Compute clusters are shared machines so they often contain very outdated versions of software in the name of backward compatibility. While the environment modules project goes a long way of alleviating this problem, I often encounter problems with the Python installation on clusters that I'm working on. In

Developing numerical programs in C, C++ or Fortran or even just fast C extensions like in my previous post, the chance of introducing a bug increases as complexity increases. One of my favorite types of bugs are caused when working with multi-dimensional arrays. While multi-dimensional arrays are relatively painless in

I absolutely love Python (and NumPy!) for the flexibility it gives me in quickly building up complex data structures and programs to arrive at interesting solutions. Still, sometimes handling larger amounts of data is too slow even after profiling my code and subsequent performance tuning. In those cases, going more