benjamin-lee

>> so it’s usually not worth spending a ton of time optimizing single-threaded performance for a single experiment when I can just perform a big MapReduce. Is this the scientific version of "rich people problems"?

> Is this the scientific version of "rich people problems"?

Author here. Yes, most certainly. In fact, it was one of the things that drew me towards the NIH for my PhD. My overall point in the post was to show that a somewhat naive Python implementation and a much faster Nim version have a small Levenshtein distance. For many people in bioinformatics who don't have a background in software engineering (that would be a significant fraction, if not a majority of them), this could be a huge boon. Combined with the fact that most bioinformatics researchers don't have the privilege of the world's largest biomedical HPC cluster at their disposal, I still think Nim would be great drop-in replacement for quick single-threaded line-oriented string processing. For numerical stuff, probably not.

However, I am mostly writing in Rust these days for longer term projects that require threading and good ecosystem support. Perhaps I'll write a follow-up retrospective on Rust versus Nim in this area.

I’m a huge fan of the moonwatch both for its beauty and history with the space program. Ever since I was 10 I’ve wanted one. However, I’m a PhD student and there’s no way I can afford to get a “real” one. The Swatch release is really attractive to me since it captures the spirit of the watch while making it accessible.

For reference, there’s a very popular variant of the Speedmaster Professional that uses a sapphire crystal rather than a hesalite (basically plastic) crystal. Despite never having been used in space (since the shattering crystal is a risk) the sapphire model is still considered a “professional” edition and highly sought after. People who wear it enjoy the aesthetic of the spacefaring version with the earthly practicality of scratch-proof sapphire. The Swatch version’s desirability is just the same logic a taken a few more steps. It’s made by Omega, has the same basic design, and evokes the imagery of the space race.

Alternatively, consider the popularity of the Tesla toy car for kids. It’s not anything like the real Tesla in terms of functionality but is still a cool electric vehicle, especially if you already have a Tesla.

I followed this plan and lost over 100 lbs (45 kg) in about a year and have kept it off for the last five years. Even more impressively, I did it while eating the very same junk food that made me fat, albeit in different quantities. I tried every diet you could imagine before reading the book and failed every single time. As a programmer, the approach Walker uses just clicks with me. If you're overweight and have an engineering mindset, it's absolutely worth the read. It changed my life.

I still follow the plan to this day, albeit with more sophisticated logging. I use a combinatorial optimizer web app that tells me what to eat every day so that it completely takes the element of choice (and ability to screw up) out of the equation. I've developed it for the last five years and am hoping to release it as a product and/or open source eventually. If anyone's interested, shoot me an email (link on my website) and I'll share access.

Ah that makes sense. I agree with you; I’m not a huge fan of trying to infer where the types came from myself either when reading code on GitHub since it doesn’t have the inference that my IDE does.

You hit the nail on the head. This is just the lexicographically minimal string rotation with a canonicalization step. I have actually had this on my to-do list for a while. The truth is that the data set I'm applying this to is small (though I'm doing my best to change that by discovering new viroid-like agents) so the optimization has yet to become pressing. At some point, I'd like to spin this script out into a standalone tool but I'm not sure the demand is there yet.

Author here. This is spot on. The majority of the code I write is either piping data around to existing tools using shell scripting and Snakemake or writing the data processing code myself when there isn't a tool that does what I need. Usually, I'm working alone or with a few other computational biologists. Many of my scripts are one-off but they have the distinct tendency of growing in complexity and scope if they are useful. That's one of the big advantages with Nim in my mind: you can write a quick and dirty script and have it be pretty fast and then go back later and optimize it to a few percent of C without having to rewrite your code in another language. In this sense, it's quite like Julia (another really good language).