I’ve been making a point of reading academic papers on the train as I ride home. It’s so easy to get behind with the sheer quantity of work that is being produced. Anyway, it occurred to me that I ought to try and summarize the papers I read on this blog so that I can I remember my reactions to them.

I’ll start with “Permission Regions for Race-Free Parallelism”, by Westbrook, Zhao, Budimilic, and Sarkar. The basic idea builds off of Habanero Java, which is a kind of fork of the X10 language that Sarkar and his group work on. The basic idea of the paper is to add a language construct permit which looks like:

permit read(x1,...,xn) write(y1,...,yn) {
    /* this code may read fields of x1...xn and write
       fields of y1...yn */

For example, imagine a method pop() that removes an item from the front of a linked list:

Node pop() {
    Node tmp = this.next;
    if (tmp != null)
        this.next = tmp.next;
    return tmp;

This would be annotated like so:

Node pop() {
    permit write(this) {
        Node tmp = this.next;
        if (tmp != null)
            permit read(tmp)
                next = tmp.next;
        return tmp;

A dynamic monitoring system will then guarantee check for races at the granularity of the permit blocks. An effect system also allows a method to be called under the stipulation that reads/writes are permitted of its parameters. Permission regions are not required for final fields, naturally.

Finally, they support a view construct for arrays which allows you to declare permission to access a portion of an array:

region r = ...;
int[.] subA = A.subView(r);
permit write(subA) { ... }

This is reminiscent of my own divide() method in PJs.

Interestingly, they allow the local variables within a permit section to be modified. Presumably each such assignment will lead to a new dynamic conflict check.

To reduce the annotation burden, the compiler will automatically insert permission regions. Basically they find the highest point in the AST that includes all accesses within a given method, but they do not cross async or isolated (the HJ keywords for spawning tasks and for creating transactions). They find this is usually right.

The whole point of this exercise is to reduce the overhead and (I believe) improve the accuracy of dynamic checks. Naturally, the slowdown for monitoring for data races varied dramatically, but it was generally around 1.5 to 2x. There are some exceptions, such as raytracer, which went as high as 22x.

My reaction:

Summary: interested but mildly skeptical.

Their performance numbers seem pretty decent for dynamic monitoring, but I’m not sure it meets their goal of “always on”. HJ’s target audience after all is scientific computing, and 2x slowdown in that field seems like a big deal to me. Still, a lot of people are using R and Python etc so maybe it is “fast enough”. And of course they can optimize further, I’m sure.

The actual semantics of their race check are sort of interesting. A narrow focus on data-races over other kinds of races can lead to programs that do the wrong thing even though they never have any races. The classic example is Java code like this:

void addIfEmpty(/*shared*/ Vector v, Object o) {
    if (v.isEmpty()) v.add(o);

These two statements were presumably intended to be atomic, but of course they may not execute atomically. Nonetheless, since Vector in Java is a fully synchronized class, there will be no data races under the technical definition of data races.

In any case, declaring permission regions seems to suggest a sensitivity to this issue, however the use of compiler inference kind of works against this intutition, since the compiler may not know the proper places to insert the checks. (Here, for example, if fully automated the compiler would still insert the permission regions within the vector calls themselves)

But I think, in the end, races vs data races is besides the point. That is, the permission regions are not intended as a kind of “declaration of things that go together” but rather as a practical means of reducing overhead and controlling the granularity of checks for detecting data races. Basically—I gather that they assume the system will be always on and, generally, ignored by programmers. But if they come up against an issue where performance is a problem, they will start using the effect system and explicit permission regions to push these checks to a higher-level.

I’m not sure how effective this will be, however. A lot of the overhead seems to derive from the array view checks, which cannot be automated. Furthermore, when the number of items to be accessed is unbounded, such as when walking a linked list, you cannot push the permission regions out any bigger. It would be nice to see numbers that compare the overhead before they tweaked it and after to get an idea of how much reduction is possible.

It brings to mind my own efforts with PJs etc: I am excited to see that bear fruit, because I think the overhead of such dynamic checks can be made extremely low. Of course my system would not be nearly as flexible as theirs, which is why the checks are so cheap. But basically I like the idea of dynamic checking for races, but I think it is not necessary something you want to just layer on top of a rather broken “everything shared and mutable all the time” system, because the overheads are just too high. Rather, you start with a sane foundation, and you should be able to monitor for violations relatively cheaply and locally.