1 / 17

In Summary

In Summary. Need more computing power Improve the operating speed of processors & other components constrained by the speed of light, thermodynamic laws, & the high financial costs for processor fabrication Connect multiple processors together & coordinate their computational efforts

caron
Download Presentation

In Summary

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. In Summary • Need more computing power • Improve the operating speed of processors & other components • constrained by the speed of light, thermodynamic laws, & the high financial costs for processor fabrication • Connect multiple processors together & coordinate their computational efforts • parallel computers • allow the sharing of a computational task among multiple processors

  2. Technology Trends... • Performance of PC/Workstations components has almost reached performance of those used in supercomputers… • Microprocessors (50% to 100% per year) • Networks (Gigabit SANs); • Operating Systems (Linux,...); • Programming environment (MPI,…); • Applications (.edu, .com, .org, .net, .shop, .bank); • The rate of performance improvements of commodity systems is much rapid compared to specialized systems.

  3. Technology Trends

  4. Trend • [Traditional Usage] Workstations with Unix for science & industry vs PC-based machines for administrative work & work processing • [Trend] A rapid convergence in processor performance and kernel-level functionality of Unix workstations and PC-based machines

  5. Rise and Fall of Computer Architectures • Vector Computers (VC) - proprietary system: • provided the breakthrough needed for the emergence of computational science, buy they were only a partial answer. • Massively Parallel Processors (MPP) -proprietary systems: • high cost and a low performance/price ratio. • Symmetric Multiprocessors (SMP): • suffers from scalability • Distributed Systems: • difficult to use and hard to extract parallel performance. • Clusters - gaining popularity: • High Performance Computing - Commodity Supercomputing • High Availability Computing - Mission Critical Applications

  6. The Dead Supercomputer Societyhttp://www.paralogos.com/DeadSuper/ • Dana/Ardent/Stellar • Elxsi • ETA Systems • Evans & Sutherland Computer Division • Floating Point Systems • Galaxy YH-1 • Goodyear Aerospace MPP • Gould NPL • Guiltech • Intel Scientific Computers • Intl. Parallel Machines • KSR • MasPar • ACRI • Alliant • American Supercomputer • Ametek • Applied Dynamics • Astronautics • BBN • CDC • Convex • Cray Computer • Cray Research (SGI?Tera) • Culler-Harris • Culler Scientific • Cydrome • Meiko • Myrias • Thinking Machines • Saxpy • Scientific Computer Systems (SCS) • Soviet Supercomputers • Suprenum Convex C4600

  7. Computer Food Chain: Causing the demise of specialize systems • Demise of mainframes, supercomputers, & MPPs

  8. Towards Clusters The promise of supercomputing to the average PC User ?

  9. Towards Commodity Parallel Computing • linking together two or more computers to jointly solve computational problems • since the early 1990s, an increasing trend to move away from expensive and specialized proprietary parallel supercomputers towards clusters of workstations • Hard to find money to buy expensive systems • the rapid improvement in the availability of commodity high performance components for workstations and networks • Low-cost commodity supercomputing • from specialized traditional supercomputing platforms to cheaper, general purpose systems consisting of loosely coupled components built up from single or multiprocessor PCs or workstations

  10. History: Clustering of Computers for Collective Computing PDA Clusters 1990 1995+ 2000+ 1980s 1960

  11. Why PC/WS Clustering Now ? • Individual PCs/workstations are becoming increasing powerful • Commodity networks bandwidth is increasing and latency is decreasing • PC/Workstation clusters are easier to integrate into existing networks • Typical low user utilization of PCs/WSs • Development tools for PCs/WS are more mature • PC/WS clusters are a cheap and readily available • Clusters can be easily grown

  12. What is Cluster ? • A cluster is a type of parallel or distributed processing system, which consists of a collection of interconnected stand-alone computers cooperatively working together as a single, integrated computing resource. • A node • a single or multiprocessor system with memory, I/O facilities, & OS • generally 2 or more computers (nodes) connected together • in a single cabinet, or physically separated & connected via a LAN • appear as a single system to users and applications • provide a cost-effective way to gain features and benefits

  13. PC/Workstation PC/Workstation PC/Workstation PC/Workstation Communications Software Communications Software Communications Software Communications Software Network Interface Hardware Network Interface Hardware Network Interface Hardware Network Interface Hardware Cluster Architecture Parallel Applications Parallel Applications Parallel Applications Sequential Applications Sequential Applications Sequential Applications Parallel Programming Environment Cluster Middleware (Single System Image and Availability Infrastructure) Cluster Interconnection Network/Switch

  14. So What’s So Different about Clusters? • Commodity Parts? • Communications Packaging? • Incremental Scalability? • Independent Failure? • Intelligent Network Interfaces? • Complete System on every node • virtual memory • scheduler • files • … • Nodes can be used individually or jointly...

  15. Windows of Opportunities • Parallel Processing • Use multiple processors to build MPP/DSM-like systems for parallel computing • Network RAM • Use memory associated with each workstation as aggregate DRAM cache • Software RAID • Redundant array of inexpensive disks • Use the arrays of workstation disks to provide cheap, highly available, & scalable file storage • Possible to provide parallel I/O support to applications • Use arrays of workstation disks to provide cheap, highly available, and scalable file storage • Multipath Communication • Use multiple networks for parallel data transfer between nodes

  16. Cluster Design Issues • Enhanced Performance (performance @ low cost) • Enhanced Availability (failure management) • Single System Image (look-and-feel of one system) • Size Scalability (physical & application) • Fast Communication (networks & protocols) • Load Balancing (CPU, Net, Memory, Disk) • Security and Encryption (clusters of clusters) • Distributed Environment (Social issues) • Manageability (admin. And control) • Programmability (simple API if required) • Applicability (cluster-aware and non-aware app.)

  17. Summary: Cluster Advantage • Price/performance ratio is low when compared with a dedicated parallel supercomputer. • Incremental growth that often matches with the demand patterns. • The provision of a multipurpose system • Scientific, commercial, Internet applications • Have become mainstream enterprise computing systems: • In 2003 List of Top 500 Supercomputers, over 50% of them are based on clusters and many of them are deployed in industries.

More Related