GeneralPurpose Computation on Graphics Hardware

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General-Purpose Computation on Graphics Hardware
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Introduction

• David Luebke
• University of Virginia

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Course Introduction

• The GPU on commodity video cards has evolved into
  an extremely flexible and powerful processor
• Programmability
• Precision
• Power
• This course will address how to harness that
  power for general-purpose computation

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Motivation Computational Power

• GPUs are fast
• 3.0 GHz dual-core Pentium4 24.6 GFLOPS
• NVIDIA GeForceFX 7800 165 GFLOPs
• 1066 MHz FSB Pentium Extreme Edition 8.5 GB/s
• ATI Radeon X850 XT Platinum Edition 37.8 GB/s
• GPUs are getting faster, faster
• CPUs 1.4 annual growth
• GPUs 1.7(pixels) to 2.3 (vertices) annual
  growth

Courtesy Kurt Akeley,Ian Buck Tim Purcell, GPU
Gems (see course notes)
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Motivation Computational Power
Courtesy Ian Buck, John Owens
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An Aside Computational Power

• Why are GPUs getting 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

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Motivation 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

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Motivation The Potential of GPGPU

• 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

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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
• Cant simply port CPU code!

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Course goals

• A detailed introduction to general-purpose
  computing on graphics hardware
• We emphasize
• Core computational building blocks
• Strategies and tools for programming GPUs
• Tips tricks, perils pitfalls of GPU
  programming
• Case studies to bring it all together

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Why a SIGGRAPH Course?

• Why SIGGRAPH, not (say) Supercomputing?
• Many graphics applications can benefit from GPGPU
• Hot topic examples shadows, level sets, fluids
• Keeping computation on-card!
• Many graphics applications strive for visual
  plausibility rather than rigorous scientific
  realism
• Better tolerate GPU limitations in precision,
  memory
• Well suited as GPGPU early adopters
• GPGPU programming still requires expertise of
  SIGGRAPH audience

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Course Prerequisites

• We assume
• Familiarity with interactive graphics and
  computer graphics hardware
• Ideally, some experience programming vertex and
  pixel shaders
• Target audience
• Researchers interested in GPGPU
• Graphics and games developers interested in
  incorporating these techniques into their work
• Attendees wishing a survey of this exciting field

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Course Topics

• GPU building blocks
• Languages and tools
• Effective GPU programming
• GPGPU case studies

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Course Topics Details

• GPU building blocks
• Linear algebra
• Sorting and searching
• Geometric Computing
• Languages and tools
• High-level languages
• Debugging tools

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Course Topics Details

• Effective GPU programming
• Efficient data-parallel programming
• GPU memory resources data layout approaches
• GPU computation strategies tricks
• Data structures
• Case studies in GPGPU Programming
• Databases and data mining operations on GPUs
• Particles grids on GPUs
• Adaptive shadow maps octree textures on GPUs

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Speakers

• In order of appearance
• David Luebke, University of Virginia
• Mark Harris, NVIDIA
• Jens Krüger, TU-Munich
• Tim Purcell, NVIDIA
• Naga Govindaraju, University of North Carolina
• Ian Buck, NVIDIA
• Cliff Woolley, University of Virginia
• Aaron Lefohn, University of California Davis

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Schedule
Luebke Harris Krüger Purcell

• 830 Introduction
• Welcome, overview, the graphics pipeline
• GPU Building Blocks
• 850 Computational concepts CPU?GPU
• Streaming, resources, CPU-GPU analogies,
  branching
• 915 Linear algebra
• Representations, operations, example algorithms
• 950 Sorting Searching
• Bitonic sort, Binary k-nearest neighbor search
• 1015 Break

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Schedule
Govindaraju Buck Purcell

• 1030 Geometric computation
• Visibility, collision proximity, reliable
  computation
• Languages and Tools
• 1100 High-level languages
• Cg/HLSL/GLslang, Sh, Brook
• 1130 Debugging tools
• imdebug, DirectX/OpenGL shader IDEs, ShadeSmith

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Schedule
Woolley Lefohn Buck Lefohn

• Effective GPGPU Programming
• 1150 GPU program optimization
• Computational frequency, profiling, load
  balancing
• 1215 Lunch break
• 145 GPU memory models
• Memory objects, layout of data structures, FBOs
• 215 GPU computation strategies tricks
• Precision, performance, scatter, branching
• 255 GPU data structures
• High-level data structures
• 330 Break

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Schedule
Govindaraju Krüger Lefohn All

• Case Studies
• 345 Databases data mining on GPUs
• Queries, aggregation, mining frequencies
  quantiles
• 415 Geometry processing on GPUs
• Particles, grids, PBO/VBO vs. FBO vs. VTF/SM3.0
• 445 Applications of adaptive data
  structures
• Adaptive shadow maps, octree textures
• Conclusion
• 515 Question-and-answer session
• 530 Wrap!

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GPU Fundamentals The Graphics Pipeline
GPU
CPU
Graphics State
Xformed, Lit Vertices (2D)
Screenspace triangles (2D)
Fragments (pre-pixels)
Final Pixels (Color, Depth)
Application
Transform Light
Rasterize
Shade
AssemblePrimitives
Vertices (3D)
VideoMemory(Textures)
Render-to-texture

• A simplified graphics pipeline
• Note that pipe widths vary
• Many caches, FIFOs, and so on not shown

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GPU Fundamentals The Modern Graphics Pipeline
GPU
CPU
Graphics State
FragmentProcessor
VertexProcessor
Xformed, Lit Vertices (2D)
Screenspace triangles (2D)
Fragments (pre-pixels)
Final Pixels (Color, Depth)
Transform
Application
Rasterize
Shade
AssemblePrimitives
Vertices (3D)
VideoMemory(Textures)
Render-to-texture

• Programmable vertex processor!

• Programmable pixel processor!

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The Coming Soon Graphics Pipeline
GPU
CPU
Graphics State
GeometryProcessor
Xformed, Lit Vertices (2D)
Screenspace triangles (2D)
Fragments (pre-pixels)
Final Pixels (Color, Depth)
Application
VertexProcessor
Rasterize
FragmentProcessor
AssemblePrimitives
Vertices (3D)
VideoMemory(Textures)
Render-to-texture

• Programmable primitive assembly!

• More flexible memory access!

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GPU Pipeline Transform

• Vertex processor (multiple in parallel)
• Transform from world space to image space
• Compute per-vertex lighting

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GPU Pipeline Rasterize

• 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

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GPU Pipeline Shade

• Fragment processors (multiple in parallel)
• Compute a color for each pixel
• Optionally read colors from textures (images)

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Coming Up

• Next Mapping computational concepts to the GPU
• Also coming up
• Core building blocks for GPGPU computing
• Memory layout, data structures, and algorithms
• Detailed advice on writing high performance
  GPGPU code
• Lots of examples

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Course Evaluation Form

• Please help us improve the GPGPU Course
• Fill out the SIGGRAPH evaluation form
• http//www.siggraph.org/cgi-bin/cgi/exCoursesEval.
  html
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