Scientific ComputingTopics for Final Projects

Dr. Guy Tel-Zur

Version 2, 15-05-2011

Best option

• Find a computational challenge in your field of research (Math, CS, Biology, Chemistry, Physics…)

• Think Parallel or Distributed• Use advanced Visualization

In the next slides are topics which can candidates for the Final Projects

Class 1: Science topic + a computational tools

• Examples:– Map-Reduce Paradigm,

http://hadoop.apache.org/core/

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

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

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

Cont’

• Performance tools: TAU (Tuning and Analysis Utilities), http://www.cs.uoregon.edu/research/tau/home.php

• 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

The NAS Parallel Benchmark

http://www.nas.nasa.gov/Software/NPB/

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

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.

Simulated Annealing (SA)

Metropolis AlgorithmExample: TSP - Traveling Salesman Problem

גמר – 12פרוייקטFractal Dimension Calculation Using the “Box Counting” Method

Neural Networks

• Parallel (MPI/OpenMP) or Distributed (Condor)

• Search for a Pattern/Optimization

Clustering

• Parallel (MPI/OpenMP) or Distributed (Condor)

• Classification of Data using Fuzzy Logic

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

N-Body ProblemUsing 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.

Multi-Grids

Solving the Discrete Poisson Equation using Multigrid

Divide-and-Conquer Method

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.

Root - Proof

• http://people.web.psi.ch/feichtinger/doc/proof_examples.html