General-Purpose Computation on General-Purpose Computation on Graphics HardwareGraphics Hardware

General-Purpose Computation on General-Purpose Computation on Graphics HardwareGraphics Hardware

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

Your PresentationsYour 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

OutlineOutline

• Overview / Motivation

• GPU Architecture Fundamentals

• GPGPU Programming and Usage

• New NVIDIA Architectures (FERMI)

• More Infromation

Motivation: Computational PowerMotivation: 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 PowerMotivation: Computational Power

Courtesy Naga Govindaraju

GPU

CPU

Motivation:Computational PowerMotivation:Computational Power

Courtesy Ian Buck

GFL

OPS

multiplies per second

NVIDIA NV30, 35, 40

ATI R300, 360, 420

Pentium 4

July 01 Jan 02 July 02 Jan 03 July 03 Jan 04

An Aside: Computational PowerAn 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 preciseMotivation: 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 GPGPUMotivation: 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 UseThe 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 PipelineGPU Fundamentals:The Graphics Pipeline

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

GPUCPU

ApplicationApplication TransformTransform RasterizerRasterizer ShadeShade VideoMemory

(Textures)

VideoMemory

(Textures)VerticesVertices

(3D)(3D)Xformed,Xformed,

LitLitVerticesVertices

(2D)(2D)

FragmentsFragments(pre-pixels)(pre-pixels)

FinalFinalpixelspixels

(Color, Depth)(Color, Depth)

Graphics StateGraphics State

Render-to-textureRender-to-texture

GPU Fundamentals:The Modern Graphics PipelineGPU Fundamentals:The Modern Graphics Pipeline

• Programmable vertex processor!

• Programmable pixel processor!

GPUCPU

ApplicationApplication VertexProcessor

VertexProcessor RasterizerRasterizer Pixel

ProcessorPixel

ProcessorVideo

Memory(Textures)

VideoMemory

(Textures)VerticesVertices

(3D)(3D)Xformed,Xformed,

LitLitVerticesVertices

(2D)(2D)

FragmentsFragments(pre-pixels)(pre-pixels)

FinalFinalpixelspixels

(Color, Depth)(Color, Depth)

Graphics StateGraphics State

Render-to-textureRender-to-texture

VertexProcessor

VertexProcessor

FragmentProcessorFragmentProcessor

GPU Pipeline: TransformGPU Pipeline: Transform

• Vertex Processor (multiple operate in parallel)– Transform from “world space” to “image space”– Compute per-vertex lighting

GPU Pipeline: RasterizerGPU 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: ShadeGPU Pipeline: Shade

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

Importance of Data ParallelismImportance 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 IntensityArithmetic 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 & KernelsData 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 GridExample: 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 ComputationStream 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. GatherScatter vs. Gather

• Grid communication– Grid cells share information

Computational Resources InventoryComputational 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 ProcessorVertex 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 ProcessorFragment 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 AnalogiesCPU-GPU Analogies

• CPU programming is familiar– GPU programming is graphics-centric

• Analogies can aid understanding

CPU-GPU AnalogiesCPU-GPU Analogies

CPU GPU

Stream / Data Array = Texture

Memory Read = Texture Sample

CPU-GPU AnalogiesCPU-GPU Analogies

Loop body / kernel / algorithm step = Fragment Program

CPU GPU

FeedbackFeedback

• 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 LanguageArchitecture Review Board

ARB GPU Assembly LanguageArchitecture Review Board

• 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

• 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

Nvidia Graphics Card ArchitectureNvidia Graphics Card Architecture• GeForce-8 Series

– 12,288 concurrent threads, hardware managed– 128 Thread Processor cores at 1.35 GHz == 518 GFLOPS peak

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Work DistributionHost CPU

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NVIDIA FERMINVIDIA FERMI

FERMI: Streaming Multiprocessor (SM)FERMI: Streaming Multiprocessor (SM)

• Each SM contains

• 32 Cores

• 16 Load/Store units

• 32,768 registers

FERMI: Core ArchitectureFERMI: Core Architecture

• Newer FP representation– IEEE 754-2008

• Two units– Floating point– Integer

• Simultaneous execution possible

FERMI: ComparisonFERMI: Comparison

FERMI: ResultsFERMI: Results

FERMI: ResultsFERMI: Results

What it looks like!What it looks like!

ApplicationsApplications

• Includes lots of sample applications– Ray-tracer– FFT– Image segmentation– Linear algebra

Brook performanceBrook performance

2-3x faster than CPU implementation

compared against 3GHz P4:• Intel Math Library• FFTW• Custom cached-blocked segment C code

GPUs still lose against SSEcache friendly code.Super-optimizations

• ATLAS• FFTW

ATI Radeon 9800 XT

NVIDIA GeForce 6800

GPGPU ExamplesGPGPU Examples

• Fluids

• Reaction/Diffusion

• Multigrid

• Tone mapping