A collection of material that accumulated over time, mostly during my high school and undergraduate studies can be found under Archived Material.
Additionally, there are a couple of projects hosted here, which are not directly integrated in this page:
We know that Ruby and especially Rails applications can be very dynamic and pretty large. Though, many of the optimizations interpreters and even just-in-time compilers use have been invented in the 1980s and 1990s before Ruby and Rails even existed. So, I was wondering: do these optimizations still have a chance of coping with the millions of lines of Ruby code that large Rails apps from Shopify, Stripe, or GitLab have?
Oct 10, 2022: The Cost of Safety in Java
Overhead of Null Checks, Array Bounds, and Class Cast Exceptions in GraalVM Native Image
Oct 5, 2022: Effortless Language Servers
Ever since my blog post in 2016, I have wanted a good language server for SOM and Newspeak. Though, I didn’t really have the time to implement more than a few features.
Oct 17, 2021: Actors! And now?
An Implementer’s Perspective on High-level Concurrency Models, Debugging Tools, and the Future of Automatic Bug Mitigation
Sep 30, 2021: How do we do Benchmarking?
Impressions from Conversations with the Community
Here at Kent, we have a large group of researchers working on Programming Languages and Systems (PLAS), and within this group, we have a small team focusing on research on interpreters, compilation, and tooling to make programming easier.
Jun 16, 2021: Interpreter Generators: A Brief Look at Existing Work
Motivated by Tiger, a tool for generating interpreters, being mentioned on Twitter, I had a brief look at vmgen, Tiger, eJSTK, Truffle DSL, and DynSem. What follows are my rather rough notes and pointers. So, this is by no means a careful literature study, and I welcome further pointers.
One of the hard problems in language implementation research is benchmarking. Some people argue, we should benchmark only applications that actually matter to people. Though, this has various issues. Often, such applications are embedded in larger systems, and it’s hard to isolate the relevant parts. In many cases, these applications can also not be made available to other researchers. And, of course, things change over time, which means maintaining projects like DaCapo, Renaissance, or Jet Stream is a huge effort.