NSLM (nslm) wrote,
NSLM
nslm

[update] walking and project

The past four days have been really nice. I've got very little work done, but did transfer the money around that I needed to. Much of Thursday, Friday and Saturday was spent walking around Bath and the surrounding area with zaniyah. During further procrastination efforts a map showing quite how much of Bath we've walked around in the last 2 weeks or so has been produced ( http://students.bath.ac.uk/ee0mdc/bath-walks.png ), it's a somewhat more sizable chunk than I think either of us realised! We're also contemplating an expedition to Frome to aquire MEAD, although this one will probably be via train rather than walking the 20 odd miles each way.


When ever I try to look at my project at the minute I feel like I'm striking my head against a brick wall. I managed to find one Math paper suggesting that the Conjugate Gradient method can be parallelised onto distribuded memory systems, but when I compared the data they were feeding it, to the data I'm using I don't think it's relevant, my data is just too damned sparse. Which means that the time I spend sending any results around totally outweighs the time it takes to just do the extra calculations. I think that my code might possibly be of some use. But only if you're using a REDICULOUSLY large data set. My one chance so far to work on a less sparse system was with a 3D model rather than the 2D ones I've been using. Unfortunatly the matrix I was given is not suitable for the CG method. Argh! And to top it all off my project supervisors not in Bath at the minute. Greh.

The only thing I can think of left to do on this project is possibly to try and use a dual CPU machine and share the memory, to see how well things would scale that way... (Any one have a multi-CPU machine I could aquire some CPU time on?) I suppose that another option ma be to create some "fake" test data to feed into the system to see quite where the usefulness of my project might lie, but I have no idea how to go about generating sparse symetric matricies.
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