A FUNCTIONAL APPROACH TO PARALLELISM AND DISTRIBUTED PROCESSING IN UNDERGRADUATE EDUCATION

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A FUNCTIONAL APPROACH TO PARALLELISM AND
DISTRIBUTED PROCESSING IN UNDERGRADUATE EDUCATION

• Dr. Maury Eggen
• Trinity University
• San Antonio, Texas USA

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Outline

• Motivation for the development of MPSCM
• Background
• Methodology
• Educational Ramifications
• Implementation
• Examples
• Higher Level Functions
• Performance
• Summary, Conclusions, Future Research

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Motivation

• The future is parallel (David Patterson)
• Complete change needed in the development of
  computer technology and program coding
• Need to develop new coding techniques
• Need to develop new programming languages MPSCM
• Beginning students need fundamentals
• Parallelism must be infused into our entire
  curriculum

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Background

• MPSCM study begun two years ago
• MzScheme dialect chosen
• fully R5RS-compliant
• TCP/IP
• Threading
• Synchronization

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Methodology

• MPI (Message Passing Interface) is the defacto
  standard for message passing
• MPSCM has many MPI like constructs
• MPSCM related to a subset of MPI
• MPSCM fits our expectations of what a message
  passing environment should be
• Focus on preserving the functional nature of
  Scheme

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Education

• Scheme commonly used in educational environments
• Clear syntax and semantics
• Few constructs, easy to learn, expressive

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Education

• MPSCM provides a technology based educational
  environment for teaching parallelism which
  focuses particularly on creating an interface
  that gives the student and educator access to
  fundamental parallel programming concepts while
  preserving the ease of use of Scheme

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Implementation

• MPSCM built in three layers
• Communication layer
• Evaluation layer
• Top level functions

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Examples-Simple Scheme

• (define fact (lambda (n) (if ( n
  0) 1 ( n (fact (- n 1))))))

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• worker snippet(let ((received-val (MPSCM-recv
  0 0))) (MPSCM-send 0 (f received-val) 1))
• master snippet(begin (MPSCM-send 1 value
  0) (MPSCM-recv 1 1))
• SIMD function(define mpi-fun (lambda
  (value) (if ( MPSCM-myrank 0) (begin
  (MPSCM-send 1 value 0) (MPSCM-recv
  1 1)) (let ((received-val (MPSCM-recv 0
  0))) (MPSCM-send 0 (f received-val)
  1)))))

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Higher Level Examples

• (define lst (list 1 2 3 4 5 6 7 8 9))
• map
• (map fact lst)
• (1 2 6 24 120 720 5040 40320 362880)
• (MPSCM-map fact lst)

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Performance

• The primary goal of MPSCM is not to provide a
  competitive alternative to quicker approaches to
  parallel programming such as C/MPI for high
  performance computing.
• Its goal is, however, to provide a highly
  expressive parallel programming environment in
  which students new to the concepts of parallelism
  can learn and flourish, and it is highly
  successful at achieving this goal.

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Summary

• Nice easy to use environment for introduction to
  parallelism and distributed processing
• Many interesting and useful characteristics
• Good starting point for further investigations
• Decomposition process well presented in this
  approach
• The authors believe the approach will pay
  educational dividends for our students

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Future Research

• Considerable parallel processing can be done with
  just six functions, initialize, finalize, size,
  rank, send and receive.
• MPI has over 100 functions and more are being
  added daily
• MPSCM has fewer than 100
• More and varied higher level functions must be
  added

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Co-Authors

• Maury Eggen, Trinity University, San Antonio,
  Texas USA
• meggen_at_cs.trinity.edu
• Dr. Roger Eggen, University of North Florida,
  Jacksonville, Florida USA
• ree_at_unf.edu
• Alexander Starche, Trinity University, San
  Antonio, Texas USA
• astarche_at_trinity.edu