The graphics processor (GPU) on today's commodity video cards has evolved into an extremely powerful and flexible processor. Modern graphics architectures provide tremendous memory bandwidth and computational horsepower, with dozens of fully programmable shading units that support vector operations and IEEE floating point precision. High-level languages have emerged for graphics hardware, making this computational power accessible. GPGPU stands for "General-Purpose Computation on GPUs". GPGPU researchers have achieved over an order of magnitude speedup over modern CPUs on some non-graphics problems.This course provides detailed coverage of general-purpose computation on graphics hardware. We emphasize core computational building blocks, ranging from linear algebra to database queries, and review the tools, perils, and strategies in GPU programming. We present analysis of GPU performance characteristics, and use this analysis to provide insight into how to build efficient GPGPU algorithms. Finally we present a set of case studies on general-purpose applications of graphics hardware.

From signal processing to drug target modeling to fixed income derivatives pricing, computer simulations have become critical tools driving the innovation economy. This session discusses the performance boost provided by using stream processors to tackle these problems. Attendees will learn how they can increase the power, space and budget efficiency of computation clusters using stream processors. Stream processors are a new class of data parallel processor architectures that offer impressive performance-per-dollar and per-watt vs. general purpose processors. Today, stream processors have not been widely used by application developers due to programming difficulty and lack of efficient application development environments. By providing a substantial increase in computing power, you'll learn how to reduce the cost of computing infrastructure, increase developer productivity and dramatically increase your competitive advantage.