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Grids From Computing to Data Management

Grids From Computing to Data Management. Jean-Marc Pierson Lionel Brunie INSA Lyon, dec 04. Outline. a very short introduction to Grids a brief introduction to parallelism a not so short introduction to Grids data management in Grids. Grid concepts : an analogy.

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Grids From Computing to Data Management

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  1. GridsFrom Computing to Data Management Jean-Marc Pierson Lionel Brunie INSA Lyon, dec 04

  2. Outline • a very short introduction to Grids • a brief introduction to parallelism • a not so short introduction to Grids • data management in Grids

  3. Grid concepts : an analogy Electric power distribution : the electric network and high voltage

  4. Grids concepts Computer power distribution : Internet network and high performance (parallelism and distribution)

  5. Parallelism : an introduction • Grids dates back only 1996 • Parallelism is older ! (first classification in 1972) • Motivations : • need more computing power (weather forecast, atomic simulation, genomics…) • need more storage capacity (petabytes and more) • in a word : improve performance ! 3 ways ... Work harder --> Use faster hardware Work smarter --> Optimize algorithms Get help --> Use more computers !

  6. Parallelism : the old classification • Flynn (1972) • Parallel architectures classified by the number of instructions (single/multiple) performed and the number of data (single/multiple) treated at same time • SISD : Single Instruction, Single Data • SIMD : Single Instruction, Multiple Data • MISD : Multiple Instructions, Single Data • MIMD : Multiple Instructions, Multiple Data

  7. SIMD architectures • in decline since 97 (disappeared from market place) • concept : same instruction performed on several CPU (as much as 16384) on different data • data are treated in parallel • subclass : vectorprocessors • act on arrays of similar data using specialized CPU; used in computer intensive physics

  8. MIMD architectures • different instructions are performed in parallel on different data • divide and conquer : many subtasks in parallel to shorten global execution time • large heterogeneity of systems

  9. Another taxonomy • based on how memories and processors interconnect • SMP : Symmetric Multiprocessors • MPP : Massively Parallel Processors • Constellations • Clusters • Distributed systems

  10. Symmetric Multi-Processors (1/2) • Small number of identical processors (2-64) • Share-everything architecture • single memory (shared memory architecture) • single I/O • single OS • equal access to resources HD Memory network CPU CPU CPU

  11. Symmetric Multi-Processors (2/2) • Pro : • easy to program : only one address space to exchange data (but programmer must take care of synchronization in memory access : critical section) • Cons : • poor scalability : when the number of processors increase, the cost to transfer data becomes too high; more CPUs = more access memory by the network = more need in memory bandwidth ! Direct transfer from proc. to proc. (->MPP) Different interconnection schema (full impossible !, growing in O(n2) when nb of procs increases by O(n)) : bus, crossbar, multistage crossbar, ...

  12. Massively Parallel Processors (1/2) • Several hundred nodes with a high speed interconnection network/switch • A share-nothing architecture • each node owns a memory (distributed memory), one or more processors, each runs an OS copy CPU Memory network Memory CPU Memory CPU

  13. Massively Parallel Processors (2/2) • Pros : • good scalability • Cons : • communication between nodes longer than in shared memory; improve interconnection schema : hypercube, (2D or 3D) torus, fat-tree, multistage crossbars • harder to program : • data and/or tasks have to be explicitly distributed to nodes • remote procedure calls (RPC, JavaRMI) • message passing between nodes (PVM, MPI), synchronous or asynchronous communications • DSM : Distributed Shared Memory : a virtual memory • upgrade : processors and/or communication ?

  14. CPU CPU CPU Memory Memory Memory network network network CPU CPU CPU CPU CPU CPU CPU CPU CPU Constellations • a small number of processors (up to 16) clustered in SMP nodes (fast connection) • SMPs are connected through a less costly network with “poorer” performance • With DSM, memory may be addressed globally : each CPU has a global memory view, memory and cache coherence is guaranteed (ccNuma) Periph. Interconnection network

  15. Clusters • a collection of workstations (PC for instance) interconnected through high speed network, acting as a MPP/DSM with network RAM and software RAID (redundant storage, // IO) • clusters = specialized version of NOW : Network Of Workstation • Pros : • low cost • standard components • take advantage of unused computing power

  16. Distributed systems • interconnection of independent computers • each node runs its own OS • each node might be any of SMPs, MPPs, constellations, clusters, individual computer … • the heart of the Grid ! • «A distributed system is a collection of independent computers that appear to the users of the system as a single computer » Distributed Operating System. A. Tanenbaum, Prentice Hall, 1994

  17. Where are we today (nov 22, 2004) ? • a source for efficient and up-to-date information : www.top500.org • the 500 best architectures ! • we head towards 100 Tflops • 1 Flops = 1 floating point operation per second • 1 TeraFlop = 1000 GigaFlops = 100 000 MegaFlops = 1 000 000 000 000 flops = one thousand billion operations per second

  18. Today's bests • comparison on a similar matrix maths test (Linpack) :Ax=b Rank Tflops Constructor Nb of procs 1 70.72 (USA) IBM BlueGene/L / DOE 32768 2 51.87 (USA) SGI Columbia / Nasa 10160 3 35.86 (Japon) NEC EarthSim 5120 4 20.53 (Espagne) IBM MareNostrum 3564 5 19.94 (USA) California Digital Corporation 4096 41 3.98 (France) HP Alpha Server / CEA 2560

  19. NEC earth simulator Single stage crossbar : 2700 km of cables - a MIMD with Distributed Memory 700 TB disk space 1.6 PB mass storage area : 4 tennis court, 3 floors

  20. How it grows ? • in 1993 (11 years ago!) • n°1 : 59.7 GFlops • n°500 : 0.4 Gflops • Sum = 1.17 TFlops • in 2004 (yesterday?) • n°1 : 70 TFlops (x1118) • n°500 : 850 Gflops (x2125) • Sum = 11274 Tflops and 408629 processors

  21. Problems of the parallelism • Two models of parallelism : • driven by data flow : how to distribute data ? • driven by control flow : how to distribute tasks ? • Scheduling : • which task to execute, on which data, when ? • how to insure highest compute time (overlap communication/computation?) ? • Communication • using shared memory ? • using explicit node to node communication ? • what about the network ? • Concurrent access • to memory (in shared memory systems) • to input/output (parallel Input/Output)

  22. The performance ? Ideally grows linearly • Speed-up : • if TS is the best time to treat a problem in sequential, its time should be TP=TS/P with P processors ! • Speedup = TS/TP • limited (Amdhal law): any program has a sequential and a parallel part : TS=F+T//, thus the speedup is limited : S = (F+T//)/(F+T///P)<1/F • Scale-up : • if TPS is the time to treat a problem of size S with P processors, then TPS should also be the time to treat a problem of size n*S with n*P processors

  23. Network performance analysis • scalability : can the network be extended ? • limited wire length, physical problems • fault tolerance : if one node is down ? • for instance in an hypercube • multiple access to media ? • inter-blocking ? • The metrics : • latency : time to connect • bandwidth : measured in MB/s

  24. Tools/environment for parallelism (1/2) • Communication between nodes : By global memory ! (if possible, plain or virtual) Otherwise : • low-level communication : sockets s = socket(AF_INET, SOCK_STREAM, 0 ); • mid-level communication library (PVM, MPI) info = pvm_initsend( PvmDataDefault ); info = pvm_pkint( array, 10, 1 ); info = pvm_send( tid, 3 ); • remote service/object call (RPC, RMI, CORBA) service runs on distant node, only its name and parameters (in, out) have to be known

  25. Tools/environment for parallelism (2/2) • Programming tools • threads : small processes • data parallel language (for DM archi.): • HPF (High Performance Fortran) say how data (arrays) are placed, the system will infer the best placement of computation (to minimize total computation time (e.g. further communications) • task parallel language (for SM archi.): • OpenMP : compiler directives and library routines; based on threads. The parallel program is close to sequential; it is a step by step transform • Parallel loop directives (PARALLEL DO) • Task parallel constructs (PARALLEL SECTIONS) • PRIVATE and SHARED data declarations

  26. Parallel databases : motivations • Necessity • Information systems increased in size ! • Transactional load increased in volume ! • Memory and IO bottleneck were worse and worse • Query in increased in complexity (multi sources, multi format txt-img-vid) • Price • the ratio "price over performance" is continuously decreasing (see clusters) • New applications • data mining (genomics, health data) • decision support (data warehouse) • management of complex hybrid data

  27. Target application example (1/2) Video servers • Huge volume of raw data • bandwidth : 150 Mb/s; 1h - 70 GB • bandwidth TVHD : 863 Mb/s; 1h = 362 GB • 1h MPEG2 : 2 GB • NEED of Parallel I/O • Highly structured, complex and voluminous metadata (descriptors) • NEED large memory !

  28. Target application example (2/2) Medical image databases • images produced by PACS (Picture Archiving Communication Systems) • 1 year of production : 8 TB of data • Heterogeneous data (multiple imaging devices) • Heterogeneous queries • Complex manipulations (multimodal image fusion, features extractions) • NEED CPUs, memory, IO demands !

  29. Other motivation : the theoric problems ! • query optimization • execution strategy • load balancing

  30. Intrinsic limitations • Startup time • Contentions : • concurrent accesses to shared resources • sources of contention : • architecture • data partitioning • communication management • execution plan • Load imbalance • response time = slowest process • NEED to balance data, IO, computations, comm.

  31. Shared memory archi. and databases • Pros : • data transparently accessible • easier load balancing • fast access to data • Cons : • scalability • availability • memory and IO contentions

  32. Shared disks archi. and databases • Pros : • no memory contentions • easy code migration from uni-processor server • Cons : • cache consistence management • IO bottleneck

  33. Share-nothing archi. and databases • Pros : • cost • scalability • availability • Cons : • data partitioning • communication management • load balancing • complexity of optimization process

  34. Communication : high performance networks (1/2) • High bandwidth : Gb/s • Low latency : µs • Thanks to : • point to point • switch based topology • custom VLSI (Myrinet) • network protocols (ATM) • kernel bypassing (VIA) • net technology (optical fiber)

  35. Communication : high performance networks (2/2) • Opportunity for parallel databases • WAN : connecting people to archives (VoD, CDN) • LAN : local network as a parallel machine NOW are used : • as virtual super-servers ex: one Oracle server + some read-only databases on idle workstations (hot sub-base) • as virtual parallel machines a different database on several machines (in hospital, one for citology, one for MRI, one for radiology, …) • SAN : on PoPC (Piles of PC), clusters • low cost parallel DBMS

  36. Bibliography / Webography G.C Fox, R.D William and P.C Messina "Parallel Computing Works !" Morgan Kaufmann publisher, 1994, ISBN 1-55860-253-4 M. Cosnard and D Trystram "Parallel Algorithms and Architectures" Thomson Learning publisher, 1994, ISBN 1-85032-125-6 M. Gengler, S. Ubéda and F. Desprez, "Initiation au parallélisme : concepts, architectures et algorithmes" Masson, 1995, ISBN 2-225-85014-3 Parallelism: www.ens-lyon.fr/~desprez/SCHEDULE/tutorials.html www.buyya.com/cluster Grids : www.lri.fr/~fci/Hammamet/Cosnard-Hammamet-9-4-02.ppt TOP 500 : www.top500.org PVM:www.csm.ornl.gov/pvm OpenMP : www.openmp.org HPF : www.crpc.rice.edu/HPFF

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