You're working for a startup company Ginormous Data Inc. (GDI) that specializes in finding patterns in large amounts of data. For example, a big retailer might give GDI all the web visits and purchases made and all the credit reports they've inspected and records of all the phone calls to them, and GDI will then find patterns in the data that suggest which toys will be hot this Christmas season. The programs that GDI writes are mostly written in Java. They aren't perfect; they're just heuristics, and they're operating on incomplete and sometimes-inaccurate information. They do need to be fast, though, as your clients are trying to find patterns faster than their competition can, and are willing to put up with a few errors even if the results aren't perfect, so long as they get good-enough results quickly.
GDI regularly uses multithreading to speed up its applications, and many of GDI's programs operate on shared-memory representations of the state of a simulation. These states are updated safely, using Java's synchronized keyword, and this is known to be a bottleneck in the code. Your boss asks you what will happen if you remove the synchronized keyword. You reply, "It'll break the simulations." She responds, "So what? If it's just a small amount of breakage, that might be good enough. Or maybe you can substitute some other synchronization strategy that's less heavyweight, and that'll be good enough." She suggests that you look into this by measuring how often GDI's programs are likely to break if they switch to inadequate-but-faster synchronization methods.
In some sense this assignment is the reverse of what software engineers traditionally do with multithreaded applications. Traditionally, they are worried about race conditions and insert enough synchronization so that the races become impossible. Here, though, you're deliberately trying to add races to the code in order to speed it up, and want to measure whether (and ideally, how badly) things will break if you do.
Java synchronization is based on the Java memory model (JMM), which defines how an application can safely avoid data races when accessing shared memory. The JMM lets Java implementations optimize accesses by allowing more behaviors than the intuitive semantics where there is a global clock and actions by threads interleave in a schedule that assumes sequential consistency. On modern hardware, these intuitive semantics are often incorrect: for example, intraprocessor cache communication might be faster than memory, which means that a cached read can return a new value on one processor before an uncached read returns an old value on another. To allow this kind of optimization, first, the JMM says that two accesses to the same location conflict if they come from different threads, at least one is a write, and the location is not declared to be volatile; and second, the JMM says that behavior is well-defined to be data-race free (DRF) unless two conflicting accesses occur without synchronization in between.
The details for proving that a program is DRF can be tricky, as is optimizing a Java implementation with data-race freedom in mind. Not only have serious memory-synchronization bugs been found in Java implementations, occasionally bugs have been found in the JMM itself, and sometimes people have even announced bugs only to find out later that they weren't bugs after all. For more details about this, please see: Lochbihler A. Making the Java memory model safe [PDF]. ACM TOPLAS 2013 Dec;35(4):12. doi:10.1145/2518191. You needn't read all this paper, just the first eight pages or so—through the end of §1.1.3.
It's easy to write programs that violate the JMM's rules. To model this, you will use a simple prototype that manages a data structure that represents an array of nonnegative integers. A state transition, called a swap, consists of subtracting 1 from one of the positive integers in the array, and adding 1 to an integer in that array. The sum of all the integers should therefore remain constant; if it varies, that indicates that one or more transitions weren't done correctly. Also, the values in the array should always be nonnegative. The converse is not true: if the sum remains constant and the values remain nonnegative it's still possible that some state transitions were done incorrectly. Still, these tests are reasonable ways to check for errors in the simulation.
For an example of a simulation, see jmm.jar, a JAR file containing the simplified source code of a simulation. It contains the following interfaces and classes:
Build and use a JMM-violating performance and reliability testing program, along the lines described below.
You may want to look at java.util.concurrent, java.util.concurrent.atomic and java.util.concurrent.locks for implementation ideas.
Submit a JAR file pmmplus.jar containing your solution. It should contain a copy of the files in pmm.jar, possibly with modifications (though you should attempt to minimize these modifications). It should also contain the source code to your new models, and a PDF file report.pdf containing your explanations, discussions, and performance and reliability results. Please limit your source-code lines to 80 characters or less, and limit the report to at most two pages. See Resources for written reports and oral presentations for more information about what we're looking for in your report.