Introduction

GPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.

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ASPLOS 2008 Tutorial

CUDA: A Heterogeneous Parallel Programming Model for Manycore Computing

Modern GPUs provide a level of massively parallel computation that was once the preserve of supercomputers like the MasPar and Connection Machine. NVIDIA's Tesla architecture for GPU Computing provides a fully programmable, massively multithreaded chip with up to 128 scalar processor cores and capable of delivering hundreds of billions of operations per second. Researchers across many scientific and engineering disciplines are using this platform to accelerate important computations by up to 2 orders of magnitude.

In this tutorial, we will provide an overview of the Tesla architecture and explore the transition it represents in massively parallel computing: from the domain of supercomputers to that of commodity "manycore" hardware available to all. We will also introduce CUDA, a scalable parallel programming model and software environment for parallel programming. By providing a small set of readily understood extensions to the C/C++ languages, CUDA allows programmers to focus on writing efficient parallel algorithms without the burden of learning a multitude of new programming constructs. Finally, as the GPU is the only widely available commodity "manycore" chip available today, we will explore its importance as a research platform for exploring important issues in parallel programming and architecture.

Course Notes

Welcome to the course notes for the half-day ASPLOS 2008 CUDA Tutorial!

The tutorial was held at ASPLOS 2008 on Sunday, March 2, 2008.

David Luebke, NVIDIA Corporation

Michael Garland, NVIDIA Corporation
John Owens, University of California, Davis
Kevin Skadron, University of Virginia

• Introduction/Overview ("GPU Computing: The Democratization of Parallel Computing") (Luebke) (PDF)
• Trends in Multicore Architecture (Skadron) (PDF)
• Parallel Computing in CUDA (programming model and language) (Garland) (PDF)
• GPU Architecture & Applications (Luebke) (PDF)
• Advanced Data-Parallel Algorithms Programming: Data Structures and Algorithms (Owens) (PDF)
• Wrapup and Open Issues (Skadron) (PDF)

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Links

Other GPGPU Courses


• Supercomputing 2007
• SIGGRAPH 2007
• SIGGRAPH 2005
• SIGGRAPH 2004
• IEEE VIS 2005
• IEEE VIS 2004

High-Level Shading Languages


• Cg: C for Graphics
  
• HLSL: The D3D Shading Language
  
• The OpenGL Shading Language
  

GPGPU Languages


• Sh - University of Waterloo
  
• Brook - Stanford University
  

Shader Conversion


• Babelshader
  

Debugging Tools


• imdebug
  
• Shadesmith
  

Utility Code


• Framebuffer Object Class
  
• RenderTexture