In my last Chalk post, I talked about an experimental, SLG-based solver that I wrote for Chalk. That particular design was based very closely on the excellent paper “Efficient top-down computation of queries under the well-founded semantics”, by W. Chen, T. Swift, and D. Warren. It followed a traditional Prolog execution model: this has a lot of strengths, but it probably wasn’t really suitable for use in rustc. The single biggest reason for this was that it didn’t really know when to stop: given a query like exists<T> { T: Sized }, it would happily try to enumerate all sized types in the system.

For the last month or so, I’ve gotten kind of obsessed with exploring a new evaluation model for Chalk. Specifically, I’ve been looking at adapting the SLG algorithm, which is used in the XSB Prolog engine. I recently opened a PR that adds this SLG-based solver as an alternative, and this blog post is an effort to describe how that PR works, and explore some of the advantages and disadvantages I see in this approach relative to the current solver that I described in my previous post.

For my next post discussing chalk, I want to take kind of a different turn. I want to talk about the general struct of chalk queries and how chalk handles them right now. (If you’ve never heard of chalk, it’s sort of “reference implementation” for Rust’s trait system, as well as an attempt to describe Rust’s trait system in terms of its logical underpinnings; see this post for an introduction to the big idea.

In my previous post, I talked over the basics of how unification works and showed how that “mathematical version” winds up being expressed in chalk. I want to go a bit further now and extend that base system to cover associated types. These turn out to be a pretty non-trival extension. What is an associated type? If you’re not a Rust programmer, you may not be familiar with the term “associated type” (although many langages have equivalents).

So in my first post on chalk, I mentioned that unification and normalization of associated types were interesting topics. I’m going to write a two-part blog post series covering that. This first part begins with an overview of how ordinary type unification works during compilation. The next post will add in associated types and we can see what kinds of mischief they bring with them. What is unification? Let’s start with a brief overview of what unification is.

Over the last year or two (man, it’s scary how time flies), I’ve been doing quite a lot of thinking about Rust’s trait system. I’ve been looking for a way to correct a number of flaws and shortcomings in the current implementation, not the least of which is that it’s performance is not that great. But also, I’ve been wanting to get a relatively clear, normative definition of how the trait system works, so that we can better judge possible extensions.

One of the things that is sometimes frustrating in Rust is the inability to define a type that indicates some subset of enum variants. For example, it is very common to have a pattern like this: match an_expr { expr_call(*) => process_call(an_expr) ... } fn process_call(a_call_expr: @ast::expr) { ... } But as you can see, the type of a_call_expr does not reflect the fact that this expression is a call. This is frustrating.

I’ve been slowly learning how type inference works in SpiderMonkey. As I understand it, SpiderMonkey’s type inference scheme is the brain child of one Brian “Hack-it”, coder extraordinaire. You may have seen a recent PLDI publication on the topic. You may, like me, have read that publication. You may, also like me, have walked away thinking, “um, I don’t really understand how that works.” In that case, dear reader, this blog post is for you.

I had a very interesting discussion with Sriram and Terrence (of Kilim and ANTLR fame, respectively—two smart dudes) yesterday. One of the things we talked about was adapting shared-memory data structures like concurrent hash maps into an actor setting. One thing we’ve found when working on Servo is that the temptation to cheat is enormous. Most of the papers you read about things like parallel layout just assume a shared memory setting and blithely make use of data strutures like concurrent hash maps.

Last Thursday and Friday I had the good fortune of presenting a paper of mine at HotPar 2012. The paper is called Parallel Closures: A New Twist on an Old Idea; it basically describes what has evolved to become the PJs (Parallel JavaScript) model, though it does so in the context of a static checker built in Java. I really enjoyed the workshop: the presenters were generally very good and the audience was lively.

I am dissatisfied with how mutability is treated in the Rust type system. The current system is that a type is not prefixed mutable; rather, lvalues are. That is, a type T is defined like so: T = [M T] | @ M T | & M T | { (M f : T)* } | int | uint | ... M = mut | const | (imm) Note that there is no type mut int (a mutable integer).

For a long time, it was considered fairly obvious, I think, that syntax didn’t really matter. It was just the surface skin over the underlying ideas. In recent times, though, the prevailing wisdom has reversed, and it is now quite common to hear people talk about how “syntax matters”. While I don’t exactly disagree, I think that the importance of trivial syntactic matters is generally overemphasized. It is not a matter of life and death whether or not semicolons are required to end a line, for example, or whether parentheses are required in making a call.

pcwalton and I (but mostly pcwalton) have been hard at work implementing regions in Rust. We are hoping to use regions to avoid a lot of memory allocation overhead in the compiler—the idea is to use memory pools (a.k.a. arenas) so that we can cheaply allocate the data needed to process a given function and then release it all in one shot. It is well known that arenas are great fit for the memory allocation patterns of a compiler, which tend to produce a lot of data that lives for the duration of a pass but is not needed afterwards.

One commonly requested feature for regions is the ability to return references to the inside of structures. I did not allow that in the proposal in my previous post because I did not want to have any region annotations beyond a simple &. I think, however, that if you want to allow returning references to the interior of a parameter, you need a way for the user to denote region names explicitly.

I was talking to brson today about the possibility of moving Rust to a regions system. He pointed out that the complexity costs may be high. I was trying to make a slimmer version where explicit region names were never required. This is what I came up with. The truth is, it’s not that different from the original: adding back region names wouldn’t change much. But I’m posting it anyway because it includes a description of how to handle regions in types and I think it’s the most complete and correct proposal at the moment.

Brian pointed out to me a nice solution to the Task API problem that I have overlooked, though it’s fairly obvious. Basically, I had rejected a “builder” style API for tasks because there is often a need for the child task to be able to send some data back to its parent after it has been spawned, and a builder API cannot easily accommodate this. Brian’s idea was to encapsulate these using futures.

It’s been a while since I wrote anything on the blog! A lot has been going on in the meantime, both in Rust, parallel JavaScript, and personally…I hate to write a big update post but I gotta’ catch up somehow! Rust First, we made our 0.1 release, which is great. We are now planning for 0.2. The goal is to make frequent, relatively regular releases. We’re still in such an early phase that it doesn’t seem to make sense to literally release every few months, but at the same time we don’t plan to wait long.

In one of the comments on yesterday’s post, Tushar Pokle asked why I would champion my model over an Erlang model of strict data separation. There are several answers to this question. The simplest answer is that Web Workers already provide an actors model, though they do not make tasks particularly cheap (it’s possible to work around this by creating a fixed number of workers and sending tasks for them to execute).

Lately the ideas for a parallel, shared memory JavaScript have begun to take shape. I’ve been discussing with various JavaScript luminaries and it seems like a design is starting to emerge. This post serves as a documentation of the basic ideas; I’m sure the details will change as we go along. User Model The model is that a JavaScript worker (the “parent”) may spawn a number of child tasks (the “children”).

The original Rust design included iterators very similar to Python’s generators. As I understand it, these were stripped out in favor of Ruby-esque blocks, partially because nobody could agree on the best way to implement iterators. I like blocks, but it seems like it’s more natural to compose iterators, so I wanted to think a bit about how one might use blocks to achieve similar things. I’m sure this is nothing new; there must be hundreds of libraries in Haskell that do the same things I’m talking about here.

In the context of thinking about parallelism for Rust, I have been reminded of an older idea I had for a lightweight, predictable dynamic race detection monitoring system based around block-scoped parallelism. I should think this would be suitable for (an extended version of) a dynamic language like Python, JavaScript, or Lua. I will write in a Python-like syntax since I know it best, but I am debating about exploring this for JavaScript.

I’ve been thinking a lot about “parallel blocks” recently and I am beginning to think they can be made to work very simply. The main thing that is needed is a type qualifier const that means “read-only”. This would be a type prefix with very low precedence, just like immutable and shared in D. The type const T would refer to an instance of T that cannot be modified. This is a deep property, so, given some record types defined like:

Marijn asked me what it is that I dislike about parameter modes. I thought I might as well explain here. For background, today in Rust a function can declare each parameter in one of several modes: By value (++): No pointer is used but the value is not owned by the callee. Therefore, the callee does not need to free it, for example, or decrement a ref count. By immutable reference (&&): a pointer to the variable in the caller’s stack frame is passed, but the callee cannot use it to make changes.

One of the better features from functional programming languages are variant types (a.k.a. algebraic data types). Basically they are a way of enumerating a small set of possibilities and then making sure that you handle every possible case. However, in real world use variant types tend to run into a few annoying problems. While working on the Harmonic compiler, I found that Scala’s case classes addressed some of these shortcomings.