Coming from a slightly different angle this time, I found that researchers were often isolating themselves to less (strictly fewer!) resources on HPC systems by not investigating what the node feature mixture looked like. As such this talk was created to help direct potentially abstract development efforts towards optimizing for which feature sets are most available on a HPC cluster. Below is my abstract for the talk as well as the recording: ...
Eventually I got to the point in data analytics where keeping things in lists, or list of lists was no longer quite cutting it. My processing was slowly starting to grind to a halt, and things were getting way too abstract. I decide to call up a friend who had worked in the business longer than me and they suggested “pandas”. I was vaguely familiar as users/clients had used it in the past. A “DataFrame” did sound like it would take care of a lot of my problems after reading the documentation casually… ...
Back when I first got hired at SHARCNET, I used a lot of Python. I mean a lot. What this meant is that I quickly became the lightning rod for all Python related questions (and commentary). During a fun Friday chat, a colleague remarked that Python was on average 40x slower than C++. I defended my current language of choice saying it was better than that, surely. To make a long story short, I was wrong. It really is about 40x slower depending on the problem. Determined to prove myself capable, and my language of choice a bit more defensible, I decided to look into ways to make Python faster. ...