Unique Opportunities in Experimental Computer Systems Research - the Berkeley Testbeds David Culler U.C. Berkeley Grad.

Slides:



Advertisements
Similar presentations
1 Uniform memory access (UMA) Each processor has uniform access time to memory - also known as symmetric multiprocessors (SMPs) (example: SUN ES1000) Non-uniform.
Advertisements

2. Computer Clusters for Scalable Parallel Computing
High Performance Computing Course Notes Grid Computing.
©2014 Extreme Networks, Inc. All rights reserved. Extreme Networks Optimized Networks Kevin Kuenker, Solutions Architect – Central Region.
Technical Review Group (TRG)Agenda 27/04/06 TRG Remit Membership Operation ICT Strategy ICT Roadmap.
IBM RS6000/SP Overview Advanced IBM Unix computers series Multiple different configurations Available from entry level to high-end machines. POWER (1,2,3,4)
Millennium Overview and Status David Culler and Jim Demmel Computer Science Division
NPACI Panel on Clusters David E. Culler Computer Science Division University of California, Berkeley
Millennium Overview and Status David Culler and Jim Demmel Computer Science Division
Millennium: Computer Systems, Computational Science and Engineering in the Large David Culler, J. Demmel, E. Brewer, J. Canny, A. Joseph, J. Landay, S.
Millennium: Cluster Technology for Computational Science and Engineering David Culler E. Brewer, J. Canny, J. Demmel, A. Joseph, J. Landay, S. McCanne.
IBM / UCB EECS Collaboration Meeting May 11, 1999 David E. Culler Computer Science Division U.C. Berkeley.
Towards I-Space Ninja Mini-Retreat June 11, 1997 David Culler, Steve Gribble, Mark Stemm, Matt Welsh Computer Science Division U.C. Berkeley.
NOW 1 Berkeley NOW Project David E. Culler Sun Visit May 1, 1998.
1 Dr. Frederica Darema Senior Science and Technology Advisor NSF Future Parallel Computing Systems – what to remember from the past RAMP Workshop FCRC.
NOW and Beyond Workshop on Clusters and Computational Grids for Scientific Computing David E. Culler Computer Science Division Univ. of California, Berkeley.
Finale’ cs294-8 Design of Deeply Networked Systems Spring 2000 David Culler & Randy Katz U.C. Berkeley
ProActive Infrastructure Eric Brewer, David Culler, Anthony Joseph, Randy Katz Computer Science Division U.C. Berkeley ninja.cs.berkeley.edu Active Networks.
MS 9/19/97 implicit coord 1 Implicit Coordination in Clusters David E. Culler Andrea Arpaci-Dusseau Computer Science Division U.C. Berkeley.
Connecting the Invisible Extremes of Computing David Culler U.C. Berkeley Summer Inst. on Invisible Computing July,
IPPS 981 Berkeley FY98 Resource Working Group David E. Culler Computer Science Division U.C. Berkeley
Grids and Grid Technologies for Wide-Area Distributed Computing Mark Baker, Rajkumar Buyya and Domenico Laforenza.
TITAN: A Next-Generation Infrastructure for Integrating and Communication David E. Culler Computer Science Division U.C. Berkeley NSF Research Infrastructure.
Clusters Massive Cluster Gigabit Ethernet System Design for Vastly Diverse Devices David Culler U.C. Berkeley HP Visit 3/9/2000.
Little Demonstration of the Power in Discovery Jason Hill, Steve Ross David E. Culler Computer Science Division U.C. Berkeley.
CS : Creating the Grid OS—A Computer Science Approach to Energy Problems David E. Culler, Randy H. Katz University of California, Berkeley August.
PRASHANTHI NARAYAN NETTEM.
Packing for the Expedition David Culler. 5/25/992 Ongoing Endeavors Millennium: building a large distributed experimental testbed –Berkeley Cluster Software.
Chiba City: A Testbed for Scalablity and Development FAST-OS Workshop July 10, 2002 Rémy Evard Mathematics.
NPACI: National Partnership for Advanced Computational Infrastructure August 17-21, 1998 NPACI Parallel Computing Institute 1 Cluster Archtectures and.
DISTRIBUTED COMPUTING
Design and Implementation of a Single System Image Operating System for High Performance Computing on Clusters Christine MORIN PARIS project-team, IRISA/INRIA.
Distributed Systems Early Examples. Projects NOW – a Network Of Workstations University of California, Berkely Terminated about 1997 after demonstrating.
SEDA: An Architecture for Well-Conditioned, Scalable Internet Services
Seaborg Cerise Wuthrich CMPS Seaborg  Manufactured by IBM  Distributed Memory Parallel Supercomputer  Based on IBM’s SP RS/6000 Architecture.
1 Configurable Security for Scavenged Storage Systems NetSysLab The University of British Columbia Abdullah Gharaibeh with: Samer Al-Kiswany, Matei Ripeanu.
 Protocols used by network systems are not effective to distributed system  Special requirements are needed here.  They are in cases of: Transparency.
Peer-to-Peer Distributed Shared Memory? Gabriel Antoniu, Luc Bougé, Mathieu Jan IRISA / INRIA & ENS Cachan/Bretagne France Dagstuhl seminar, October 2003.
Module 11: Implementing ISA Server 2004 Enterprise Edition.
Amy Apon, Pawel Wolinski, Dennis Reed Greg Amerson, Prathima Gorjala University of Arkansas Commercial Applications of High Performance Computing Massive.
Advanced Computer Networks Topic 2: Characterization of Distributed Systems.
Copyright © 2002 Intel Corporation. Intel Labs Towards Balanced Computing Weaving Peer-to-Peer Technologies into the Fabric of Computing over the Net Presented.
Issues Autonomic operation (fault tolerance) Minimize interference to applications Hardware support for new operating systems Resource management (global.
PARALLEL COMPUTING overview What is Parallel Computing? Traditionally, software has been written for serial computation: To be run on a single computer.
CLUSTER COMPUTING TECHNOLOGY BY-1.SACHIN YADAV 2.MADHAV SHINDE SECTION-3.
SimMillennium Project Overview David E. Culler Computer Science Division U.C. Berkeley NSF Site Visit March 2, 1998.
Time This powerpoint presentation has been adapted from: 1) sApr20.ppt.
ProActive Infrastructure Eric Brewer, David Culler, Anthony Joseph, Randy Katz Computer Science Division U.C. Berkeley ninja.cs.berkeley.edu Active Networks.
Millennium Executive Committee Meeting David E. Culler Computer Science Division
Grid Computing Unit I Introduction. Information anytime anywhere!!! support computation across administrative domains Generally  virtualizing computing.
COMP381 by M. Hamdi 1 Clusters: Networks of WS/PC.
3/12/2013Computer Engg, IIT(BHU)1 PARALLEL COMPUTERS- 2.
Università di Perugia Enabling Grids for E-sciencE Status of and requirements for Computational Chemistry NA4 – SA1 Meeting – 6 th April.
Societal-Scale Computing: The eXtremes Scalable, Available Internet Services Information Appliances Client Server Clusters Massive Cluster Gigabit Ethernet.
Cluster computing. 1.What is cluster computing? 2.Need of cluster computing. 3.Architecture 4.Applications of cluster computing 5.Advantages of cluster.
Background Computer System Architectures Computer System Software.
INTRODUCTION TO GRID & CLOUD COMPUTING U. Jhashuva 1 Asst. Professor Dept. of CSE.
Lecture 13 Parallel Processing. 2 What is Parallel Computing? Traditionally software has been written for serial computation. Parallel computing is the.
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING CLOUD COMPUTING
Clouds , Grids and Clusters
Berkeley Cluster Projects
U.C. Berkeley Millennium Project
Scaling for the Future Katherine Yelick U.C. Berkeley, EECS
University of Technology
System G And CHECS Cal Ribbens
Chapter 7: Consistency & Replication IV - REPLICATION MANAGEMENT -Sumanth Kandagatla Instructor: Prof. Yanqing Zhang Advanced Operating Systems (CSC 8320)
IBM Pervasive Computing Visit June 9, 1997
Results of Prior NSF RI Grant: TITAN
IBM Pervasive Computing Visit Jan 7, 1999
CLUSTER COMPUTING.
Presentation transcript:

Unique Opportunities in Experimental Computer Systems Research - the Berkeley Testbeds David Culler U.C. Berkeley Grad Student Presentations 8/27/1999

8/26/99Grad Presentation2 Emerging Problems of Scale Scalable, Available Internet Services Info. appliances Client Server Clusters Massive Cluster Gigabit Ethernet

8/26/99Grad Presentation3 Convergence at the Extremes Powerful Services on “Small” Devices –massive computing and storage in the infrastructure –active adaptation of form and content “along the way” Extremes more alike that either is to the middle –More specialized in function –Communication centric design »wide range of networking options –Federated System of Many Many Systems –Hands-off operation, mgmt, development –High Reliability, Availability –Scalability –Power and space limited –simplicity They have to “work or die!”

8/26/99Grad Presentation4 100 node Ultra/Myrinet NOW GLUnix Active Messages xFS Fast sockets, MPI, and SVM Titanium and Split-C ScaLapack

8/26/99Grad Presentation5 Novel Systems Design Virtual networks –integrate communication events into virtual memory system Implicit Co-scheduling –cause local schedulers to co-schedule parallel computations using a two-phase spin-block and observing round-trip Co-operative caching –access remote caches, rather than local disk, and enlarge global cache coverage by simple cooperation Reactive Scalable I/O Network virtual memory, fast sockets ISAAC “active” security Internet Server Architecture TACC Proxy architecture

8/26/99Grad Presentation6 The Millennium Vision To work, think, and study in a computationally rich environment with deep information stores and powerful services –test ideas through simulation –explore and investigate data and information –share, manipulate, and interact through natural actions Organized in a manner consistent with the University setting –clusters of clusters –Computational Economy Novel modes of interacting with large amounts of data

8/26/99Grad Presentation7 The Millennium Community School of Info. Mgmt and Sys. Computer Science Electrical Eng. Mechanical Eng. BMRC Nuclear Eng. IEOR Civil Eng. MSME Inst. Of Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation8 NT Workstations for Sci. & Eng. SIMS C.S. E.E. M.E. BMRC N.E. IEOR C. E. MSME Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation9 SMP => storage, small-scale parallelism SIMS C.S. E.E. M.E. BMRC N.E. IEOR C. E. MSME Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation10 Group Cluster of SMPs => Parallelism SIMS C.S. E.E. M.E. BMRC N.E. IEOR C. E. MSME NERSC Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation11 Campus Cluster => large-scale Parallelism SIMS C.S. E.E. M.E. BMRC N.E. IEOR C. E. MSME NERSC Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation12 Gigabit Ethernet Connectivity Gigabit Ethernet SIMS C.S. E.E. M.E. BMRC N.E. IEOR C. E. MSME NERSC Transport Business Chemistry Astro Physics Biology Economy Math

8/26/99Grad Presentation13 FIAT LUX: crossing areas Combines –Image Based Modeling and Rendering, –Image Based Lighting, –Dynamics Simulation and –Global Illumination in a completely novel fashion to achieve unprecedented levels of scientific accuracy and realism Computing Requirements –15 Days of worth of time for development. –5 Days for rendering Final piece. –4 Days for rendering in HDTV resolution on 140 Processors Storage –72,000 Frames, 108 Gigabytes of storage –7.2 Gigs after motion blur –500 MB JPEG premiere at the SIGGRAPH 99 Electronic Theater –

8/26/99Grad Presentation14 An upcoming Comp. Econ Experiment Two identical 32 proc Millennium Clusters One open shop One with usage based on bid-based proportional share scheduling

8/26/99Grad Presentation15 Ninja: Push Services into an Active Infrastr. Servers Clients Servers Infrastructure Services Open => enable Distributed Innovation of Scalable, Avail. Services

8/26/99Grad Presentation16 Universal Computing Lab (464 Soda) Computing in the infra, in the walls, on the desk, in your hand,... Not just a new project, a new computing culture

8/26/99Grad Presentation17 universal Function: adjective 1 : including or covering all or a whole collectively or distributively without limit or exception 2 a : present or occurring everywhere b : existent or operative everywhere or under all conditions 3 a : embracing a major part or the greatest portion (as of mankind) b : comprehensively broad and versatile 4 a : affirming or denying something of all members of a class or of all values of a variable b : denoting every member of a class 5 : adapted or adjustable to meet varied requirements (as of use, shape, or size)