Exploiting the vast horse power of contemporary GPUs for general purpose applications has become a must for any realtime or interactive application nowadays. Current computer games use the GPUs not only for rendering graphics, but also for collision detection, physics, or artificial intelligence. General purpose computing on GPUs (GPGPU) has also penetrated the field of scientific computing enabling realtime experience of large scale fluid simulations, medical visualization, or signal processing.

This lecture introduces the concepts of programming graphics cards for non-graphical applications, such as data sorting, image filtering (e.g. denoising, sharpening), or physically based simulations. During the course, students familiarize themselves with the architecture of the current GPUs (NVIDIA GTX 480, ATI Radeon 5870) and develop parallel programming skills using the OpenCL and CUDA language.

The practical assignments will cover the following topics:

• The architecture of contemporary GPUs (execution model, memory model, etc.)
• Introduction to the OpenCL programming language
• Introduction to the CUDA programming language
• Reduction, scanning, and sorting parallel algorithms
• Efficient parallel algebraic operations (matrix multiplication, linear equations)
• Image filtering via separable convolution kernels
• Physical simulation (cloth simulation using particles and springs)
• Introduction to optimization and profiling on the GPU