CSE 690 General-Purpose Computation on Graphics Hardware (GPGPU)

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CSE 690General-Purpose Computation on Graphics
Hardware(GPGPU)
Courtesy David Luebke, University of Virginia
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(No Transcript)
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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 GHz Pentium4 theoretical 6 GFLOPS, 5.96 GB/sec
  peak
• GeForceFX 5900 observed 20 GFLOPs, 25.3 GB/sec
  peak
• GPUs are getting faster, faster
• CPUs annual growth ? 1.5 ? decade growth ? 60
• GPUs annual growth gt 2.0 ? decade growth gt 1000

Courtesy Kurt Akeley,Ian Buck Tim Purcell, GPU
Gems
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MotivationComputational Power
GPU
CPU
Courtesy Naga Govindaraju
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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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MotivationFlexible 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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MotivationThe Potential of GPGPU

• 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 co-processor

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The ProblemDifficult To Use

• GPUs designed for and driven by video games
• Programming model is unusual tied to computer
  graphics
• Programming environment is tightly constrained
• Underlying architectures are
• Inherently parallel
• Rapidly evolving (even in basic feature set!)
• Largely secret
• Cant simply port code written for the CPU!

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Course Goals

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

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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
• Database operations
• Languages and tools
• High-level languages
• Debugging tools

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Course Topics Details

• Effective GPU programming
• Efficient data-parallel programming
• Data formatting addressing
• GPU computation strategies tricks
• Case studies in GPGPU Programming
• Physically-based simulation on GPUs
• Ray tracing photon mapping on GPUs
• Tone mapping on GPUs
• Level sets on GPUs

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Intended Audience

• Anyone interested in accelerated computing
• from all academic disciplines
• No graphics background required
• will cover all necessary and relevant detail in
  the course
• What is required
• working knowledge of C/C
• linear algebra
• enthusiasm and an open mind

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Course Schedule

• Understanding the fabric computer graphics
  basics
• Overview of GPUs
• architecture
• features
• programming model
• some simple applications
• System issues
• cache and data management,
• languages and compilers
• stream processing
• GPU-CPU load balancing

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Course Schedule

• GPU-specific implementations of standard
  operations
• sorting and searching
• linear algebra
• signal processing
• differential equations
• numerical solvers

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Course Schedule

• Numerical and scientific computations
• non-linear optimization
• FFT
• differential equations for rigid body simulation
• statistics
• fluid dynamics
• molecular dynamics

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Course Schedule

• Geometric computations
• proximity and collision computations
• Voronoi diagrams and distance fields
• motion planning and navigation
• Image processing
• automatic and user-guided interactive
  segmentation
• level-set operations
• visual feedback (coupling computational and
  visualization aspects)
• Medical imaging
• computed tomography
• functional imaging

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Course Schedule

• Computer vision and AI
• real-time tracking
• surface, shape, and scene reconstruction
• reasoning and belief propagation
• Database computations
• database queries predicates, booleans,
  aggregates
• streaming databases
• data mining and visual data mining

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Course Schedule

• Computer graphics and visualization
• raytracing
• photon-mapping
• shadows
• radiosity
• amorphous phenomena
• volume rendering

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Course Schedule

• GPU Clusters
• parallel computing environments for GPUs
• comparison with other high performance
  specialized hardware (playstation2 cluster from
  UIUC)
• Project presentations
• at the end of the term