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Title: Advanced Hardware Parallel/Distributed Processing High Performance Computing Top 500 list Grid computing


1
Advanced Hardware Parallel/Distributed
Processing High Performance ComputingTop 500
listGrid computing
CMPE 478, Parallel Processing
picture of ASCI WHITE, the most powerful
computer in the world (2001)
2
Von Neumann Architecture
CPU
RAM
Device
Device
BUS
  • sequential computer

3
History of Computer Architecture
  • 4 Generations (identified by logic technology)
  • Tubes
  • Transistors
  • Integrated Circuits
  • VLSI (very large scale integration)

4
PERFORMANCE TRENDS
5
PERFORMANCE TRENDS
  • Traditional mainframe/supercomputer performance
    25 increase per year
  • But microprocessor performance 50 increase per
    year since mid 80s.

6
Moores Law
  • Transistor density doubles every 18 months
  • Moore is co-founder of Intel.
  • 60 increase per year
  • Exponential growth
  • PC costs decline.
  • PCs are building bricks of all future systems.

7
VLSI Generation
8
Bit Level Parallelism(upto mid 80s)
  • 4 bit microprocessors replaced by 8 bit, 16 bit,
    32 bit etc.
  • doubling the width of the datapath reduces the
    number of cycles required to perform a full
    32-bit operation
  • mid 80s reap benefits of this kind of
    parallelism (full 32-bit word operations combined
    with the use of caches)

9
Instruction Level Parallelism(mid 80s to mid
90s)
  • Basic steps in instruction processing
    (instruction decode, integer arithmetic, address
    calculations, could be performed in a single
    cycle)
  • Pipelined instruction processing
  • Reduced instruction set (RISC)
  • Superscalar execution
  • Branch prediction

10
Thread/Process Level Parallelism(mid 90s to
present)
  • On average control transfers occur roughly once
    in five instructions, so exploiting instruction
    level parallelism at a larger scale is not
    possible
  • Use multiple independent threads or processes
  • Concurrently running threads, processes

11
Sequential vs Parallel Processing
  • physical limits reached
  • easy to program
  • expensive supercomputers
  • raw power unlimited
  • more memory, multiple cache
  • made up of COTS, so cheap
  • difficult to program

12
Amdahls Law
  • The serial percentage of a program is fixed. So
    speed-up obtained by employing parallel
    processing is bounded.
  • Lead to pessimism in in the parallel processing
    community and prevented development of parallel
    machines for a long time.

1
Speedup
1-s
s
P
  • In the limit
  • Spedup 1/s

s
13
Gustafsons Law
  • Serial percentage is dependent on the number of
    processors/input.
  • Broke/disproved Amdahls law.
  • Demonstrated achieving more than 1000 fold
    speedup using 1024 processors.
  • Justified parallel processing

14
Hillis Thesis 85
Piece of silicon
Sequential computer
Parallel computer
  • proposed The Connection Machine with massive
    number of processors each with small memory
    operating in SIMD mode.
  • CM-1, CM-2 machines from Thinking Machines
    Corporation (TMC)were examples of this
    architecture with 32K-128K processors.
    Unfortunately, TMC went out of business.

15
Grand Challenge Applications
  • Important scientific engineering problems
    identified by U.S. High Performance Computing
    Communications Program (92)

16
Flynns Taxonomy
  • classifies computer architectures according to
  • Number of instruction streams it can process at a
    time
  • Number of data elements on which it can operate
    simultaneously

Data Streams
Single Multiple
Single
SIMD
SISD
Instruction Streams
Multiple
MISD
MIMD
17
SPMD Model (Single Program Multiple Data)
  • Each processor executes the same program
    asynchronously
  • Synchronization takes place only when processors
    need to exchange data
  • SPMD is extension of SIMD (relax synchronized
    instruction execution)
  • SPMD is restriction of MIMD (use only one
    source/object)

18
Parallel Processing Terminology
  • Embarassingly Parallel
  • applications which are trivial to parallelize
  • large amounts of independent computation
  • Little communication
  • Data Parallelism
  • model of parallel computing in which a single
    operation can be applied to all data elements
    simultaneously
  • amenable to SIMD or SPMD style of computation
  • Control Parallelism
  • many different operations may be executed
    concurrently
  • require MIMD/SPMD style of computation

19
Parallel Processing Terminology
  • Scalability
  • If the size of problem is increased, number of
    processors that can be effectively used can be
    increased (i.e. there is no limit on
    parallelism).
  • Cost of scalable algorithm grows slowly as input
    size and the number of processors are increased.
  • Data parallel algorithms are more scalable than
    control parallel algorithms
  • Granularity
  • fine grain machines employ massive number of
    weak processors each with small memory
  • coarse grain machines smaller number of powerful
    processors each with large amounts of memory

20
Shared Memory Machines
  • Memory is globally shared, therefore processes
    (threads) see single address
  • space
  • Coordination of accesses to locations done by use
    of locks provided by
  • thread libraries
  • Example Machines Sequent, Alliant, SUN Ultra,
    Dual/Quad Board Pentium PC
  • Example Thread Libraries POSIX threads, Linux
    threads.

21
Shared Memory Machines
  • can be classified as
  • UMA uniform memory access
  • NUMA nonuniform memory access
  • based on the amount of time a processor takes to
    access local and global memory.

P M P M .. P M
Inter- connection network
P M P M .. P M
Inter- connection network
M M M .. M
P P .. P
M M .. M
Inter- connection network/ or BUS
(a)
(c)
(b)
22
Distributed Memory Machines
  • Each processor has its own local memory (not
    directly accessible by others)
  • Processors communicate by passing messages to
    each other
  • Example Machines IBM SP2, Intel Paragon, COWs
    (cluster of workstations)
  • Example Message Passing Libraries PVM, MPI

23
Beowulf Clusters
  • Use COTS, ordinary PCs and networking equipment
  • Has the best price/performance ratio

PC cluster
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The Future Most Powerful Computer ? (now
operational and 1)
47
TOP 500 LIST June 2002
48
Grid Computing
  • provide access to computing power and various
    resources just like accessing electrical power
    from electrical grid
  • Allows coupling of geographically distributed
    resources
  • Provide inexpensive access to resources
    irrespective of their physical location or access
    point
  • Internet dedicated networks can be used to
    interconnect distributed computational resources
    and present them as a single unified resource
  • Resources supercomputers, clusters, storage
    systems, data resources, special devices

49
Grid Computing
  • the GRID is, in effect, a set of software tools,
    which when combined with hardware, would let
    users tap processing power off the Internet as
    easily as the electrical power can be drawn from
    the electricty grid.
  • Examples of Grid projects
  • - Seti_at_home search for extraterrestial
    intelligence
  • - Entropia company to broker processing power
    of idle computers, about 30,000 volunteer
    computers and total processing power 1 Tflop.
  • Xpulsar_at_home sifts astronomical data for
    pulsars
  • - Folding_at_home protein folding
  • - Evolutionary_at_home population dynamics

50
Seti_at_home Project
  • Screen-saver program
  • Sifts through signals recorded by the giant
    Arecibo radio telescope in Puerto Rico
  • 3 million people downloaded screen saver and run
    it.
  • Program periodically prompts its host to
    retrieve a new chunk of data from the Internet
    and sends latest processed results back to SETI.
  • Equivalent of more than 600,000 years of PC
    processing time has already clocked up.

51
More Grid Projects
  • GriPhyN grid developed by consortium of American
    labs for physics projects
  • Earth System Grid make huge climate simulations
    spanning hundreds of years.
  • Earthquake Engineering Simulation Grid
  • Particle Physics Data Grid
  • Information Power Grid supported by NASA for
    massive engineering calculations
  • DataGrid European, coordinated by CERN. Aim is
    to develop middleware for research projects in
    biological sciences, earth observation and high
    energy physics.

52
Gordon Bell Jim Gray on Whats next in High
Performance Computing
  • Beowulf s economics and sociology are poised to
    kill off the other architectural lines
  • Computational Grid can federate systems into
    supercomputers far beyond the power of any
    current computing center
  • The centers will become super-data and
    super-application centers
  • Clusters (currently) perform poorly on
    applications that require large shared memory

53
Gordon Bell Jim Gray on Whats next in High
Performance Computing
  • Now individuals and laboratories can assemble and
    incrementally grow any-size super-computer
    anywhere in the world.
  • By 2010, the cluster is likely to be the
    principal computing structure.
  • Seti_at_home does not run Linpack, so does not
    qualify in the top500 list. But Seti_at_home
    avarages 13 Tflops making it more powerful than
    the top 3 of top500 machines combined.
  • GRID and P2P computing using the Internet is
    likely to remain the worlds most powerful
    supercomputer.

54
Gordon Bell Jim Gray on Whats next in High
Performance Computing
  • Concerned that traditional supercomputer
    architecture is dead and a supercomputer
    mono-culture is being born.
  • Recommend increased investment in peta-scale
    distributed databases.
  • By 2010, the cluster is likely to be the
    principal computing structure.
  • Research programs that stimulate cluster
    understanding and training are a good investment
    for laboratories that depend on highest
    performance machines.
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