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1 ITCS 4/5010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Dec 31, 2012 Emergence of GPU systems and clusters for general purpose High Performance Computing.

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Presentation on theme: "1 ITCS 4/5010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Dec 31, 2012 Emergence of GPU systems and clusters for general purpose High Performance Computing."— Presentation transcript:

1 1 ITCS 4/5010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Dec 31, 2012 Emergence of GPU systems and clusters for general purpose High Performance Computing These notes will introduce: The development of GPU devices from the 1970’s to the present day Their use in high performance computers today

2 2 CPU-GPU architecture evolution 1970s - 1980s Co-processors -- very old idea that appeared in 1970s and 1980s with floating point co- processors attached to microprocessors that did not then have floating point capability. These coprocessors simply executed floating point instructions that were fetched from memory. Around same time, interest to provide hardware support for displays, especially with increasing use of graphics and PC games. Led to graphics processing units (GPUs) attached to CPU to create video display. CPU Graphics card Display Memory Early design

3 3 Pipelined programmable GPU Dedicated pipeline (late1990s-early 2000s) By late1990’s, graphics chips needed to support 3-D graphics, especially for games and graphics APIs such as DirectX and OpenGL. Graphics chips generally had a pipeline structure with individual stages performing specialized operations, finally leading to loading frame buffer for display. Individual stages may have access to graphics memory for storing intermediate computed data. Input stage Vertex shader stage Geometry shader stage Rasterizer stage Frame buffer Pixel shading stage Graphics memory

4 Graphics Processing Units (GPUs) Brief History 1970 2010 200019901980 Atari 8-bit computer text/graphics chip Source of information http://en.wikipedia.org/wiki/Graphics_Processing_Unit IBM PC Professional Graphics Controller card S3 graphics cards- single chip 2D accelerator OpenGL graphics API Hardware-accelerated 3D graphics DirectX graphics API Playstation GPUs with programmable shading Nvidia GeForce GE 3 (2001) with programmable shading General-purpose computing on graphics processing units (GPGPUs) GPU Computing

5 NVIDIA products NVIDIA Corp. is the leader in GPUs for high performance computing: 1993201019991995 http://en.wikipedia.org/wiki/GeForce 2009200720082000200120022003200420052006 Established by Jen- Hsun Huang, Chris Malachowsky, Curtis Priem NV1GeForce 1 GeForce 2 series GeForce FX series GeForce 8 series GeForce 200 series GeForce 400 series GTX460/465/470/475/ 480/485 GTX260/275/280/285/295 GeForce 8800 GT 80 Tesla Quadro NVIDIA's first GPU with general purpose processors C870, S870, C1060, S1070, C2050, … Tesla 2050 GPU has 448 thread processors Fermi Kepler (2011) Maxwell (2013)

6 6 GeForce 6 Series Architecture (2004-5) From GPU Gems 2, Copyright 2005 by NVIDIA Corporation

7 7 General-Purpose GPU designs High performance pipelines call for high-speed (IEEE) floating point operations. People tried to use GPU cards to speed up scientific computations Known as GPGPU (General-purpose computing on graphics processing units) -- Difficult to do with specialized graphics pipelines, but possible.) By mid 2000’s, recognized that individual stages of graphics pipeline could be implemented by a more general purpose processor core (although with a data-parallel paradigm) a

8 8 NVIDIA GT 80 chip/GeForce 8800 card (2006) First GPU for high performance computing as well as graphics Unified processors that could perform vertex, geometry, pixel, and general computing operations Could now write programs in C rather than graphics APIs. Single-instruction multiple thread (SIMT) prog. model

9 9 GPU performance gains over CPUs T12 Westmere NV30 NV40 G70 G80 GT200 3GHz Dual Core P4 3GHz Core2 Duo 3GHz Xeon Quad Source © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007-2009 ECE 498AL Spring 2010, University of Illinois, Urbana-Champaign

10 10 * Whitepaper NVIDIA’s Next Generation CUDA Compute Architecture: Fermi, NVIDIA, 2008 Data parallel single instruction multiple data operation (“Stream” processing) Up to 512 cores (“stream processing engines”, SPEs, organized as 16 SPEs, each having 32 SPEs) 3GB or 6 GB GDDR5 memory Many innovations including L1/L2 caches, unified device memory addressing, ECC memory, … First implementation: Tesla 20 series (single chip C2050/2070, 4 chip S2050/2070) 3 billion transistor chip? Number of cores limited by power considerations, C2050 has 448 cores. Evolving GPU design: NVIDIA Fermi architecture (announced Sept 2009)

11 11 NVIDIA Kepler architecture and GPUs (2012) A lot of major new features over earlier Fermi architecture – will look at them later in course GeForce 600 series card introduced early 2012. GTX 680 has 1536 cores, 195 watts. Introduced March 2012. GXT 690 has two dies, 3072 cores (2 x 1536 cores), 300 watts. Introduced April 2012. CUDA Computer Capability 3.0 see next http://en.wikipedia.org/wiki/GeForce_600_Series http://www.tomshardware.com/news/Nvid ia-Kepler-GK104-GeForce-GTX-670- 680,14691.html GK104 chip with 1536 cores

12 12 Tesla K20 GPU Computing modules Kepler architecture. Introduced November 2012 K20 – 2496 thread processors (cores) K20X – 2688 thread processors (cores) K20: 2496 FP32 cores, 832 FP64 cores Wattage 225 watts 3.5 compute capability GFLOPs: Single Precision: 3519 /4106 Double Precision: 1173

13 13 18,688 NVIDIA Tesla K20X GPUs 20 petaflops Upgraded from Jaguar supercomputer. 10 times faster and 5 times more energy efficient than 2.3-petaflops Jaguar system while occupying the same floor space. Titan Supercomputer Oak Ridge National Laboratory in Oak Ridge, Tenn World’s fastest computer as of Nov 2012 No 1 rank on TOP500 list http://nvidianews.nvidia.com/ Releases/NVIDIA-Powers- Titan-World-s-Fastest- Supercomputer-For-Open- Scientific-Research- 8a0.aspx#source=pr

14 14 CUDA (Compute Unified Device Architecture) Architecture and programming model introduced in NVIDIA in 2007 Enables GPUs to execute programs written in C. Within C programs, call SIMT “kernel” routines that are executed on GPU. CUDA syntax extension to C identify routine as a Kernel. Very easy to learn although to get highest possible execution performance requires understanding of hardware architecture. Version 3 introduced in 2009 – the one we have been using Current version 4 introduced 2011 – significant additions including “unified virtual addressing” – a single address space across GPU and host, see later. We will go into CUDA in detail later and have programming experiences.

15 15 2010: NVIDIA Corp. selected UNC- Charlotte Department of Computer Science to be a CUDA Teaching Center, kindly providing GPU equipment and TA support. 2011: NVIDIA kindly provided 50 GTX 480 GPU cards valued at $15,000 as continuing support for the CUDA Teaching Center. 2012: NVIDIA donates a K20! UNC-C CUDA Teaching Center Our course materials are posted on NVIDIA’s corporate site next to those from Stanford, and other top schools.

16 16 http://developer.nvidia.com/cuda-training

17 Questions


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