The document discusses using GPUs for general purpose computing. It provides examples showing that GPUs can compute normal vectors for images significantly faster than CPUs, with times of 125 clock cycles for a 640x480 image on GPU vs 625 on CPU and 172 clock cycles for a 1280x1024 image on GPU vs 2500 on CPU. It also provides an overview of tools for GPGPU programming, such as CUDA and shader languages, and how GPUs are specialized for parallel processing which allows them to outperform CPUs for certain tasks.

1. 2010/2/25
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GPGPU
Ked
Result
Computation of Normal Vector
Image resolution: 640 x 480
CPU: 625 clock time
GPU: 125 clock time
Result
Computation of Normal Vector
Image resolution: 1280 x 1024
CPU: 2500 clock time
GPU: 172 clock time
OK, What is GPU
 A graphics accelerator incorporates custom
microchips which contain special mathematical
operations commonly used in graphics rendering.
GPGPU
• General purpose computing on GPU
 GPGPU
 GPGP
 GP2
(boring RD -.-||)‫‏‬
hi, I am R2-D2
Why faster

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Why faster Why faster
 CPU GPU
General purpose Specialized hardware
Serial execution Parallel execution
Minimum latency Maximum throughput
Development tools:
Focus on GPGPU
 CUDA:
 Compute Unified Device Architecture
 Developed by NVIDIA
 C like language
 Full developing environment
 Compiler
 Debugger
 Math libraries
Development tools:
Focus on GPGPU
 Advantage:
 Shared memory amongst threads
 16k
 Faster downloads and readbacks to and from GPU
 Full support for integer and bitwise operations
Development tools:
Shader programming
 ARB low-level assembly language
 OpenGL shading language
 Cg programming language
 DirectX high-level shader language
Development tools:
Shader programming

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Development tools:
Shader programming
 Developing tools of GLSL:
Pipeline of GPU processing
Shader programming
Vertex shader
Fragment shader
Geometry shader
RenderMan shading language
 Developed by Pixar has uncompromising image
quality as its fundamental goal
 Light shader
 Displacement shader
 Surface shader
 Volume shader
 Imager shader
Vertex shader Fragment shader

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Streaming of fragment shader
 Stream processing is a computer programming paradigm,
related to SIMD, that allows some applications to more
easily exploit a limited form of parallel processing. Such
applications can use multiple computational units, such
as the floating point units on a GPU, without explicitly
managing allocation, synchronization, or communication
among those units.
Branch of fragment shader
Conception of GPGPU
 Textures => Computing arrays
 Vertex Coordinates => Computational range
 Fragment programs => Computation
 Read from framebuffer => Get result
Case study:
Computation of normal vector
 Normal(V0) =
[ normal(F401) +
normal(F102) +
normal(F203) +
normal(F304) ] / 4
 Normal(F102) =
cross(v1v0, v2v0)‫‏‬
Prepare:
Choose graphic card
Prepare:
Test the graphic card
need

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Use GLSL in BCB environment:
Call GLee library Other choice: GLew
Install shader:
Run-time building
Texture:
Computing array
Vertex coordinate:
Computational range
Fragment program:
Computation
Read from framebuffer:
Get result
FameBuffer Object is a better choice

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Trivia:
Ghost in numerical computing
Review the result
Image resolution: 640 x 480
CPU: 625 clock time
GPU: 125 clock time
Image resolution: 1280 x 1024
CPU: 2500 clock time
GPU: 172 clock time
Reference
 GPU Gems 2
 OpenGL Shading Language
 OpenGL Programming Guide
 Dominik Göddeke
-- GPGPU::Basic Math Tutorial
(website)‫‏‬
 GPGPU: SIGGRAPH 2004 course
 Batch, batch, batch:
what does it really means
Thx.