Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
SIAC 2000 Program 1 SIAC 2000 at a Glance AM Lunch PM Mon NOW HPC Tue PVMMPI Clusters Wed Condor HPVM Sun Dinner Condor Globus Clusters HPVM 2 SIAC 2000 Materials: Book 1 • David Spector, “Building Linux Clusters: Scaling Linux for Scientific and Enterprise Applications,” August 2000 • CD-ROM RedHat Linux, PVM, … • O'Reilly & Associates, ISBN: 1565926250 3 SIAC 2000 Materials: Book 2 • Rajkumar Buyya (Editor), “High Performance Cluster Computing: Programming and Applications” Volume 2, June 1999 • Prentice Hall; ISBN: 0130137855 4 SIAC 2000 Materials: Book 3 • Selection of papers – Linux clusters – Beowulf HOWTO – PVM, MPI • FAQs • Handouts from speakers 5 Introduction to Cluster Computing Prabhaker Mateti Wright State University Dayton, Ohio 6 Overview • High performance computing • High throughput computing • NOW, HPC, and HTC • Parallel algorithms • Software technologies 7 “High Performance” Computing • CPU clock frequency • Parallel computers • Alternate technologies – Optical – Bio – Molecular 8 “Parallel” Computing • Traditional supercomputers – SIMD, MIMD, pipelines – Tightly coupled shared memory – Bus level connections – Expensive to buy and to maintain • Cooperating networks of computers 9 “NOW” Computing • Workstation • Network • Operating System • Cooperation • Distributed (Application) Programs 10 Traditional Supercomputers • Very high starting cost – Expensive hardware – Expensive software • High maintenance • Expensive to upgrade 11 Traditional Supercomputers No one is predicting their demise, but … 12 Computational Grids are the future 13 Computational Grids “Grids are persistent environments that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organizations in widespread locations.” 14 Computational Grids • Individual nodes can be supercomputers, or NOW • High availability • Accommodate peak usage • LAN : Internet :: NOW : Grid 15 Globus: A Computational Grid • Lee Liming, Argonne • Monday, Aug 21, 2000, 1:00 – 5:30 PM 16 “NOW” Computing • Workstation • Network • Operating System • Cooperation • Distributed+Parallel Programs 17 Workstation? PC? Mac? 18 “Workstation Operating System” • Authenticated users • Protection of resources • Multiple processes • Preemptive scheduling • Virtual Memory • Hierarchical file systems • Network centric 19 Network • Ethernet – 10 Mbps obsolete – 100 Mbps common – 1000 Mbps desired • Protocols – TCP/IP 20 Cooperation • Workstations are “personal” • Others use slows you down •… • Willing to share • Willing to trust 21 Distributed Programs • Spatially distributed programs – A part here, a part there, … – Parallel – Synergy • Temporally distributed programs – Compute half today, half tomorrow – Combine the results at the end • Migratory programs – Have computation, will travel 22 Technological Bases of Distributed+Parallel Programs • Spatially distributed programs – Message passing • Temporally distributed programs – Shared memory • Migratory programs – Serialization of data and programs 23 Distributed Shared Memory • “Simultaneous” read/write access by spatially distributed processors • Abstraction layer of an implementation built from message passing primitives • Semantics not so clean 24 Technological Bases for Migratory programs • Same CPU architecture – X86, PowerPC, MIPS, SPARC, …, JVM • Same OS + environment • Be able to “checkpoint” – suspend, and – then resume computation – without loss of progress 25 Development of Distributed+Parallel Programs • New code + algorithms • Old programs rewritten in new languages that have distributed and parallel primitives • Parallelize legacy code 26 New Programming Languages • With distributed and parallel primitives • Functional languages • Logic languages • Data flow languages 27 Parallel Programming Languages • based on the shared-memory model • based on the distributed-memory model • parallel object-oriented languages • parallel functional programming languages • concurrent logic languages 28 PVM, and MPI • Message passing primitives • Can be embedded in many existing programming languages • Architecturally portable • Open-sourced implementations 29 PVM, and MPI • Prabhaker Mateti, WSU • Tuesday Aug 22, 2000 8:30 – 12:00 30 OpenMP • Distributed shared memory API • Implementations: Real soon now • http://www.openmp.org/ 31 SPMD • Single program, multiple data • Contrast with SIMD • Same program runs on multiple nodes • May or may not be lock-step • Nodes may be of different speeds • Barrier synchronization 32 Condor • Cooperating workstations • Migratory programs – Checkpointing – Remote IO • Resource matching 33 Condor • Prabhaker Mateti, WSU • Wednesday Aug 22, 2000 8:30 – 12:00 34 Clusters of Workstations • Inexpensive alternative to traditional supercomputers • High availability – Lower down time – Easier access • Development platform with production runs on traditional supercomputers 35 Linux Clusters • Kumaran Kalyanasundaram, SGI • Monday Aug 21, 2000 6:30 – 9:00 PM • Tuesday Aug 22, 2000 1:00 – 5:30 PM 36 HPVM Clusters • Mario Lauria • Wednesday Aug 23, 2000 1:00 – 4:00 PM 37