UIUC CSL Global Technology Forum © NVIDIA Corporation 2007 Computing in Crisis: Challenges and Opportunities David B. Kirk.

Slides:



Advertisements
Similar presentations
Monte-Carlo method and Parallel computing  An introduction to GPU programming Mr. Fang-An Kuo, Dr. Matthew R. Smith NCHC Applied Scientific Computing.
Advertisements

Supercomputing and Sciences Rong Ge Marquette University.
IDC HPC User Forum Conference Appro Product Update Anthony Kenisky, VP of Sales.
FSOSS Dr. Chris Szalwinski Professor School of Information and Communication Technology Seneca College, Toronto, Canada GPU Research Capabilities.
PARALLEL PROCESSING COMPARATIVE STUDY 1. CONTEXT How to finish a work in short time???? Solution To use quicker worker. Inconvenient: The speed of worker.
1 Threading Hardware in G80. 2 Sources Slides by ECE 498 AL : Programming Massively Parallel Processors : Wen-Mei Hwu John Nickolls, NVIDIA.
Name: Kaiyong Zhao Supervisor: Dr. X. -W Chu. Background & Related Work Multiple-Precision Integer GPU Computing & CUDA Multiple-Precision Arithmetic.
1 ITCS 6/8010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Jan 19, 2011 Emergence of GPU systems and clusters for general purpose High Performance Computing.
Lecture 1: Introduction to High Performance Computing.
Accelerating Machine Learning Applications on Graphics Processors Narayanan Sundaram and Bryan Catanzaro Presented by Narayanan Sundaram.
Gregex: GPU based High Speed Regular Expression Matching Engine Date:101/1/11 Publisher:2011 Fifth International Conference on Innovative Mobile and Internet.
© David Kirk/NVIDIA and Wen-mei W. Hwu, ECE408/CS483, University of Illinois, Urbana-Champaign 1 ECE408 / CS483 Applied Parallel Programming.
Debunking the 100X GPU vs. CPU Myth: An Evaluation of Throughput Computing on CPU and GPU Presented by: Ahmad Lashgar ECE Department, University of Tehran.
Emergence of GPU systems for general purpose high performance computing ITCS 4145/5145 April 4, 2013 © Barry Wilkinson CUDAIntro.ppt.
Motivation “Every three minutes a woman is diagnosed with Breast cancer” (American Cancer Society, “Detailed Guide: Breast Cancer,” 2006) Explore the use.
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.
Training Program on GPU Programming with CUDA 31 st July, 7 th Aug, 14 th Aug 2011 CUDA Teaching UoM.
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007 ECE 498AL, University of Illinois, Urbana-Champaign 1 ECE 498AL Lectures 7: Threading Hardware in G80.
Chapter 2 Computer Clusters Lecture 2.3 GPU Clusters for Massive Paralelism.
CuMAPz: A Tool to Analyze Memory Access Patterns in CUDA
David Luebke NVIDIA Research GPU Computing: The Democratization of Parallel Computing.
MATLAB and the GPU Who is AccelerEyes? What’s a GPU?
Computer Graphics Graphics Hardware
BY: ALI AJORIAN ISFAHAN UNIVERSITY OF TECHNOLOGY 2012 GPU Architecture 1.
GPUs and Accelerators Jonathan Coens Lawrence Tan Yanlin Li.
By Arun Bhandari Course: HPC Date: 01/28/12. GPU (Graphics Processing Unit) High performance many core processors Only used to accelerate certain parts.
Introduction to CUDA 1 of 2 Patrick Cozzi University of Pennsylvania CIS Fall 2012.
GPU Computing April GPU Outpacing CPU in Raw Processing GPU NVIDIA GTX cores 1.04 TFLOPS CPU GPU CUDA Architecture Introduced DP HW Introduced.
© David Kirk/NVIDIA and Wen-mei W. Hwu Taiwan, June 30-July 2, 2008 Taiwan 2008 CUDA Course Programming Massively Parallel Processors: the CUDA experience.
© David Kirk/NVIDIA and Wen-mei W. Hwu Urbana, Illinois, August 18-22, 2008 VSCSE Summer School 2008 Accelerators for Science and Engineering Applications:
Applying GPU and POSIX Thread Technologies in Massive Remote Sensing Image Data Processing By: Group 17 King Mongkut's Institute of Technology Ladkrabang.
NVIDIA Tesla GPU Zhuting Xue EE126. GPU Graphics Processing Unit The "brain" of graphics, which determines the quality of performance of the graphics.
© David Kirk/NVIDIA and Wen-mei W. Hwu, ECE 498AL, University of Illinois, Urbana-Champaign 1 CS 395 Winter 2014 Lecture 17 Introduction to Accelerator.
Emergence of GPU systems and clusters for general purpose high performance computing ITCS 4145/5145 April 3, 2012 © Barry Wilkinson.
GPU Architecture and Programming
Introducing collaboration members – Korea University (KU) ALICE TPC online tracking algorithm on a GPU Computing Platforms – GPU Computing Platforms Joohyung.
© David Kirk/NVIDIA and Wen-mei W. Hwu, ECE 498AL, University of Illinois, Urbana-Champaign 1 CMPS 5433 Dr. Ranette Halverson Programming Massively.
Hardware Acceleration Using GPUs M Anirudh Guide: Prof. Sachin Patkar VLSI Consortium April 4, 2008.
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007 ECE 498AL, University of Illinois, Urbana-Champaign 1 ECE 498AL Lecture 18: Final Project Kickoff.
© David Kirk/NVIDIA and Wen-mei W. Hwu, ECE 498AL, University of Illinois, Urbana-Champaign 1 ECE 498AL Lectures 8: Threading Hardware in G80.
Introduction What is GPU? It is a processor optimized for 2D/3D graphics, video, visual computing, and display. It is highly parallel, highly multithreaded.
Carlo del Mundo Department of Electrical and Computer Engineering Ubiquitous Parallelism Are You Equipped To Code For Multi- and Many- Core Platforms?
ICAL GPU 架構中所提供分散式運算 之功能與限制. 11/17/09ICAL2 Outline Parallel computing with GPU NVIDIA CUDA SVD matrix computation Conclusion.
Compiler and Runtime Support for Enabling Generalized Reduction Computations on Heterogeneous Parallel Configurations Vignesh Ravi, Wenjing Ma, David Chiu.
Introduction to CUDA (1 of n*) Patrick Cozzi University of Pennsylvania CIS Spring 2011 * Where n is 2 or 3.
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007 ECE 498AL, University of Illinois, Urbana-Champaign 1 Final Project Notes.
GPU Programming Shirley Moore CPS 5401 Fall 2013
© David Kirk/NVIDIA and Wen-mei W. Hwu, University of Illinois, Urbana-Champaign 1 CS/EE 217 GPU Architecture and Parallel Programming Project.
CS6068 Week 2 Quiz. What are David Patterson’s Three Wall of Computer Architecture?
Havok FX Physics on NVIDIA GPUs. Copyright © NVIDIA Corporation 2004 What is Effects Physics? Physics-based effects on a massive scale 10,000s of objects.
NICS Update Bruce Loftis 16 December National Institute for Computational Sciences University of Tennessee and ORNL partnership  NICS is the 2.
© David Kirk/NVIDIA and Wen-mei W. Hwu, 2007 ECE 498AL, University of Illinois, Urbana-Champaign 1 Final Project Kickoff.
3/12/2013Computer Engg, IIT(BHU)1 CUDA-3. GPGPU ● General Purpose computation using GPU in applications other than 3D graphics – GPU accelerates critical.
Parallel Computers Today Oak Ridge / Cray Jaguar > 1.75 PFLOPS Two Nvidia 8800 GPUs > 1 TFLOPS Intel 80- core chip > 1 TFLOPS  TFLOPS = floating.
Fast and parallel implementation of Image Processing Algorithm using CUDA Technology On GPU Hardware Neha Patil Badrinath Roysam Department of Electrical.
The Limits of Volunteer Computing Dr. David P. Anderson University of California, Berkeley March 20, 2011.
Computer Engg, IIT(BHU)
Computer Graphics Graphics Hardware
Emergence of GPU systems for general purpose high performance computing ITCS 4145/5145 July 12, 2012 © Barry Wilkinson CUDAIntro.ppt.
CMSC 611: Advanced Computer Architecture
Parallel Computing Lecture
Super Computing By RIsaj t r S3 ece, roll 50.
ECE 498AL Spring 2010 Lectures 8: Threading & Memory Hardware in G80
Emergence of GPU systems for general purpose high performance computing ITCS 4145/5145 © Barry Wilkinson GPUIntro.ppt Nov 4, 2013.
The Yin and Yang of Processing Data Warehousing Queries on GPUs
Mattan Erez The University of Texas at Austin
Computer Graphics Graphics Hardware
Mattan Erez The University of Texas at Austin
CIS 6930: Chip Multiprocessor: GPU Architecture and Programming
CIS 6930: Chip Multiprocessor: Parallel Architecture and Programming
Presentation transcript:

UIUC CSL Global Technology Forum © NVIDIA Corporation 2007 Computing in Crisis: Challenges and Opportunities David B. Kirk

© NVIDIA Corporation UIUC CSL Global Technology Forum Future Science and Engineering Breakthroughs Hinge on Computing Computational Modeling Computational Chemistry Computational Medicine Computational Physics Computational Biology Computational Finance Computational Geoscience Image Processing

© NVIDIA Corporation UIUC CSL Global Technology Forum TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF TEX L1 SP Shared Memory IU SP Shared Memory IU TF L2 Memory Work Distribution Host CPU L2 Memory L2 Memory L2 Memory L2 Memory L2 Memory The Future Computing is Parallel CPU clock rate growth is slowing, future speed growth will be from parallelism GeForce-8 Series is a massively parallel computing platform 12,288 concurrent threads, hardware managed 128 Thread Processor cores at 1.35 GHz == 518 GFLOPS peak GPU Computing features enable C on Graphics Processing Unit SP

© NVIDIA Corporation UIUC CSL Global Technology Forum Implications and Opportunities Massively parallel computing allows Drastic reduction in “time to discovery” New, 3 rd paradigm for research: computational experimentation The “democratization of supercomputing” $3,000/Teraflop in personal computers today $5,000,000/Petaflops in clusters in two years HW cost will no longer be the main barrier for big science Global competition will be won with abilities to create and use parallel systems for discovery This is once-in-a-career opportunity for many! Future winner academic institutions will be leaders in research in Parallel Programming and Parallel Architecture More importantly, teach massively parallel programming to CS/ECE students, scientists and other engineers. UIUC is already uniquely positioned!