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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

Training Program onGPU Programming with CUDA

31st July, 7th Aug, 14th Aug 2011

CUDA Teaching Center @ UoM

training program on gpu programming with cuda1

Day 1, Session 1

Introduction

Training Program on GPU Programming with CUDA

Sanath Jayasena

CUDA Teaching Center @ UoM

outline
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

overview of training program
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

cuda teaching center
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

gpu computing introduction
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

gpu computing introduction1
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

gpu computing introduction2
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

cpu gpu performance gap
CPU-GPU Performance Gap

Source: CUDA Prog. Guide 4.0

CUDA Training Program

cpu gpu performance gap1
CPU-GPU Performance Gap

Source: CUDA Prog. Guide 4.0

CUDA Training Program

gpgpu cuda
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

more details on gpus
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

example specifications
Example Specifications

CUDA Training Program

cpu vs gpu architecture
CPU vs. GPU Architecture

The GPU devotes more transistors for computation

CUDA Training Program

cpu gpu communication
CPU-GPU Communication

CUDA Training Program

cuda architecture
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

cuda architecture1
CUDA Architecture
  • There are two main parts
  • Host (CPU part)
      • -Single Program, Single Data
  • Device (GPU part)
      • -Single Program, Multiple Data

CUDA Training Program

cuda architecture2
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

some applications of gpgpu
Some Applications of GPGPU

Computational Structural Mechanics

Bio-Informatics and Life Sciences

Computational Electromagnetics and

Electrodynamics

Computational Finance

CUDA Training Program

some applications
Some Applications…

Computational Fluid Dynamics

Data Mining, Analytics, and Databases

Imaging and Computer Vision

Medical Imaging

CUDA Training Program

some applications1
Some Applications…

Molecular Dynamics

Numerical Analytics

Weather, Atmospheric, Ocean Modeling

and Space Sciences

CUDA Training Program

accessing using the cuda gpus
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

example program 1
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

example program 2 part 1
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

example program 2 part 2
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

example program 3
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

example program 3 contd
Example Program 3 …contd

Note:

Details on threads and thread IDs will come later

CUDA Training Program

example program 4
Example Program 4

CUDA Training Program

grids blocks and threads
Grids, Blocks and Threads
  • A grid of size 6 (3x2 blocks)
  • Each block has 12 threads (4x3)

CUDA Training Program

conclusion
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

references for this session
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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