Coming from a slightly different angle this time, I found that researchers were often isolating themselves to 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 toward optimizing for the feature sets that are most available on an 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 lists 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 decided to call up a friend who had worked in the business longer than me, and they suggested “pandas.” I was vaguely familiar with it because users and 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. This meant 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. ...