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Grids From Computing to Data Management

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Title: Grids From Computing to Data Management


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 --gt Use faster hardware
  • Work smarter --gt Optimize algorithms
  • Get help --gt 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. (-gtMPP)
  • 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
CPU
CPU
Memory
Memory
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
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) Axb
  • 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!)
  • n1 59.7 GFlops
  • n500 0.4 Gflops
  • Sum 1.17 TFlops
  • in 2004 (yesterday?)
  • n1 70 TFlops (x1118)
  • n500 850 Gflops (x2125)
  • Sum 11274 Tflops and 408629 processors

21
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22
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23
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)

24
The performance ? Ideally grows linearly
  • Speed-up
  • if TS is the best time to treat a problem in
    sequential, its time should be TPTS/P with P
    processors !
  • Speedup TS/TP
  • limited (Amdhal law) any program has a
    sequential and a parallel part TSFT//, thus
    the speedup is limited S (FT//)/(FT///P)lt1/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 nS with nP
    processors

25
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

26
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

27
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

28
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

29
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 !

30
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 !

31
Other motivation the theoric problems !
  • query optimization
  • execution strategy
  • load balancing

32
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.

33
Shared memory archi. and databases
  • Pros
  • data transparently accessible
  • easier load balancing
  • fast access to data
  • Cons
  • scalability
  • availability
  • memory and IO contentions

34
Shared disks archi. and databases
  • Pros
  • no memory contentions
  • easy code migration from uni-processor server
  • Cons
  • cache consistence management
  • IO bottleneck

35
Share-nothing archi. and databases
  • Pros
  • cost
  • scalability
  • availability
  • Cons
  • data partitioning
  • communication management
  • load balancing
  • complexity of optimization process

36
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)

37
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

38
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/tut
    orials.html www.buyya.com/cluster
  • Grids www.lri.fr/fci/Hammamet/Cosnard-Hammamet-
    9-4-02.ppt
  • TOP 500 www.top500.org PVMwww.csm.ornl.gov/pvm
  • OpenMP www.openmp.org HPF www.crpc.rice.edu/H
    PFF
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