This is the third installment in my summary of the sessions I attended at JavaOne this year.

 

The previous installments covered Java futures and Java programming practice. This one covers concurrency.

Once again, the titles here are not always the ones that appear in the conference programme. I tried to undo the mangling that the Trademark Police typically impose on talk titles, so let me just state up front that Java, JMX, and JVM are trademarks of Sun Microsystems, Inc., and any other abbreviation beginning with J has a sporting chance of being one too.

Here are the talks summarized in this installment:

Let's Resync: What's New for Concurrency in Java SE
Experiences with Debugging Data Races
Transactional Memory in Java Systems
 

In the remaining installments, I'll cover JMX and miscellaneous other stuff.


TS-5515, Let's Resync: What's New for Concurrency in Java SE, Brian Goetz. It's well known by now that processors are not getting much faster, but they are getting much more parallel, so applications need to be parallel to exploit them. Brian suggests that the existing tools in java.util.concurrent are fine when you're dealing with a small number of CPU cores. But what about the future, when there may be hundreds or even thousands? "All the cores you can eat."

The answer might be a massive migration to functional programming languages, but assuming people stick with Java, they will need the Fork/Join framework being designed for Java 7. The basic idea is to facilitate a divide-and-conquer approach, where you divide your problem into subproblems, and those into subsubproblems, and so on until you have enough work for your cores. A nifty technique calledwork stealing makes this fill up available cores without requiring work-queue bottlenecks.

A class called ParallelArray will allow the expression of parallel tasks without having to code an explicit divide-and-conquer algorithm. The idea is that you can write something like this...

double highestGpa = students.withFilter(graduatesThisYear)
                            .withMapping(selectGpa)
                            .max();

...where graduatesThisYear is a predicate object and selectGpa is a transforming object. (This is one of the main use cases cited for closures, by the way.) The calls to withFilter andwithMapping just return new objects, but the call tomax triggers the whole computation of filtering and mapping in parallel. Very nice!

TS-6237, Experiences with Debugging Data Races, Siva Annamalai, Cliff Click. I was already familiar with most of the material in this talk, but I thought the most important message was this. You might be debugging your data races with println, or maybe with a hand-crafted ring buffer because println perturbs the timing and makes the race go away. You might be thinking that there must surely be a better way. But one of the world's leading experts on concurrency confirms that, although tools can help in some cases, in general you do still need to be able to do the println stuff.

TS-6316, Transactional Memory in Java Systems, Vyacheslav Shakin; Suresh Srinivas. The idea of Transactional Memory is that your programming language allows you to write things like this...

atomic {
    return map.get(k);
}

...and the system gives you the familiar ACID properties (well, maybe not Durability) in the face of other concurrent accesses. This can be implemented entirely in software or with hardware assistance. Hardware systems build on logic already needed for cache coherence. There are "weak" atomic systems where transactions are only ACID relative to other transactions, and "strong" ones where they are ACID relative to all memory accesses. The slides are a good summary of the domain and I'd recommend them to anyone who wants to get an idea of what it's about. Unfortunately they're not yet present on the developers.sun.com site but I'll update this entry when they are. (They'll probably be at this address.)


In the next installment I'll cover the sessions on JMX technology.

[Tags: javaone javaone2008 concurrencyfork/join transactional memory data races.]