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The University of Southampton
Interdisciplinary Research Excellence

Complexity Systems Simulation Seminar Series (CS^4)  Event

Complexity Seminar Series (CS4)
Time:
15:00 - 18:00
Date:
10 October 2012
Venue:
Highfield Campus, Building 53 room 4025a/b, University of Southampton.

For more information regarding this event, please telephone Alison Simmance on +44 (0) 23 8059 3244 or email A.Simmance@southampton.ac.uk .

Event details

You are warmly invited to attend the Complex Systems Simulation Seminar Series (CS^4) 2012/13.

The CS4 seminar series 2012/13 will take place fortnightly on Wednesday afternoons at 3pm-6pm, building 53 room 4025 and will be pitched for a general academic audience from a wide variety of backgrounds.

 

Seminar 1: 10th October 2012

Title: General purpose GPU computing: Transforming your desktop into a personal super-computer

By Thomas Nowotny

 

Abstract:

Driven by the ever-increasing computational demands of the games industry graphical processing units (GPUs) have developed from simple co-processors to powerful compute platforms. Unlike modern CPUs that are still essentially using a sequential model of computing, GPUs are massively parallel with half a thousand cores on a single chip in modern graphics cards. With the introduction of the CUDA (common unified device architecture) application programming interface, NVIDIA, one of the leading GPU manufacturers, has made their GPUs accessible to general purpose computing applications. Given a suitable computational problem and an optimized parallel implementation, modern GPUs can achieve speed-ups of up to 50 to 100 times over a classical CPU core of a recent CPU. 

In this talk I will introduce the opportunities and challenges of general purpose GPU computing and illustrate them with the GPU enhanced neuronal network (GeNN) framework we are developing at Sussex. I will argue that building meta-compilers that generate code from a simpler domain-specific problem description is one of the better approaches to GPU programming. GeNN is based on such an approach which offers many decisive advantages:

(i) The system can provide for a large number of potential model elements,

(ii) it can optimise for specific model structures and GPU hardware properties at compile time, and

(iii) it can be extended more easily than pre-compiled solutions.

The present alpha version of GeNN shows encouraging competitive computing performance and is available under the GPL v2 licenses at http://genn.sourceforge.net.

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