In this lecture we turn to the technology fundamentals behind the rise of the GPU: what are the technology trends of today's VLSI designs and how and why do they impact the GPU and its architecture? We also contrast CPUs and GPUs as well as the differences between task-parallel and time-multiplexed architectures.

The modern GPU can be used as a general-purpose processor. This field of "GPGPU" (general-purpose programmability of graphics hardware) or "GPU computing" is having an increasing impact on GPU architecture, GPU software and programming environments, and the computing industry. These two lectures discuss the fundamentals of GPGPU: the programming model, the hardware, and some fundamental algorithms. We use NVIDIA's CUDA and G80 architecture as a representative example.

The modern GPU can be used as a general-purpose processor. This field of "GPGPU" (general-purpose programmability of graphics hardware) or "GPU computing" is having an increasing impact on GPU architecture, GPU software and programming environments, and the computing industry. These two lectures discuss the fundamentals of GPGPU: the programming model, the hardware and some fundamental algorithms. We use NVIDIA's CUDA and G80 architecture as a representative example.

The graphics pipeline has recently added programmable stages. This lecture covers the software and hardware fundamentals of the GPU's programmable stages, in particular the vertex shader and fragment shader.

How do we measure graphics performance? How do we characterize graphics hardware? What are the bottlenecks in a graphics application and how do we detect them? What are benchmarks, what makes a good benchmark, and how do we use benchmarks?

What are different ways that we might consider doing rendering? Why did OpenGL make the decisions it did and what does the OpenGL pipeline look like?

Introduction to the course: why we should study graphics architecture, history of graphics architecture, overview of the course, administrivia.