Friday 29th May
G8 Main Building
Join NVIDIA experts for a free, in-depth seminar that features new NVIDIA Tesla hardware* and the NVIDIA Tesla Personal Supercomputer.
The Tesla Personal Supercomputer is powered by up to 960 parallel processing cores to solve the most complex computational problems – all in a form factor the size of a standard desktop workstation.
You will also hear from leading academic and industry experts about how the CUDA parallel computing architecture has enabled them to utilize C with NVIDIA GPUs to create computationally-intensive applications with amazing results.
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Standard C language for parallel application development on the GPU
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Standard numerical libraries for FFT (Fast Fourier Transform) and BLAS (Basic Linear Algebra Subroutines)
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Dedicated CUDA driver for computing with fast data transfer path between GPU and CPU
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CUDA driver interoperates with OpenGL and DirectX graphics drivers
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Support for Linux 32/64-bit and Windows XP 32/64-bit operating systems
Now you can take advantage of this breakthrough in computational research with the Tesla Personal Supercomputer bringing never before possible levels of computing, right to your fingertips. The Tesla Personal Supercomputer is powered by up to 960 parallel processing cores to solve the most complex computational problems – all in a form factor the size of a standard desktop workstation.
Join us at this seminar as we explore the increasingly important role that CUDA is playing for parallel computation and how the Tesla Personal Supercomputer now offers supercomputing for the masses.
Agenda:
13:00 Arrival and lunch
13:30 Introduction to Tesla hardware and system architecture, CUDA Programming update, CUDA Research applications, hands on demonstrations
16:30 Close and drinks
Register here.
Please contact Karen Newman-Brown or John Williams if you would like further information.
* NVIDIA® CUDA™ is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to solve many complex computational problems in a fraction of the time required on a CPU. It includes the CUDA Instruction Set Architecture (ISA) and the parallel compute engine in the GPU. To program to the CUDATM architecture, developers can, today, use C, one of the most widely used high-level programming languages, which can then be run at great performance on a CUDATM enabled processor. Other languages will be supported in the future, including FORTRAN and C++.
With over 100 million CUDA-enabled GPUs sold to date, thousands of software developers are already using the free CUDA software development tools to solve problems in a variety of professional and home applications – from video and audio processing and physics simulations, to oil and gas exploration, product design, medical imaging, and scientific research.
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