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General-Purpose Computation on Graphics Hardware. Adapted from: David Luebke (University of Virginia) and NVIDIA. Your Presentations. Everyone Must attend 10% loss of presentation points if a day is missed Presentation Grading Criteria posted online Everyone must present

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general purpose computation on graphics hardware

General-Purpose Computation on Graphics Hardware

Adapted from: David Luebke (University of Virginia) and NVIDIA

your presentations
Your Presentations
  • Everyone Must attend
      • 10% loss of presentation points if a day is missed
  • Presentation Grading Criteria posted online
  • Everyone must present
      • Grades are given to the group, not individual
outline
Outline
  • Overview / Motivation
  • GPU Architecture Fundamentals
  • GPGPU Programming and Usage
  • New NVIDIA Architectures (FERMI)
  • More Infromation
motivation computational power
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
    • GeForce 6800 Ultra observed: 53 GFLOPS, 35.2 GB/sec peak
    • GeForce 8800 GTX: estimated at 520 GFLOPS, 86.4 GB/sec peak (reference here)
      • That’s almost 100 times faster than a 3 Ghz Pentium4!
  • GPUs are getting faster, faster
    • CPUs: annual growth  1.5× decade growth  60×
    • GPUs: annual growth > 2.0× decade growth > 1000

Courtesy Kurt Akeley,Ian Buck & Tim Purcell, GPU Gems (see course notes)

motivation computational power1
Motivation: Computational Power

GPU

CPU

Courtesy Naga Govindaraju

motivation computational power2
Motivation:Computational Power

multiplies per second

NVIDIA NV30, 35, 40

ATI R300, 360, 420

GFLOPS

Pentium 4

July 01

Jan 02

July 02

Jan 03

July 03

Jan 04

Courtesy Ian Buck

an aside computational power
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
motivation flexible and precise
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
motivation the potential of gpgpu
Motivation: The 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 coprocessor
the problem difficult to use
The Problem: Difficult 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
  • Can’t simply “port” code written for the CPU!
gpu fundamentals the graphics pipeline
GPU Fundamentals:The Graphics Pipeline
  • A simplified graphics pipeline
    • Note that pipe widths vary
    • Many caches, FIFOs, and so on not shown

CPU

GPU

Graphics State

Application

Transform

Rasterizer

Shade

VideoMemory(Textures)

Vertices(3D)

Xformed,LitVertices(2D)

Fragments(pre-pixels)

Finalpixels(Color, Depth)

Render-to-texture

gpu fundamentals the modern graphics pipeline
Programmable vertex processor!

Programmable pixel processor!

GPU Fundamentals:The Modern Graphics Pipeline

CPU

GPU

Graphics State

VertexProcessor

FragmentProcessor

Application

VertexProcessor

Rasterizer

PixelProcessor

VideoMemory(Textures)

Vertices(3D)

Xformed,LitVertices(2D)

Fragments(pre-pixels)

Finalpixels(Color, Depth)

Render-to-texture

gpu pipeline transform
GPU Pipeline: Transform
  • Vertex Processor (multiple operate in parallel)
    • Transform from “world space” to “image space”
    • Compute per-vertex lighting
gpu pipeline rasterizer
GPU Pipeline: Rasterizer
  • 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
gpu pipeline shade
GPU Pipeline: Shade
  • Fragment Processors (multiple in parallel)
    • Compute a color for each pixel
    • Optionally read colors from textures (images)
importance of data parallelism
Importance of Data Parallelism
  • GPU: Each vertex / fragment is independent
    • Temporary registers are zeroed
    • No static data
    • No read-modify-write buffers
  • Data parallel processing
    • Best for ALU-heavy architectures: GPUs
      • Multiple vertex & pixel pipelines
    • Hide memory latency (with more computation)

Courtesy of Ian Buck

arithmetic intensity
Arithmetic Intensity
  • Lots of ops per word transferred
  • GPGPU demands high arithmetic intensity for peak performance
    • Ex: solving systems of linear equations
    • Physically-based simulation on lattices
    • All-pairs shortest paths

Courtesy of Pat Hanrahan

data streams kernels
Data Streams & Kernels
  • Streams
    • Collection of records requiring similar computation
      • Vertex positions, Voxels, FEM cells, etc.
    • Provide data parallelism
  • Kernels
    • Functions applied to each element in stream
      • Transforms, PDE, …
    • No dependencies between stream elements
      • Encourage high arithmetic intensity

Courtesy of Ian Buck

example simulation grid
Example: Simulation Grid
  • Common GPGPU computation style
    • Textures represent computational grids = streams
  • Many computations map to grids
    • Matrix algebra
    • Image & Volume processing
    • Physical simulation
    • Global Illumination
      • ray tracing, photon mapping, radiosity
  • Non-grid streams can be mapped to grids
stream computation
Stream Computation
  • Grid Simulation algorithm
    • Made up of steps
    • Each step updates entire grid
    • Must complete before next step can begin
  • Grid is a stream, steps are kernels
    • Kernel applied to each stream element
scatter vs gather
Scatter vs. Gather
  • Grid communication
    • Grid cells share information
computational resources inventory
Computational Resources Inventory
  • Programmable parallel processors
    • Vertex & Fragment pipelines
  • Rasterizer
    • Mostly useful for interpolating addresses (texture coordinates) and per-vertex constants
  • Texture unit
    • Read-only memory interface
  • Render to texture
    • Write-only memory interface
vertex processor
Vertex Processor
  • Fully programmable (SIMD / MIMD)
  • Processes 4-vectors (RGBA / XYZW)
  • Capable of scatter but not gather
    • Can change the location of current vertex
    • Cannot read info from other vertices
    • Can only read a small constant memory
  • Future hardware enables gather!
    • Vertex textures
fragment processor
Fragment Processor
  • Fully programmable (SIMD)
  • Processes 4-vectors (RGBA / XYZW)
  • Random access memory read (textures)
  • Capable of gather but not scatter
    • No random access memory writes
    • Output address fixed to a specific pixel
  • Typically more useful than vertex processor
    • More fragment pipelines than vertex pipelines
    • RAM read
    • Direct output
cpu gpu analogies
CPU-GPU Analogies
  • CPU programming is familiar
    • GPU programming is graphics-centric
  • Analogies can aid understanding
cpu gpu analogies1
CPU-GPU Analogies

CPU GPU

Stream / Data Array = Texture

Memory Read = Texture Sample

cpu gpu analogies2
CPU-GPU Analogies

Loop body / kernel / algorithm step = Fragment Program

CPU

GPU

feedback
Feedback
  • Each algorithm step depend on the results of previous steps
  • Each time step depends on the results of the previous time step
arb gpu assembly language architecture review board
ARB GPU Assembly LanguageArchitecture Review Board
  • ABS - absolute value
  • LOG - logarithm base 2 (approximate)
  • MAD - multiply and add
  • MAX - maximum
  • MIN - minimum
  • MOV - move
  • MUL - multiply
  • POW - exponentiate
  • RCP – reciprocal
  • RSQ - reciprocal square root
  • SGE - set on greater than or equal
  • SLT - set on less than
  • SUB - subtract
  • SWZ - extended swizzle
  • XPD - cross product
  • ABS - absolute value
  • ADD - add
  • ARL - address register load
  • DP3 - 3-component dot product
  • DP4 - 4-component dot product
  • DPH - homogeneous dot product
  • DST - distance vector
  • EX2 - exponential base 2
  • EXP - exponential base 2 (approximate)
  • FLR - floor
  • FRC - fraction
  • LG2 - logarithm base 2
  • LIT - compute light coefficients
nvidia graphics card architecture

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

IU

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

SP

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

Shared

Memory

TEX L1

TEX L1

TEX L1

TEX L1

TEX L1

TEX L1

TEX L1

TEX L1

TF

TF

TF

TF

TF

TF

TF

TF

L2

L2

L2

L2

L2

L2

Memory

Memory

Memory

Memory

Memory

Memory

Nvidia Graphics Card Architecture
  • GeForce-8 Series
    • 12,288 concurrent threads, hardware managed
    • 128 Thread Processor cores at 1.35 GHz == 518 GFLOPS peak

Work Distribution

Host CPU

fermi streaming multiprocessor sm
FERMI: Streaming Multiprocessor (SM)
  • Each SM contains
    • 32 Cores
    • 16 Load/Store units
    • 32,768 registers
fermi core architecture
FERMI: Core Architecture
  • Newer FP representation
    • IEEE 754-2008
  • Two units
    • Floating point
    • Integer
  • Simultaneous execution possible
applications
Applications
  • Includes lots of sample applications
    • Ray-tracer
    • FFT
    • Image segmentation
    • Linear algebra
brook performance
Brook performance

2-3x faster than CPU implementation

ATI Radeon 9800 XT

NVIDIA GeForce 6800

  • GPUs still lose against SSE
  • cache friendly code.
  • Super-optimizations
    • ATLAS
    • FFTW
  • compared against 3GHz P4:
  • Intel Math Library
  • FFTW
  • Custom cached-blocked
  • segment C code
gpgpu examples
GPGPU Examples
  • Fluids
  • Reaction/Diffusion
  • Multigrid
  • Tone mapping
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