Case Studies

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Case Studies

• Class 4

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LAM/MPI

• LAM/MPI is a high-quality open-source
  implementation of the Message Passing Interface
  specification, including all of MPI-1.2 and much
  of MPI-2. Intended for production as well as
  research use, LAM/MPI includes a rich set of
  features for system administrators, parallel
  programmers, application users, and parallel
  computing researchers.

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LAM/MPI

• Cluster Friendly, Grid Capable
• From its beginnings, LAM/MPI was designed to
  operate on heterogeneous clusters. With support
  for Globus and Interoperable MPI, LAM/MPI can
  span clusters of clusters.
• Performance
• Several transport layers, including Myrinet, are
  supported by LAM/MPI. With TCP/IP, LAM imposes
  virtually no communication overhead, even at
  gigabit Ethernet speeds. New collective
  algorithms exploit hierarchical parallelism in
  SMP clusters.
• Empowering Developers
• The xmpi profiling tool and parallel debugger
  support (e.g., using TotalView or the Distributed
  Debugging Tool) enable in-depth application
  tuning and debugging.
• A Stable Extensible Platform for Research
• Enables developers to incorporate new
  functionality into LAM/MPIwithout having to
  understand its internal details. By writing to
  LAM/MPIs system services interface, researchers
  can readily add support for new transport layers,
  new collective algorithms, boot protocols,
  checkpoint/restart, and more.
• Tools and Third Party Applications
• Since LAM/MPI implements the specified MPI
  standard, most third-party parallel applications
  are developed based on MPI. In addition, a number
  of auxiliary tools are available for LAM/MPI.

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SPRNG

• The Scalable Parallel Random Number Generators
  Library
• SPRNG is a set of libraries for scalable and
  portable pseudorandom number generation, and has
  been developed keeping in mind the requirements
  of users involved in parallel Monte Carlo
  simulations
• Monte Carlo calculations consume a large fraction
  of all supercomputing cycles. The accuracy of
  these computations is critically influenced by
  the quality of the random number generators used.
  While the issue of random number generation in
  sequential calculations has been well studied,
  albeit on less powerful computers, there has been
  comparatively less work done in the context of
  parallel Monte Carlo applications. SPRNG seeks to
  fill this gap by implementing parallel random
  number generators that satisfy the requirements
  given below.

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SPRNG

• Quality
• Provide high quality pseudorandom numbers in a
  computationally inexpensive and scalable manner.
• Reproducibility
• Provide totally reproducible streams of parallel
  pseudorandom numbers, independent of the number
  of processors used in the computation and of the
  loading produced by sharing of the parallel
  computer.
• Locality
• Allow for the creation of unique pseudorandom
  number streams on a parallel machine with minimal
  interprocessor communication.
• Portability
• Should be portable between serial and parallel
  platforms and must be available on the most
  commonly used workstations and supercomputers.

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MPICH and SPRNG
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MPICH and SPRNG

• Estimation of Pi using Monte Carlo Method
• Consider a circle of radius r circumscribed by a
  square board. Darts are thrown to the board.
  The ratio of r number of darts (n) fall on the
  circle to that of all throwed (N) is
  approximately equal to the ratio of the area of
  the circle and the square. The more random darts
  are thrown, the more accurate is the
  approximation.
• n/N Pi/4. Therefore, Pi can be estimated.

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MPICH and SPRNG

• Estimation of Pi using Monte Carlo Method

2r
r
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MPICH and SPRNG

• Estimation of Pi using Monte Carlo Method
• Consider a quadrant in a unit square
• random coordinates (xi, yi) are generated with
  SPRNG and count the number of (xi,yi) that fall
  within the quadrant.
• The process can be parallelized and the counting
  workload can be distributed to multiple compute
  nodes.
• The partial counts can then add together to form
  n and the ratio n/N can be calculated.
• The method can be extended to find integrals of
  arbitrary function.

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MPICH and SPRNG

• Himalaya Option Pricing using Monte Carlo and
  Quasi Monte Carlo Simulation
• Like an Asian option, the Himalaya is a call on
  the average performance of the best stocks within
  the basket. Throughout the life of the option,
  there are particular measurement dates where the
  best performer within the basket is removed, and
  this process is continued until all the assets
  with the exception of 1 have been removed from
  the basket. The total return on this last stock
  is taken as the final measure. The payoff is the
  sum of all the measured returns over the life of
  the option.
• Implemented by HKBU Mathematics Department

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MPICH and SPRNG
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mpiJava
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mpiJava

• mpiJava is an object-oriented Java interface to
  the standard Message Passing Interface (MPI). The
  interface was developed as part of the HPJava
  project, but mpiJava itself does not assume any
  special extensions to the Java language - it
  should be portable to any platform that provides
  compatible Java-development and native MPI
  environments.

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mpiJava

• Nozzle
• This code simulates a 2-D inviscid flow through
  an axisymmetric nozzle. The simulation yields
  contour plots of all flow variables, including
  velocity components, pressure, mach number,
  density and entropy, and temperature. The plots
  show the location of any shock wave that would
  reside in the nozzle. Also, the code finds the
  steady state solution to the 2-D Euler equations

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mpiJava
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To be continued