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Training Program on GPU Programming with CUDA

Training Program on GPU Programming with CUDA. 31 st July, 7 th Aug, 14 th Aug 2011 CUDA Teaching Center @ UoM. Day 1, Session 1 Introduction. Training Program on GPU Programming with CUDA. Sanath Jayasena CUDA Teaching Center @ UoM. Outline. Training Program Description

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Training Program on GPU Programming with CUDA

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  1. Training Program onGPU Programming with CUDA 31st July, 7th Aug, 14th Aug 2011 CUDA Teaching Center @ UoM

  2. Day 1, Session 1 Introduction Training Program on GPU Programming with CUDA Sanath Jayasena CUDA Teaching Center @ UoM

  3. Outline • Training Program Description • CUDA Teaching Center at UoM Subject Matter • Introduction to GPU Computing • GPU Computing with CUDA • CUDA Programming Basics CUDA Training Program

  4. Overview of Training Program • 3 Sundays, starting 31st July • Schedule and program outline • Main resource persons • Sanath Jayasena, Jayathu Samarawickrama, Kishan Wimalawarna, Lochandaka Ranathunga • Dept of Computer Science & Eng, Dept of Electronic & Telecom. Engineering (of Faculty of Engineering) and Faculty of IT CUDA Training Program

  5. CUDA Teaching Center • UoM was selected as a CTC • A group of people from multiple Depts • http://research.nvidia.com/content/cuda-teaching-centers • Benefits • Donation of hardware by NVIDIA (GeForce GTX480s and Tesla C2070) • Access to other resources • Expectations • Use of the resources for teaching/research, industry collaboration CUDA Training Program

  6. GPU Computing: Introduction • Graphics Processing Units (GPUs) • high-performance many-core processors that can be used to accelerate a wide range of applications • GPGPU - General-Purpose computation on Graphics Processing Units • GPUs lead the race for floating-point performance since start of 21st century • GPUs are being used as parallel processors CUDA Training Program

  7. GPU Computing: Introduction • General computing, until end of 20th century • Relied on the advances in hardware to increase the speed of software/apps • Slowed down since then due to • Power consumption issues • Limited productivity within a single processor • Switch to multi-core and many-core models • Multiple processing units (processor cores) used in each chip to increase the processing power • Impact on software developers? CUDA Training Program

  8. GPU Computing: Introduction • A sequential program will only run on one of the cores, which will not become any faster • With each new generation of processors • Software that will continue to enjoy performance improvement will be parallel programs • Where, multiple threads of execution cooperate to achieve the functionality faster CUDA Training Program

  9. CPU-GPU Performance Gap Source: CUDA Prog. Guide 4.0 CUDA Training Program

  10. CPU-GPU Performance Gap Source: CUDA Prog. Guide 4.0 CUDA Training Program

  11. GPGPU & CUDA • GPU designed as a numeric computing engine • Will not perform well on some tasks as CPUs • Most applications will use both CPUs and GPUs • CUDA • NVIDIA’s parallel computing architecture aimed at increasing computing performance by harnessing the power of the GPU • A programming model CUDA Training Program

  12. More Details on GPUs • GPU is typically a computer card, installed into a PCI Express 16x slot • Market leaders: NVIDIA, Intel, AMD (ATI) • Example NVIDIA GPUs (donated to UoM) GeForce GTX 480 Tesla 2070 CUDA Training Program

  13. Example Specifications CUDA Training Program

  14. CPU vs. GPU Architecture The GPU devotes more transistors for computation CUDA Training Program

  15. CPU-GPU Communication CUDA Training Program

  16. CUDA Architecture • CUDA is NVIDA’s solution to access the GPU • Can be seen as an extension to C/C++ CUDA Software Stack CUDA Training Program

  17. CUDA Architecture • There are two main parts • Host (CPU part) • -Single Program, Single Data • Device (GPU part) • -Single Program, Multiple Data CUDA Training Program

  18. CUDA Architecture • The Grid • A group of threads all running • the same kernel • Can run multiple grids at once The Block Grids composed of blocks Each block is a logical unit containing a number of coordinating threads and some amount of shared memory GRID Architecture CUDA Training Program

  19. Some Applications of GPGPU Computational Structural Mechanics Bio-Informatics and Life Sciences Computational Electromagnetics and Electrodynamics Computational Finance CUDA Training Program

  20. Some Applications… Computational Fluid Dynamics Data Mining, Analytics, and Databases Imaging and Computer Vision Medical Imaging CUDA Training Program

  21. Some Applications… Molecular Dynamics Numerical Analytics Weather, Atmospheric, Ocean Modeling and Space Sciences CUDA Training Program

  22. CUDA ProgrammingBasics

  23. Accessing/Using the CUDA-GPUs • You have been given access to our cluster • User accounts on 192.248.8.13x • It is a Linux system • CUDA Toolkit and SDK for development • Includes CUDA C/C++ compiler for GPUs (“nvcc”) • Will need C/C++ compiler for CPU code • NVIDIA device drivers needed to run programs • For programs to communicate with hardware CUDA Training Program

  24. Example Program 1 #include <cuda.h> #include <stdio.h> __global__ void kernel (void) { } int main (void) { kernel <<< 1, 1 >>> (); printf("Hello World!\n"); return 0; } • “__global__” says the function is to be compiled to run on a “device” (GPU), not “host” (CPU) • Angle brackets “<<<“ and “>>>” for passing params/args to runtime A function executed on the GPU (device) is usually called a “kernel” CUDA Training Program

  25. Example Program 2 – Part 1 • As can be seen in next slide: • We can pass parameters to a kernel as we would with any C function • We need to allocate memory to do anything useful on a device, such as return values to the host CUDA Training Program

  26. Example Program 2 – Part 2 int main (void) { int c, *dev_c; cudaMalloc ((void **) &dev_c, sizeof (int)); add <<< 1, 1 >>> (2,7, dev_c); cudaMemcpy(&c, dev_c, sizeof(int), cudaMemcpyDeviceToHost); printf(“2 + 7 = %d\n“, c); cudaFree(dev_c); return 0; } CUDA Training Program

  27. Example Program 3 Within host (CPU) code, call the kernel by using <<< and >>> specifying the grid size (number of blocks) and/or the block size (number of threads) - (more details later) CUDA Training Program

  28. Example Program 3 …contd Note: Details on threads and thread IDs will come later CUDA Training Program

  29. Example Program 4 CUDA Training Program

  30. Grids, Blocks and Threads • A grid of size 6 (3x2 blocks) • Each block has 12 threads (4x3) CUDA Training Program

  31. Conclusion • In this session we discussed • Introduction to GPU Computing • GPU Computing with CUDA • CUDA Programming Basics • Next session • Data Parallelism • CUDA Programming Model • CUDA Threads CUDA Training Program

  32. References for this Session • Chapters 1 and 2 of: D. Kirk and W. Hwu, Programming Massively Parallel Processors, Morgan Kaufmann, 2010 • Chapters 1-4 of: E. Kandrot and J. Sanders, CUDA by Example, Addison-Wesley, 2010 • Chapters 1-2 of: NVIDIACUDA C Programming Guide, NVIDIA Corporation, 2006-2011 (Versions 3.2 and 4.0) CUDA Training Program

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