octave, contrary to previous attempts to provide GPU variants for a set of Octave functions.  Back
 
Topics:
Application Design & Porting Techniques
Type:
Poster
Event:
GTC Silicon Valley
Year:
2012
Session ID:
P2213
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Topics:
Tools & Libraries
Type:
Webinar
Event:
GTC Webinars
Year:
2012
Session ID:
GTCE022
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Speakers:
John Melonakos
Abstract:

In this tutorial, we will discuss AccelerEyes' Jacket software which connects MATLAB to the graphics processing unit (GPU). With the GPU as a backend computation engine, Jacket brings together the best of three important computational worlds: computational speed, visualization, and the user-friendliness of MATLAB programming. Jacket enables developers to write and run code on the GPU in the native M-Language used in MATLAB. Jacket accomplishes this by automatically wrapping the M-Language into a GPU compatible form. By simply casting input data to Jacket's GPU data structure, MATLAB functions are transformed into GPU functions. Jacket also preserves the interpretive nature of the M-Language by providing real-time, transparent access to the GPU compiler. The tutorial will provide examples of running MATLAB code on the GPU for image and signal processing, life science, finance, and other applications. Also, a Q/A session will enable audience members to ask specific questions about the Jacket project and MATLAB GPU computing.

In this tutorial, we will discuss AccelerEyes' Jacket software which connects MATLAB to the graphics processing unit (GPU). With the GPU as a backend computation engine, Jacket brings together the best of three important computational worlds: computational speed, visualization, and the user-friendliness of MATLAB programming. Jacket enables developers to write and run code on the GPU in the native M-Language used in MATLAB. Jacket accomplishes this by automatically wrapping the M-Language into a GPU compatible form. By simply casting input data to Jacket's GPU data structure, MATLAB functions are transformed into GPU functions. Jacket also preserves the interpretive nature of the M-Language by providing real-time, transparent access to the GPU compiler. The tutorial will provide examples of running MATLAB code on the GPU for image and signal processing, life science, finance, and other applications. Also, a Q/A session will enable audience members to ask specific questions about the Jacket project and MATLAB GPU computing.

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Topics:
Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2009
Session ID:
S09016
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