Parallel Programming

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Parallel Programming

• Henri Bal
• (bal_at_cs.vu.nl)
• Vrije Universiteit
• Faculty of Sciences
• Amsterdam

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Overview

• What is parallel programming?
• Why do we need parallel programming?
• Organization of this course
• Practicum Parallel Programming

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ICWall

• A large display suitable for
• Education (classroom)
• Modern scientific applications thatneed to
  visualize large data-sets
• 3D applications

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ICWall an "off-the-shelf" video wall

• High-end graphics is very expensive
• Use tiled display with many projectors
• Use standard (inexpensive) components
• 9 PCs, graphic and network cards, 16 projectors
• Similar idea as cluster computing, but applied
  to graphics
• Current ICWall is strongly simplified version
• 1 PC with 2 projectors

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Parallel Programming

• Sequential programming
• Single thread of control
• Parallel programming
• Multiple threads of control
• Why parallel programming?
• Eases programming? Not really.
• Performance? Yes!

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Famous quote

• Parallel programming may do something to revive
  the pioneering spirit in programming,which
  seems to be degenerating intoa rather dull and
  routine occupation
• S. Gill, Computer Journal, 1958

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Why do we need parallel processing?

• Many applications need much faster machines
• Sequential machines are reaching their speed
  limits
• Moores Law (1975) Circuit complexity doubles
  every 18 months
• Using many transistors effectively to increase
  performance becomes critical problem
• Memory becomes a bottleneck
• DRAM access times improve only 10 per year
• Caches more and more important

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Parallel processing

• Use multiple processors to solve large problems
  fast
• Also gives more cache memory and aggregate memory
  bandwidth
• Microprocessors are getting cheaper and cheaper
• Cheap multiprocessors and multicore CPUsbring
  parallel processing to the desktop!

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History

• 1950s first ideas (see Gills quote)
• 1967 first parallel computer (ILLIAC IV)
• 1970s programming methods, experimental machines
• 1980s parallel languages (SR, Linda, Orca),
  commercial supercomputers
• 1990s software standardization (MPI), clusters,
  large-scale machines (Blue Gene)
• 2000s grid computing combining resources
  world-wide (Globus)
• Now much research on multi-cores, Graphical
  Processor Units (GPUs), IBM Cell, .

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Large-scale parallel machines

• Many parallel machines exist
• See http//www.top500.org
• Current 1 RoadRunner Cell clusterwith 122400
  cores
• 2 is IBM BlueGene/L with 212992 cores

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Challenging Applications

• Modeling ozone layer, climate, ocean
• Quantum chemistry
• Protein folding
• General computational science
• Aircraft modeling
• Handling use volumes of data from scientific
  instruments
• Lofar (astronomy)
• LHC (CERN, high-energy physics)
• Computer chess
• Analyzing multimedia content
• Generating movies

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Application areas

• Engineering and design
• Scientific applications
• Commercial applications (e.g., transactions,
  databases)
• Embedded systems (e.g., cars)
• This class focuses on scientific applications

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About this Course

• Goal Study how to write programs that run in
  parallel on a large number of machines.
• Focus on programming methods, languages,
  applications
• Focus on distributed-memory (message passing)
  machines, not (yet) on multi-cores, special
  purpose (GPU, Cell) machines
• Prerequisites
• Some knowledge about sequential languages
• Little knowledge about networking and operating
  systems

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Aspects of Parallel Computing

• Algorithms and applications
• Programming methods, languages, and environments
• Parallel machines and architectures

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Course Outline

• Introduction in algorithms and applications
• Parallel machines and architectures
• Overview of parallel machines, trends in
  top-500
• Cluster computers, BlueGene
• Programming methods, languages, and environments
• Message passing (SR, MPI, Java)
• Higher-level language HPF
• Applications
• N-body problems, search algorithms,
  bioinformatics
• Grid computing Multimedia content analysis
  on Grids (guest lecture Frank Seinstra)

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Course Information

• Examination
• Written exam based on
• - Reader available electronically from
  Blackboard
• - Lectures
• More information (and slides)
• http//www.cs.vu.nl/bal/college08.html

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Practicum Parallel Programming

• Separate practicum (6 ECTS)
• Implement ASP SOR algorithms in C/MPI
• Implement Capture Go search algorithm in
  Java/RMI
• Test and measure the programs on our DAS cluster

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More information

• Register by email to ppp_at_cs.vu.nl (Niels Drost)
  now
• Starts after this course (mid October)
• See http//www.cs.vu.nl/pp-cursus/ for a
  complete description