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)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 Courtesy Naga Govindaraju GPU CPU

6. Motivation: Computational Power Courtesy Ian Buck GFLOPS multiplies per second NVIDIA NV30, 35, 40 ATI R300, 360, 420 Pentium 4 July 01Jan 02July 02Jan 03July 03Jan 04

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 GPUCPU Application Transform Rasterizer Shade Video Memory (Textures) Vertices (3D) Xformed, Lit Vertices (2D) Fragments (pre-pixels) Final pixels (Color, Depth) Graphics State Render-to-texture

12. GPU Fundamentals: The Modern Graphics Pipeline Programmable vertex processor! Programmable pixel processor! GPUCPU Application Vertex Processor Rasterizer Pixel Processor Video Memory (Textures) Vertices (3D) Xformed, Lit Vertices (2D) Fragments (pre-pixels) Final pixels (Color, Depth) Graphics State Render-to-texture Vertex Processor Fragment Processor

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 CPUGPU

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 Language Architecture 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

30. Nvidia Graphics Card Architecture GeForce-8 Series –12,288 concurrent threads, hardware managed –128 Thread Processor cores at 1.35 GHz == 518 GFLOPS peak TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF TEX L1 SP SharedMemory IU SP SharedMemory IU TF L2 Memory Work Distribution Host CPU L2 Memory L2 Memory L2 Memory L2 Memory L2 Memory

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

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 compared against 3GHz P4: Intel Math Library FFTW Custom cached-blocked segment C code GPUs still lose against SSE cache friendly code. Super-optimizations ATLAS FFTW ATI Radeon 9800 XT NVIDIA GeForce 6800

40. GPGPU Examples Fluids Reaction/Diffusion Multigrid Tone mapping