Sunday, October 20, 2013

A fast, massively scalable, distributed and parallel solver for Poisson's equation

This semester I have been studying at Queensland in Australia, more specificly at Queensland University of Technology in Brisbane. Even though most of a semester abroad is about what happens outside school this post is focused on school.
I have taken a course in Parallel Computation at QUT. As a semester project I created a direct distributed memory solver for Poisson's equation. The solver is written in plain C taking use of technologies as OpenMP, BLAS and OpenMPI.
Compared to a naively written single core solver written in C my solution had a speedup of 200 on a 50 core cluster. Compared to a bleedingly optimized sequential implementation taking use of the Intel MKL library for hand tuned matrix multiply code a speedup of 20 was measured. The distributed nature of the solver can also cope with much bigger problems than the sequential implementation. One run was done over 64 nodes each using 8 cores, that is 512 cores. The problem was a finite difference discretization with 32000x32000 grid points. Calculating the solution took only 2 minutes. A sequential implementation would use take several days to finish. All the code is as usual available in my GitHub together with the report.

Sunday, April 21, 2013

Film job at ISFIT!

I had the pleasure of taking shots for the closing ceremony movie at ISFIT 2013. The shot's where taken by a GoPro Hero3 strapped to my quad. Movie below, my 5 seconds of fame starts at 6:00

ISFiT 2013 from ISFiT on Vimeo.

Thursday, January 31, 2013

FDM heat equation of isolated rod with dynamic end temparatures

To test my understanding of the FDM method I made a simple implementation of the Crank-Nickelson method applied on the heat equation. The physical intepretation is this: You have a perfectly insulated rod of length $l$. At time $t_0$ you know the temperature distribution in the rod, $f(x)$. You also know that each end of the rod will have a temperature that is a function of time, $g_0(t)$ and $g_1(t)$. Given this information, what will the temperature in the rod be at an arbitraray time $t$ and position $x$? This is what the heat equation tells you, the problem, as usual with partial differential equations is that you can't always solve them explicitly. This is where numerical techniques comes to play and saves the day. Below is the result of a numerical simulation of such a senario. For simplicity the rods length is $1$. \[ f(x) = (2+2 \sin(6 \pi x)) (1-|2(x-1)|), 0\leq x \leq1 \] \[ g_0(x) = g_1(x) = \sin(t), 0 \leq t \] This gives rise to the following solution:
Here is the Octave code that calculates this plot, quite simple and self explanetory:
M = 401; % number of space nodes
h = 1/(M+1); % space step size

T = 40000;  % number of timesteps
t = 0.0000005; % time step size

%U0 = 1-abs(linspace(-1,1,M)); % initial data
U0 = (2+2*sin(linspace(0,6*pi,M))).*(1-abs(linspace(-1,1,M))); % initial data
G1 = sin(linspace(0,2*pi,T));
GM = G1;

r = t/h^2;

U = zeros(M,T);
U(:,1) = U0;

n = M;
e = ones(n,1);
A = spdiags([e, -2*e, e], -1:1, n, n);

lkern = eye(M) + (r/2)*A;
rkern = eye(M) - (r/2)*A;

%for y=1:100
  U(:,1) = U0;
  for i=1:T-1
    U(:,i+1) = rkern\(lkern*U(:,i));
    U(1,i+1) = G1(i+1);
    U(M,i+1) = GM(i+1);

downscale = 1000;

Y = zeros(M,T/downscale);
for i=1:T/downscale;
  Y(:,i) = U(:,downscale*i);

tx = linspace(0,1,M);
tt = linspace(0,t*T,T/downscale);
hold off