May 8, 2007Farid Harhad and Alaa Shams CS7080 Overview of the GPU Architecture CS7080 Final Class Project Supervised by: Dr. Elias Khalaf By: Farid Harhad.

May 8, 2007 Farid Harhad and Alaa ShamsCS7080

Overview of the GPU Architecture

CS7080 Final Class ProjectSupervised by: Dr. Elias Khalaf

By: Farid Harhad & Alaa Shams


Outline

• Introduction

• GPU Architecture

• GPU programming– GPU programming model– Toolkit and language

• Sample Code

• Conclusion


Introduction

• The GPU on commodity video cards has evolved into extremely flexible and powerful processor.

• GPUs are fast :– 3.0 GHz Pentium 4: 6 GFLOPs, 6 GB/Sec peak– 3.0 GHz dual-core Pentium 4: 24.6 GFLOPs– GeoForceFX 6800: 53 GFLOPs, 34 GB/Sec Peak – GeoForceFX 7800: 165 GFLOPs– 1066 MHz FSB Pentium Extreme Edition: 8.5 GB/s– ATI Radeo X850 XT Platinum Edition: 37.8 GB/s

• GPUS are getting faster and faster– CPUs: ~1.5x annual growth ~60x per decade– GPUs: ~2.3x annual growth ~1000x per decade


Computational power


Cont.


Why are GPUsgetting faster so fast?

• Arithmetic intensity: the specialized nature of GPUs makes it easier to use additional transistors for computation not cache

• Economics: multi-billion dollar video game market is a pressure cooker that drives innovation


Flexible and Precise

• Modern GPUs are deeply Programmable– Programmable pixel, vertex, video engines

– Solidifying high-level language support

• Modern GPUs support high precision– 32 bit floating point throughout the pipeline

– High enough for many (not all) applications


The Potential of GPU

• In short:– The power and flexibility of GPUs makes

them an attractive platform for general- purpose computation

– Example applications range from in-game physics simulation to conventional Computational science

– Goal: make the inexpensive power of the GPU available to developers as a sort of computational coprocessor


The Problem: Difficult To Use

• GPUs designed for & driven by video games– Programming model unusual– Programming idioms tied to computer graphics– Programming environment tightly constrained

• Underlying architectures are:– Inherently parallel– Rapidly evolving (even in basic feature set!)– Largely secret

• Can’t simply “port” CPU code!


GPU ArchitectureGraphic PL


Modern Graphic PL


Transform

• Vertex processor

(multiple in parallel)– Transform from “world space” to “image space”

– Compute per-vertex lighting


Rasterizer

– Convert geometric rep. (vertex) to image rep. (fragment)• Fragment = image fragment

– Pixel + associated data: color, depth, stencil, etc.

– Interpolate per-vertex quantities across pixels


Shade

• Fragment processors

(multiple in parallel)– Compute a color for each pixel

– Optionally read colors from textures (images)


GPU programming


GPU Programming Model

• Useful analogies:– Rasterization = Kernel Invocation– Texture coordinates = Computation domain– Vertex coordinates = computational range

• Invoking computation amounts to drawing pixels:– GPGPU invocation is commonly a full-screen quad

GPU CPU

Stream / Data array:• Memory read

Texture:• Texture sampling

Loop body / Kernel / Algorithm Fragment program

Feedback: Array write Feedback: render a texture


GPU Programming Model


Toolkits and Language

• 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

• CUDA SDK– Includes a C compiler and many libraries


Sample Code


Conclusion

• GPU provide the programmer with unparalleled flexibility and performance

in a product line that spans the entire PC market.

• Utilizing the capabilities of the GPU allow the programmers to develop newer applications-either graphical or general purpose-in more efficient way.

References

• GPU Gem2 (Chapters 29 & 30)

• SIGGRAPH 2005 GPGPU Course

• http://www.gpgpu.org/



Questions?

Thanks

May 8, 2007Farid Harhad and Alaa Shams CS7080 Overview of the GPU Architecture CS7080 Final Class Project Supervised by: Dr. Elias Khalaf By: Farid Harhad.

Documents

alaa shamscs7080may

decade farid harhad

alaa shamscs7080cont

alaa shamscs7080overview

alaa shamscs7080flexible

alaa shamscs7080rasterizer

alaa shamscs7080why

innovationfarid harhad