1. Emergence of GPU systems and clusters for general purpose high performance computing ITCS 4145/5145 April 3, 2012 © Barry Wilkinson

2. 2 Graphic processing units(GPUs) for high performance computing  Single computers with GPU cards  GPU clusters  GPU Grids (Geographically distributed resources used collectively, see http://coitweb.uncc.edu/~abw/gridcourse/index.html)  GPU Clouds With advent of graphic processing units (GPUs) for scientific high performance computing, can incorporate GPUs into systems, greatly increasing their compute capability.

3. 3 Many if not most high performance clusters use GPUs for HPC Fastest computer systems in the world Uses NVIDIA GPUs Japanese http://top500.org/ Chinese Tianhe-1A (was #1 now #2) has 7168 NVIDIA M2050 GPUs

4. coit-grid01.uncc.edu – coit-grid7.uncc.edu cluster coit- grid01 switch coit- grid05 coit- grid03 coit- grid02 coit- grid04 All user’s home directories on coit- grid05 (NFS) coit- grid06 NVIDIA Tesla GPU (C2050 448 core Fermi) Login from within the campus only Login from on-campus or off-campus Use coit-grid01.uncc.edu coit- grid07 NVIDIA Tesla GPU (C2050 448 core Fermi) coit-grid07: GPU server, X5560 2.8GHz quad-core Xeon processor with NVIDIA 2050 GPU, 12GB main memory GPU servers grid06 and grid07 for HPC GPU programming Will also use Windows machines in lab 335, which have mid-level (48-core) NVIDIA cards

5. 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

6. 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)

7. 7 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

8. 8 CPU-GPU architecture evolution 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

9. 9 Birth of general purpose 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

10. 10 GeForce 6 Series Architecture (2004-5) From GPU Gems 2, Copyright 2005 by NVIDIA Corporation

11. 11 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

12. 12 2006 -- First GPU for general high performance computing as well as graphics processing, NVIDIA GT 80 chip/GeForce 8800 card. 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) programming model GPU design for general high performance computing

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14. 14 Evolving GPU design NVIDIA Fermi architecture (announced Sept 2009) 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.

15. 15 Fermi Streaming Multiprocessor (SM) * Whitepaper NVIDIA’s Next Generation CUDA Compute Architecture: Fermi, NVIDIA, 2008

16. 16 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

17. 17 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. UNC-C CUDA Teaching Center Our course materials are posted on NVIDIA’s corporate site next to those from Stanford, and other top schools.

18. 18 http://developer.nvidia.com/cuda-training

19. Questions