brrt to the future

Hello there, I thought it high time to write to you again and update you on the world of JITs. Since last I wrote, PyPy 4.0 was released. Also in python-land, Pyston 0.4 was released, and finally Guile 2.1.1 was released and Andy Wingo wrote a nice piece about that, as is his custom. I present these links not only to give these projects the attention they deserve, but also because I think they are relevant to our own project. In chronological order, the release of PyPy 4.0 marked the first 'production' release of that projects' autovectorizer, which was developed over the course of this years Google Summer of Code. I'd like to take this opportunity to publicly congratulate the PyPy team on this achievement. So called 'vector' or SIMD operations perform a computation on multiple values in a single step and are an essential component of high-performance numerical computations. Autovectorizing refers to the compiler capability to automatically use such operat...

It's wrap-up time! This will be my final post in the GSoC 2014 program, because the GSoC 2014 progam ends 19:00 UTC today, so that's early enough that I won't be writing another post today. It's hard enough to write a post every week as it is. So what I'd like to do today is wrap-up what we've reached and what I've learned in the past three months. Let's start with a summary of what has been reached. MoarVM has an optional JIT compiler based on spesh , which is the optimization framework I've mentioned often by now. The JIT compiled code cannot handle all instruction yet, but it can handle loops, invocations, perl6 container ops, some regular expressions, expressions and deoptimisation. Reported benefits are anywhere between nothing and 50% faster code, which is awesome. I assume very few people have a lot of truly computation-intensive code running on rakudo perl6 yet, but I suspect that is where it would help most. The code-generator that we cu...

This week I made progress on the central 'tiling' algorithm used for code generation. I think it makes an interesting story and theory, so I will try to explain it in this post. Unfortunately, I don't think I can explain it very well without resorting to computer-science jargon, but I hope you can forgive me. What is tiling? Tiling refers to the process of breaking an input tree into many pieces. The tree refers to the data structures representing the code that the JIT should compile. As I wrote a few weeks back, these trees are ultimately generated from templates, mapped to the MoarVM opcodes. Let's suppose we have a tree for the expression: result = LOCAL[8] + 12 and that it'd look like the code below: (add (load (addr (local) 8) int_sz) (const 8 int_sz)) I realize most people probably are not familiar with the syntax used here. These are called s-expressions, and there is fortunately little to know: '(' opens a...