scientific computing topics for final projects dr. guy tel-zur version 2, 15-05-2011
TRANSCRIPT
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Scientific ComputingTopics for Final Projects
Dr. Guy Tel-Zur
Version 2, 15-05-2011
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Best option
• Find a computational challenge in your field of research (Math, CS, Biology, Chemistry, Physics…)
• Think Parallel or Distributed• Use advanced Visualization
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In the next slides are topics which can candidates for the Final Projects
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Class 1: Science topic + a computational tools
• Examples:– Map-Reduce Paradigm,
http://hadoop.apache.org/core/
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Class 2: Study new computational tools + case studies / benchmarks
• In class 2 there is less emphasize on the scientific story
• Examples:– CFD, learn OpenFoam,
http://www.opencfd.co.uk/openfoam/ such projects also include learning how to install the tool
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Class 3: Porting a scientific problem to another new software
• Examples:– Program the “Game of Life” in Erlang, UPC,
Chapel, Fortress– Port the “Game of Life” to GPGPU– “Game of Life” in Microsoft’s Axum,
http://msdn.microsoft.com/en-us/devlabs/dd795202.aspx
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More topics
• Develop distributed code for Grid Mathematica or Maple
• Run your project on Amazon’s EC2 Cloud• Find a CPU intensive problem like parameter
sweep or Monte Carlo and solve it using Condor
• Do your project in “R”• Do your project using MatlabMPI / pMatlab
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Cont’
• Performance tools: TAU (Tuning and Analysis Utilities), http://www.cs.uoregon.edu/research/tau/home.php
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• 2D Ising Model Simulation• DLA – Difussion Limited Aggregation• Parallel Sorting algorithms• Game: the sesmic duck in OpenMP:
http://home.comcast.net/~arch.robison/seismic_duck.html
• Open|SpeedShop, http://www.openspeedshop.org/wp/– A strong CS background is needed
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The NAS Parallel Benchmark
http://www.nas.nasa.gov/Software/NPB/
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Parallel Numerical Libraries: Scalapack
1. Download packages.2. Write an example program.3. Make benchmarks (speedup & efficiency)• Ref: ScaLAPACK: a portable linear algebra for distributed memory
computers – design issues and performance. J.Choi et al. Computer Physics Communications 97 (1196) 1-15
• http://oscinfo.osc.edu/training/parlib/parlib.ls.pdf
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Parallel Genetic Algorithms
A genetic algorithm (GA) is a search procedure that optimizes some objective function f by maintaining a population P of candidate solutions and employing operations inspired by genetics (called crossover and mutation) to generate a new population from the previous one. Generally, the candidate solutions are encoded as bit strings.
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Simulated Annealing (SA)
Metropolis AlgorithmExample: TSP - Traveling Salesman Problem
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גמר – 12פרוייקט
Fractal Dimension Calculation Using the “Box Counting” Method
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Neural Networks
• Parallel (MPI/OpenMP) or Distributed (Condor)
• Search for a Pattern/Optimization
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Clustering
• Parallel (MPI/OpenMP) or Distributed (Condor)
• Classification of Data using Fuzzy Logic
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DLA
The Diffusion-Limited Aggregation (DLA) is a growth model based on diffusing particles. The growth is started with a single seedA random walker travels about a square lattice; when the walker reaches a site adjacent to the growing cluster it sticks
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N-Body Problem
Using the Barnes-Hut Algorithm
An O(n log n) algorithm based on a hierarchical octree representation of space in three dimensions. It computes interactions between distant particles by means of the first order approximation.
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Multi-Grids
Solving the Discrete Poisson Equation using Multigrid
Divide-and-Conquer Method
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Ising Model
Spins interactions
The Monte Carlo code should be parallel in the sense that each processor will perform work on a separate region of the lattice.
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Root - Proof
• http://people.web.psi.ch/feichtinger/doc/proof_examples.html