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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. 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

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  1. General-Purpose Computation on Graphics Hardware Adapted from: David Luebke (University of Virginia) and NVIDIA

  2. 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

  3. Outline • Overview / Motivation • GPU Architecture Fundamentals • GPGPU Programming and Usage • New NVIDIA Architectures (FERMI) • More Infromation

  4. 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)

  5. Motivation: Computational Power GPU CPU Courtesy Naga Govindaraju

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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!

  11. 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

  12. 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

  13. GPU Pipeline: Transform • Vertex Processor (multiple operate in parallel) • Transform from “world space” to “image space” • Compute per-vertex lighting

  14. 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

  15. GPU Pipeline: Shade • Fragment Processors (multiple in parallel) • Compute a color for each pixel • Optionally read colors from textures (images)

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. Scatter vs. Gather • Grid communication • Grid cells share information

  22. 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

  23. 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

  24. 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

  25. CPU-GPU Analogies • CPU programming is familiar • GPU programming is graphics-centric • Analogies can aid understanding

  26. CPU-GPU Analogies CPU GPU Stream / Data Array = Texture Memory Read = Texture Sample

  27. CPU-GPU Analogies Loop body / kernel / algorithm step = Fragment Program CPU GPU

  28. Feedback • Each algorithm step depend on the results of previous steps • Each time step depends on the results of the previous time step

  29. 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

  30. 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

  31. NVIDIA FERMI

  32. FERMI: Streaming Multiprocessor (SM) • Each SM contains • 32 Cores • 16 Load/Store units • 32,768 registers

  33. FERMI: Core Architecture • Newer FP representation • IEEE 754-2008 • Two units • Floating point • Integer • Simultaneous execution possible

  34. FERMI: Comparison

  35. FERMI: Results

  36. FERMI: Results

  37. What it looks like!

  38. Applications • Includes lots of sample applications • Ray-tracer • FFT • Image segmentation • Linear algebra

  39. 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

  40. GPGPU Examples • Fluids • Reaction/Diffusion • Multigrid • Tone mapping

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