GPU Training Posted on 18 January 2012

Organizer: The Irish Centre for High-End computing (ICHEC), NVIDIA, Applied Parallel Computing Ltd.

Host: Dublin City University (DCU) at SCI-SYM (the centre for Scientific Computing and Complex Systems Modelling)

Registration deadline: 27th January

Registration policy: free of charge, previous registration by e-mail is requiredComputer architectures have undergone a significant paradigm shift recently, with the advent of multi- and many-core systems delivering tens to many thousands of concurrent hardware processing elements per workstation or supercomputer node. In particular, NVIDIA GPU (Graphics Processor Unit) technology has attracted significant attention of late in the field of High Performance Computing (HPC). The latest improvements in the CUDA (Compute Unified Device Architecture) software development capability have made it possible for scientists to achieve significant speedup in a wide range of research areas. To promote the use of this technology in Ireland, the Irish Centre for High-End Computing (ICHEC) and NVIDIA are co-organizing an introductory course to GPU computing and CUDA language. A 2-day course will be presented for 20 participants who will have the opportunity to learn how to program massively parallel processors such as NVIDIA GPUs. Both lectures and practical sessions will be held at Dublin City University (DCU) on the 6th and 7th  of February, facilitated by the SCI-SYM centre. The course is fully sponsored by NVIDIA and is therefore free of charge for students and post-docs. Only 20 places are available and applicants will be selected on a "first come, first served" basis. However, some proficiency in scientific programming using C is required (see application requirements below). Knowledge of parallel programming is considered a plus. ICHEC staff may refuse some applicants on these grounds. A detailed program will be announced in due course.

Send your application to training@ichec.ie.

Application requirements:
1) Your contact details.
2) The name of your PI.
3) A brief paragraph describing motivations to approach GPU computing.
4) A brief paragraph describing your experience in scientific / parallel programming.