GPU Computing: Programming a Massively Parallel Processor

Summary form only given. Many researchers have observed that general purpose computing with programmable graphics hardware (GPUs) has shown promise to solve many of the world´s compute intensive problems, many orders of magnitude faster the conventional CPUs. The challenge has been working within the constraints of a graphics programming environment and limited language support to leverage this huge performance potential. GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems, transforming the GPU into a massively parallel processor. The NVIDIA C-compiler for the GPU provides a complete development environment that gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. In this talk, I will provide a description of how CUDA can solve compute intensive problems and highlight the challenges when compiling parallel programs for GPUs including the differences between graphics shaders vs. CUDA applications