Monthly Archives: January 2018

It has been a while since I have last looked at CPU benchmarks and performances. In particular now that am I getting a new server that can be used as a small cluster head node. But server prices range from £300 into the astronomical. From back in the days when I build my desktop computers from scratch I also remembered the amazing tools that let you compare various CPUs in terms of spec and performance, price etc, but literally haven’t looked at them in years.

So before delving through manually picking up CPU names and putting them into some CPU comparison site I thought, maybe i can just put a quick script together that will give me a scatter plot of  the CPU mark versus its current price. Easy enough 20 minutes later I have figured out how to use Beautifulsoup to parse some html text. To be precise the html of, the comparison of server CPUs on PassMark. The html contains the chart info from which i read the CPU mark and the current price where given. Then with some very rudimentary plotting I can look at cheap, yet high performing CPUs (Ok I was lazy, I could have made sure that the labels aren’t overlapping…but this is really just a quick and dirty plot):

The price range actually goes up quite a bit higher, but I have a spending limit and therefore decided to only plot up to $1500.

Then I soon realised the flaw in my great plan. Looking at some of the cheap yet high performing CPUs, it turns out they are all quite old and have an ‘end of life’ status. The table does not contain that info and I would have to go back to some manual comparison. I am not that desperate to learn that much more about CPUs nor the time to further crawl my way through some html files. However, it seems that the Xeon Silver 4114 CPU, that is commonly sold in customisable server setups isn’t such a bad choice as a midrange CPU, which will probably be a likely choice in a potential server purchase.

 

Baby steps with KNIME

I am trying to explore documenting some of the things I try at work a little better. Rather than just write things into a notebook and not find it ever again I thought I’d give making a small video a go. The story behind this little project, is that I am trying build workflows for biomolecular simulations. However, there are a great many ( or at least one ) workflow tools out there already, so I am trying to explore how easily adaptable such a workflow tool might be for the purpose of running a biomolecular simulation. Until about a month ago I had never heard about KNIME, until today I had absolutely no idea what it did. So I took my first baby steps, downloaded it (knime.com), and tried to run a first workflow. Running a simple k-means clustering on a typical benchmark clustering dataset (https://cs.joensuu.fi/sipu/datasets/), was really quite easy and everything was very intuitive and didn’t take much longer than half an hour to get the hang of things. I made a little video of how to do this:

The video certainly has a lot of room for improvement, but it also happens to be my first ever screen capture video I attempted to make, so bare with me as I learn to improve on these skills. (Yes, I am also aware that apparently I don’t know the difference between 250 and 2500, but redoing the video just because of that seemed not all that useful)

Next I’ll try and actually write my own KNIME node that will execute a simple python script, since being able to execute workflow elements mostly run in python is really what I am interested in.