An Introduction to GPU3D Games to HPC

Krishnaraj RaoPresented at Bangalore DV Club, 03/12/2010

Agenda

3D GraphicsThe Big PictureQuick OverviewProgramming ModelImportance of 3D

High Performance Parallel ComputingWhy GPUs for HPPC?Available APIsGPU Computing architecture

Q & A

The Big Picture – Movies

Creation

Capture Models Scene API

Rendering Post Processing

Creation

The Big Picture - Games

Creation

Capture Models Scene API

Rendering Post Processing

Creation

GPU’sDrivers

HLSL,Cg

Models end up in World Space

Y

X

Z

Light Source

Screen

View Pointor Camera

World Coordinate Space

Worldspace includes everything!Position and orientation for allitems is needed to accurately calculatetransformations into screen space.

View Transformation world ends up on Screen

Screen Coordinate Space

Simple Interactive 3D Graphics App

A simple exampleStatic scene geometry, moving viewer

Repeat this loop:CPU takes user input from joystick or mouseCPU re-calculates viewer position, view direction, and light positions in 3-D world spaceGPU clears memory and draws the complete scene geometry with the new viewer and light positionsRepeat forever

VertexEngine Setup Raster

Z Cull

FragmentEngine

Texture

Raster Ops

ReadJoystickPosition

Update Viewer Position and Light

Direction

Draw all Scene

Objects

Adding Programmability to the Graphics Pipeline

3D Applicationor Game

3D API:OpenGL or Direct3D

ProgrammableVertex

Processor

PrimitiveAssembly

Rasterization & Interpolation

3D API Commands

Transformed Vertices

Assembled Polygons, Lines, and

Points

GPU Command &

Data Stream

ProgrammableFragmentProcessor

RasterizedPre-transformed

Fragments

TransformedFragments

RasterOperations Framebuffer

Pixel UpdatesGPU

Front End

Pre-transformed Vertices

Vertex Index Stream

Pixel Location Stream

CPU – GPU Boundary

NVIDIA Confidential

A History of Innovation

1999GeForce 256

22 Million Transistors

2002GeForce463 MillionTransistors

2003GeForce FX130 Million Transistors

2004GeForce 6 222 Million Transistors

1995NV1

1 Million Transistors

2005GeForce 7 302 Million Transistors 2008

GeForce GTX 2001.4 BillionTransistors

2006-2007GeForce 8 754 Million Transistors

…. but what do all these extra transistors do?

GPU continues to offload CPU work

GeomGather

GeomProc

TriangleProc

PixelProc Z / Blend

GPUCPU

GeomGather

GeomProc

TriangleProc

PixelProc Z / Blend

GPUCPU

GeomGather

GeomProc

TriangleProc

PixelProc Z / Blend

GPUCPU

Physics and AI

Scene Mgmt

GeomGather

GeomProc

TriangleProc

PixelProc Z / Blend

GPUCPU

Physics and AI

Scene Mgmt

1996

2000

2004

2008

Programming ModelAPI: Set of functions, procedures or classes that an OS, library or service provides to support requests made by computer programsDirectX: Collection of APIs to handle multimedia, esp. game programming and video tasks, on MS platforms.OpenGL (Open Graphics Library) is a standard specification defining a cross-language, cross-platform API for writing applications that produce 2D and 3D computer graphics.

Why is 3D Graphics important?More than just Fun and Games....

Tokyo, Japan California Coastline

3D Consumer Applications

Music

Vista

Photos Maps

PDFsOffice

GPUS IN HPC

MassiveData

Parallelism

Data Fits in Cache Huge Data Sets

Evolution of Processors

InstructionLevel

Parallelism

GPU Processing Power

GPUNVIDIA GTX 285240 cores1.04 TFLOPS

CPUIntel Core i7 965

4 cores102 GFLOPS

CPU

GPU

CPU, meet your new partner!

With floating-point math and textures, graphics processors can be used for more than just graphics

GPGPU = “General Purpose Computing on GPUs”

Lots of ongoing research mapping algorithms and problems onto programmable GPUs

Solving Linear EquationsBlack-Scholes Options PricingRigid- and Soft-Body Dynamics

Middleware layers being developed to accelerate “eye candy” game physics on GPUs (HavokFX)

Beyond Graphics

What is GPGPU ?General Purpose computation using GPUin applications other than 3D graphics

GPU accelerates critical path of applicationData parallel algorithms leverage GPU attributes

Large data arrays, streaming throughputFine-grain SIMD parallelismFloating point (FP) computation

Great for “embarrassingly parallel” algorithms

Applications – see //GPGPU.orgGame effects (FX) physics, image processingPhysical modeling, computational engineering, matrix algebra, convolution, correlation, sorting

A quiet buildup of potentialCalculation Throughput and Memory Bandwidth: 10XEquivalent performance at fraction of power & costGPU in every PC – pervasive presence and massive impact

GPUs have always been parallel “multi-core”Natively designed to handle massive threadingEvery pixel is a threadIncreased precision (fp32), programmability, flexibilityGPUs are a mass-market parallel processor

Economies of scalePeak floating point performance is much higher than comparable CPUs

Why Computation on the GPU?

ATI x1900XT$400 (video card)250 GFLOPs (SP Float)46 GB main memory BW

Intel Core 2 Duo E6600$400 (processor only)40 GFLOPS (SP Float)8.5 GB main memory BW

Why Computation on the GPU?Supercomputing Performance

Inherently Parallel Architecture1000+ cores, massively parallel processing250x the compute performance of a PC

Personal“One Researcher, One Supercomputer”Supercomputer in a desktop system Plugs into standard power strip

AccessibleProgram in C, C++, Fortran for Windows or LinuxAvailable from OEMs and resellers worldwide and priced like a workstation

Compute ApplicationsComputational Fluid DynamicsComputer Aided EngineeringDigital Content CreationElectronic Design AutomationFinanceGame PhysicsGraphicsImaging and Computer VisionMedical ImagingNumericsBio-Informatics and Life SciencesComputational ChemistryComputational Electromagnetics & Electrodynamics

Data Mining, Analytics & DatabasesMATLAB AccelerationMolecular DynamicsWeather, Atmospheric, Ocean Modeling, and Space SciencesLibrariesOil & GasProgramming ToolsRay TracingSignal ProcessingVideo & Audio

Heterogeneous Computing

Multi-CoreCPU

Parallel-CoreGPU

APIS FOR HETEROGENEOUS COMPUTING

APIs for Heterogeneous ComputingCUDA (Compute Unified Device Architecture) is a parallel computing architecture developed by NVIDIA. Programmers use 'C for CUDA' (C with NVIDIA extensions), compiled through a PathScale Open64 C compiler, to code algorithms for execution on the GPU. Both low/high level APIs are providedOpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors.Microsoft DirectCompute is an API that supports General-purpose computing on GPUs on Microsoft Win Vista or Win 7. DirectCompute is part of the Microsoft DirectX collection of APIs.

OpenCL

One Host+ one or more Compute DevicesEach Compute Device is composed of one or more Compute UnitsEach Compute Unit is further divided into one or more Processing Elements

OpenCL: Platform Model & Program Structure

CUDA Parallel Computing Architecture

ISA and hardware compute engine

Includes a C-compiler plus support for OpenCL and DX11 Compute

Architected to natively support all computational interfaces (standard languages and APIs)

Shared back-end compiler and optimization technology

OpenCL and C for CUDA

OpenCL

C for CUDA

PTX

GPU

Entry point for developers who prefer high-level C

Entry point for developers who want

low-level API

Option 1

146X

Medical Imaging

U of Utah

36X

Molecular Dynamics

U of Illinois, Urbana

18X

Video Transcoding

Elemental Tech

50X

MatlabComputing

AccelerEyes

100X

Astrophysics

RIKEN

149X

Financial simulation

Oxford

47X

Linear AlgebraUniversidad

Jaime

20X

3D UltrasoundTechniscan

130X

Quantum Chemistry

U of Illinois, Urbana

30X

Gene Sequencing

U of Maryland

CUDA Success—Science & ComputationNot 2x or 3x, but speedups are 20x to 150x

$100K - $1MAccessibility

Perfo

rman

ce

250x

< $10 K

TeslaPersonal

Supercomputer

Today’sWorkstations

1x

250xFaster

100x more affordable20x less power consumption

SupercomputingCluster

Solving the World’s Most Complex Challenges

Oil & Gas

Science

Medicine

Broadcast Space Exploration

Film

Auto Design

Grand Computing Challenges

Renewable Energy

Personalized Medicine

Mathematics for Scientific Discovery

InformationData Mining

Machines That Think

Natural Human Machine

Interaction

Predict Environmental

Changes

Economic Analysis

Final Thoughts

GPU and heterogeneous parallel architecture will revolutionize computing

Parallel computing needed to solve some of the most interesting and important human challenges ahead

Learning parallel programming is imperative for students in computing and sciences

From Virtua Fighter to Tsubame

1995 – NV1 2008 – GT2000.8M transistors 1,200M transistors

50MHz 1.3GHz

1M Bytes 4G Bytes

0 GFLOPS 1 TFLOPS

Another 1000x in 15 years?

BACKUP

Graphics API History

Open GL1992: OpenGL 1.01996: OpenGL 1.1 (Vertex Arrays, Improved Texturing)

1998: OpenGL 1.2 (3D Textures, BGRA pixel format)

1998: OpenGL 1.2.1 (Multi-Texture)

2001: OpenGL 1.3 (Multi-sample AA, Cube/Compressed Textures)

2002: OpenGL 1.4 (Depth/Shadow mapping, Auto mipmap generation)

2003: OpenGL 1.5 (Vertex Attr from Vid Mem)

2005: OpenGL 2.0 (GLSL, Vertex/Pixel Shaders, MRT, Non P-of-2 Tex)

2006: OpenGL 2.1 (GLSL1.2, sRGB Textures)

2008: OpenGL 3.0 (GLSL1.3, 32b FP Textures)

2009: OpenGL 3.1 (March 2009, GLSL1.4, Perf, CopyBufferAPI)

2009: OpenGL 3.2 (Aug 2009, GLSL1.5, Geom Shaders)

OpenGL ES

Designed for hand-held and embedded devicesGoal is smaller footprint to support OpenGLPlayStation 3 and cell phone industry adopting ES

OpenGL ES 1.1Strips out anything deemed extra in OpenGLKeeps conventional fixed-function vertex and fragment processing

OpenGL ES 2.0Adds programmable vertex and fragment shadersShaders specified in binary formatDrops support for fixed-function vertex and fragment processing

OpenGL ES – Cont

OpenGL ES 1.0 : Symbian OS, Android PlatformOpenGL ES 1.0+ : Playstation 3OpenGL ES 1.1 : iPhone SDK, Bberry (Some Models)Open GL ES 2.0 : iPhone 3GS, iPOD touch

DirectX

GDI: legacy Windows graphics API ~1985DirectX 1.0 – 1995/6 (No 3D support, DirectDraw, DirectSound, DirectInput)

DirectX 3.0 – 1996 (Rasterization only 3D Support, Akward prog. Model, Not successful)DirectX 5.0 – 1997 (Draw Primitives, DirectX vs OpenGL War)DirectX 6.0 – 1998 (Multitexture, OGL/Glide features, Texture Compression)DirectX 7.0 – 1999 (Geometry HW accleration and Blending, Cube mapping)DirectX 8.0 – 2000/1 (Programable VS/PS Shaders, XBOX)DirectX 9.0 – 2002-2003 (More programmability, Branching, FP pixel prog.)DirectX 9.0c – 2004 (ShaderModel 3.0)DirectX 10.0 – 2006 (SM4.0, WinVista, Geometry Shaders, Streaming Output)DirectX 10.1 – 2008 (SM4.1, Better Image Quality)DirectX 11.0 - 2009 (SM5.0, DirectCompute Tesselation, WinVista SP2, Win7)