A new blog engine

This week, I’ve updated my blog engine and I’m using WordPress now.

Why ? Because it is a real blog engine, there is a huge community that writes a lot of useful plugins (syntax highlighters, sitemaps, …) but mainly, it allows me to specify multiple categories when I’m posting, and this was needed for some blog aggregators (O’ Reilly, planet.scipy.org, …).

The theme is almost the same as the last one, everything is not yet available (like everything that was on my blogroll), but it will be back soon 🙂

Grid computing for Python

In my lab, we frequently process huge amounts of data, each process can take hours or days. The problem is that we don’t have a usable tool to do this.

Our legacy software is in C and we plan on moving to Python in the next weeks. We could use some commercial software, but it is not optimal.

This is where P2P comes into the game. We have a lot of unused computers or dual cores that are not used even at 50% because we are not trained in parallel computing (and we won’t in the near future). By “we”, I mainly mean PhD students. Our background is signal or image processing, not Computer Science and even less parallel computing. Those unused computers could be used for our computations, but this implies that the computer is only used if nobody works on it, that we only use what is available at a precise moment, and that some computers may get used during the computations. That’s why P2P seems an elegant idea, as a grid computing tool.

P2P computation is not new in the lab (we developed P2P-MPI in Java for instance), but for our team, it is. For the time being, I did not find much about the tools that we could use, but the JXTA protocol seems a good start. I hope I will be able to talk more about this subject in the near future.

Some news about the manifold learning scikit

I got the word today that my paper was accepted, so I can now focus on delivering the code.

I’m in the process of refactoring it so that it depends less on some of our libraries here. In two weeks, there is a nipy sprint in Paris I will attend, and machine learning is one of the topic we will discuss, so this may indicate where and how I’ll contribute the code I will keep going on showing some results next week.

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