Bioinformatics

On the Cython mailing list, I saw this mentioned for avoiding init overhead, so i wrote up some code to try it. Basically, instead of using an __init__, it uses the PY_NEW macro (which I don't pretend to understand fully). I ran a benchmark with 5 cases: PY_NEW macro (still has python overhead for each call to the creator function) regular python init python init using __slots__ cython init (cdef'ed class) batch PY_NEW: calling PY_NEW from inside cython to avoid python call overhead batch init on cython class the timings look like this: PY_NEW on Cython class: 1.160 __init__ on Python class: 30.414 __init__ on Python class with slots: 10.242 __init__ on Cython class 1.185 batch PY_NEW total: 0.855 , interval only: 0.383 batch __init__ on Cython class total 0.998 , interval_only: 0.540 So, the PY_NEW is .383 compared to .540 for using a __init__ on a Cython class, but both are much faster than python. I was surprised that using slots gives a 3x speed improvement over a regular

I'm partly writing this because I think such valuable info is too hard to find, I'd read a lot that if you want php, you have to use mpm-prefork but it's not true! I've been using slicehost for dev server for almost a year now. Since my 1 year deal is up, I decided to switch to their affiliate mosso . I really like slicehost, but Mosso "cloud servers" seem a good fit for a server that goes through spurts of development and use followed by weeks of non-use. So now, I can keep it as a 256MB instance at about $10/month and update to a larger instance when doing real dev. I built it today as a 1024MB instance -- installed all my usual stuff, and updated my build script for ubuntu. That's here . The machine I'm on is extremely fast, normally I set GDAL building and leave, but it finished before I had a chance. After all was built, I resized it to a 256MB server -- that took 12minutes, but my instance was accessible for at least 10 of those. After that, I