Programming Java threads in the real world, Part 1

A Java programmer's guide to threading architectures

All Java programs other than simple console-based applications are multithreaded, whether you like it or not. The problem is that the Abstract Windowing Toolkit (AWT) processes operating system (OS) events on its own thread, so your listener methods actually run on the AWT thread. These same listener methods typically access objects that are also accessed from the main thread. It may be tempting, at this point, to bury your head in the sand and pretend you don't have to worry about threading issues, but you can't usually get away with it. And, unfortunately, virtually none of the books on Java addresses threading issues in sufficient depth. (For a list of helpful books on the topic, see Resources.)

This article is the first in a series that will present real-world solutions to the problems of programming Java in a multithreaded environment. It's geared to Java programmers who understand the language-level stuff (the synchronized keyword and the various facilities of the Thread class), but want to learn how to use these language features effectively.

Platform dependence

Unfortunately, Java's promise of platform independence falls flat on its face in the threads arena. Though it's possible to write a platform-independent multithreaded Java program, you have to do it with your eyes open. This isn't really Java's fault; it's almost impossible to write a truly platform-independent threading system. (Doug Schmidt's ACE [Adaptive Communication Environment] framework is a good, though complex, attempt. See Resources for a link to his program.) So, before I can talk about hard-core Java-programming issues in subsequent installments, I have to discuss the difficulties introduced by the platforms on which the Java virtual machine (JVM) might run.

Atomic energy

The first OS-level concept that's important to understand is atomicity. An atomic operation cannot be interrupted by another thread. Java does define at least a few atomic operations. In particular, assignment to variables of any type except long or double is atomic. You don't have to worry about a thread preempting a method in the middle of the assignment. In practice, this means that you never have to synchronize a method that does nothing but return the value of (or assign a value to) a boolean or int instance variable. Similarly, a method that did a lot of computation using only local variables and arguments, and which assigned the results of that computation to an instance variable as the last thing it did, would not have to be synchronized. For example:

class some_class
    int some_field;
    void f( some_class arg ) // deliberately not synchronized
        // Do lots of stuff here that uses local variables
        // and method arguments, but does not access
        // any fields of the class (or call any methods
        // that access any fields of the class).
        // ...
        some_field = new_value;     // do this last.

On the other hand, when executing x=++y or x+=y, you could be preempted after the increment but before the assignment. To get atomicity in this situation, you'll need to use the keyword synchronized.

All this is important because the overhead of synchronization can be nontrivial, and can vary from OS to OS. The following program demonstrates the problem. Each loop repetitively calls a method that performs the same operations, but one of the methods (locking()) is synchronized and the other (not_locking()) isn't. Using the JDK "performance-pack" VM running under Windows NT 4, the program reports a 1.2-second difference in runtime between the two loops, or about 1.2 microseconds per call. This difference may not seem like much, but it represent a 7.25-percent increase in calling time. Of course, the percentage increase falls off as the method does more work, but a significant number of methods -- in my programs, at least -- are only a few lines of code.

import java.util.*;
class synch
   synchronized int locking     (int a, int b){return a + b;}
    int              not_locking (int a, int b){return a + b;}

    private static final int ITERATIONS = 1000000;
    static public void main(String[] args)
        synch tester = new synch();
        double start = new Date().getTime();
      for(long i = ITERATIONS; --i >= 0 ;)
        double end = new Date().getTime();
        double locking_time = end - start;
        start = new Date().getTime();
      for(long i = ITERATIONS; --i >= 0 ;)
        end = new Date().getTime();
        double not_locking_time = end - start;
        double time_in_synchronization = locking_time - not_locking_time;
        System.out.println( "Time lost to synchronization (millis.): "
                        + time_in_synchronization );
        System.out.println( "Locking overhead per call: "
                        + (time_in_synchronization / ITERATIONS) );
            not_locking_time/locking_time * 100.0 + "% increase" );

Though the HotSpot VM is supposed to address the synchronization-overhead problem, HotSpot isn't a freebee -- you have to buy it. Unless you license and ship HotSpot with your app, there's no telling what VM will be on the target platform, and of course you want as little as possible of the execution speed of your program to be dependent on the VM that's executing it. Even if deadlock problems (which I'll discuss in the next installment of this series) didn't exist, the notion that you should "synchronize everything" is just plain wrong-headed.

Concurrency versus parallelism

The next OS-related issue (and the main problem when it comes to writing platform-independent Java) has to do with the notions of concurrency and parallelism. Concurrent multithreading systems give the appearance of several tasks executing at once, but these tasks are actually split up into chunks that share the processor with chunks from other tasks. The following figure illustrates the issues. In parallel systems, two tasks are actually performed simultaneously. Parallelism requires a multiple-CPU system.

Unless you're spending a lot of time blocked, waiting for I/O operations to complete, a program that uses multiple concurrent threads will often run slower than an equivalent single-threaded program, although it will often be better organized than the equivalent single-thread version. A program that uses multiple threads running in parallel on multiple processors will run much faster.

Though Java permits threading to be implemented entirely in the VM, at least in theory, this approach would preclude any parallelism in your application. If no operating-system-level threads were used, the OS would look at the VM instance as a single-threaded application, which would most likely be scheduled to a single processor. The net result would be that no two Java threads running under the same VM instance would ever run in parallel, even if you had multiple CPUs and your VM was the only active process. Two instances of the VM running separate applications could run in parallel, of course, but I want to do better than that. To get parallelism, the VM must map Java threads through to OS threads; so, you can't afford to ignore the differences between the various threading models if platform independence is important.

Get your priorities straight

I'll demonstrate the ways the issues I just discussed can impact your programs by comparing two operating systems: Solaris and Windows NT.

Java, in theory at least, provides ten priority levels for threads. (If two or more threads are both waiting to run, the one with the highest priority level will execute.) In Solaris, which supports 231 priority levels, this is no problem (though Solaris priorities can be tricky to use -- more on this in a moment). NT, on the other hand, has seven priority levels available, and these have to be mapped into Java's ten. This mapping is undefined, so lots of possibilities present themselves. (For example, Java priority levels 1 and 2 might both map to NT priority level 1, and Java priority levels 8, 9, and 10 might all map to NT level 7.)

NT's paucity of priority levels is a problem if you want to use priority to control scheduling. Things are made even more complicated by the fact that priority levels aren't fixed. NT provides a mechanism called priority boosting, which you can turn off with a C system call, but not from Java. When priority boosting is enabled, NT boosts a thread's priority by an indeterminate amount for an indeterminate amount of time every time it executes certain I/O-related system calls. In practice, this means that a thread's priority level could be higher than you think because that thread happened to perform an I/O operation at an awkward time.

The point of the priority boosting is to prevent threads that are doing background processing from impacting the apparent responsiveness of UI-heavy tasks. Other operating systems have more-sophisticated algorithms that typically lower the priority of background processes. The downside of this scheme, particularly when implemented on a per-thread rather than a per-process level, is that it's very difficult to use priority to determine when a particular thread will run.

It gets worse.

In Solaris, as is the case in all Unix systems, processes have priority as well as threads. The threads of high-priority processes can't be interrupted by the threads of low-priority processes. Moreover, the priority level of a given process can be limited by a system administrator so that a user process won't interrupt critical OS processes. NT supports none of this. An NT process is just an address space. It has no priority per se, and is not scheduled. The system schedules threads; then, if a given thread is running under a process that isn't in memory, the process is swapped in. NT thread priorities fall into various "priority classes," that are distributed across a continuum of actual priorities. The system looks like this:

Windows NT's priority architecture

The columns are actual priority levels, only 22 of which must be shared by all applications. (The others are used by NT itself.) The rows are priority classes. The threads running in a process pegged at the idle priority class are running at levels 1 through 6 and 15, depending on their assigned logical priority level. The threads of a process pegged as normal priority class will run at levels 1, 6 through 10, or 15 if the process doesn't have the input focus. If it does have the input focus, the threads run at levels 1, 7 through 11, or 15. This means that a high-priority thread of an idle priority class process can preempt a low-priority thread of a normal priority class process, but only if that process is running in the background. Notice that a process running in the "high" priority class only has six priority levels available to it. The other classes have seven.

NT provides no way to limit the priority class of a process. Any thread on any process on the machine can take over control of the box at any time by boosting its own priority class; there is no defense against this.

The technical term I use to describe NT's priority is unholy mess. In practice, priority is virtually worthless under NT.

So what's a programmer to do? Between NT's limited number of priority levels and it's uncontrollable priority boosting, there's no absolutely safe way for a Java program to use priority levels for scheduling. One workable compromise is to restrict yourself to Thread.MAX_PRIORITY, Thread.MIN_PRIORITY, and Thread.NORM_PRIORITY when you call setPriority(). This restriction at least avoids the 10-levels-mapped-to-7-levels problem. I suppose you could use the system property to detect NT, and then call a native method to turn off priority boosting, but that won't work if your app is running under Internet Explorer unless you also use Sun's VM plug-in. (Microsoft's VM uses a nonstandard native-method implementation.) In any event, I hate to use native methods. I usually avoid the problem as much as possible by putting most threads at NORM_PRIORITY and using scheduling mechanisms other than priority. (I'll discuss some of these in future installments of this series.)


There are typically two threading models supported by operating systems: cooperative and preemptive.

The cooperative multithreading model

In a cooperative system, a thread retains control of its processor until it decides to give it up (which might be never). The various threads have to cooperate with each other or all but one of the threads will be "starved" (meaning, never given a chance to run). Scheduling in most cooperative systems is done strictly by priority level. When the current thread gives up control, the highest-priority waiting thread gets control. (An exception to this rule is Windows 3.x, which uses a cooperative model but doesn't have much of a scheduler. The window that has the focus gets control.)

The main advantage of cooperative multithreading is that it's very fast and has a very low overhead. For example, a context swap -- a transfer of control from one thread to another -- can be performed entirely by a user-mode subroutine library without entering the OS kernel. (In NT, which is something of a worst-case, entering the kernel wastes 600 machine cycles. A user-mode context swap in a cooperative system does little more than a C setjump/longjump call would do.) You can have thousands of threads in your applications significantly impacting performance. Since you don't lose control involuntarily in cooperative systems, you don't have to worry about synchronization either. That is, you never have to worry about an atomic operation being interrupted. The main disadvantage of the cooperative model is that it's very difficult to program cooperative systems. Lengthy operations have to be manually divided into smaller chunks, which often must interact in complex ways.

The preemptive multithreading model

The alternative to a cooperative model is a preemptive one, where some sort of timer is used by the operating system itself to cause a context swap. The interval between timer ticks is called a time slice. Preemptive systems are less efficient than cooperative ones because the thread management must be done by the operating-system kernel, but they're easier to program (with the exception of synchronization issues) and tend to be more reliable since starvation is less of a problem. The most important advantage to preemptive systems is parallelism. Since cooperative threads are scheduled by a user-level subroutine library, not by the OS, the best you can get with a cooperative model is concurrency. To get parallelism, the OS must do the scheduling. Of course, four threads running in parallel will run much faster than the same four threads running concurrently.

Some operating systems, like Windows 3.1, only support cooperative multithreading. Others, like NT, support only preemptive threading. (You can simulate cooperative threading in NT with a user-mode library like the "fiber" library, but fibers aren't fully integrated into the OS.) Solaris provides the best (or worst) of all worlds by supporting both cooperative and preemptive models in the same program.

Mapping kernel threads to user processes

The final OS issue has to do with the way in which kernel-level threads are mapped into user-mode processes. NT uses a one-to-one model, illustrated in the following picture.

NT user-mode threads effectively are kernel threads. They are mapped by the OS directly onto a processor and they are always preemptive. All thread manipulation and synchronization are done via kernel calls (with a 600-machine-cycle overhead for every call). This is a straightforward model, but is neither flexible nor efficient.

The Solaris model, pictured below, is more interesting. Solaris adds to the notion of a thread, the notion of a lightweight process (LWP). The LWP is a schedulable unit on which one or more threads can run. Parallel processing is done on the LWP level. Normally, LWPs reside in a pool, and they are assigned to particular processors as necessary. An LWP can be "bound" to a specific processor if it's doing something particularly time critical, however, thereby preventing other LWPs from using that processor.

Up at the user level, you have a system of cooperative, or "green," threads. In a simple situation, a process will have one LWP shared by all the green threads. The threads must yield control to each other voluntarily, but the single LWP the threads share can be preempted by an LWP in another process. This way the processes are preemptive with respect to each other (and can execute in parallel), but the threads within the process are cooperative (and execute concurrently).

A process isn't limited to a single LWP, however. The green threads can share a pool of LWPs in a single process. The green threads can be attached (or "bound") to an LWP in two ways:

  1. The programmer explicitly "binds" one or more threads to a specific LWP. In this case, the threads sharing a LWP must cooperate with each other, but they can preempt (or be preempted by) threads bound to a different LWP. If every green thread was bound to a single LWP, you'd have an NT-style preemptive system.

  2. The threads are bound to green threads by the user-mode scheduler. This is something of a worst case from a programming point of view because you can't assume a cooperative or a preemptive environment. You may have to yield to other threads if there's only one LWP in the pool, but you might also be preempted.

This threading model gives you an enormous amount of flexibility. You can choose between an extremely fast (but strictly concurrent) cooperative system, a slower (but parallel) preemptive system, or any combination of the two.

So why does this matter to a Java programmer? The main issue is that the choice of threading model is entirely up to the VM -- you have no control. For example, early versions of the Solaris VM were strictly cooperative. Java threads were all green threads sharing a single LWP. The current version of the Solaris VM, however, uses several LWPs. Similarly, the NT VMs don't have the equivalent of green threads, so they're always preemptive. In order to write platform-independent code, you must make two seemingly contradictory assumptions:

  1. You can be preempted by another thread at any time. You must use the synchronized keyword carefully to assure that non-atomic operations work correctly.

  2. You will never be preempted unless you give up control. You must occasionally perform some operation that will give control to other threads so they can have a chance to run. Use yield() and sleep() in appropriate places (or make blocking I/O calls). For example, you might want to consider calling yield() every one hundred iterations or so of a long loop, or voluntarily going to sleep for a few milliseconds every so often to give lower-priority threads a chance to run. (yield() will yield control only to threads running at your priority level or higher).

Wrapping it up

So, those are the main OS-level issues you must consider when you're writing a Java program. Since you can make no assumptions about your operating environment, you have to program for the worst case. For example, you have to assume you can be preempted at any time, so you must use synchronized appropriately, but you must also assume that you will never be preempted, so you must also use yield(), sleep(), or occasionally blocking I/O calls to permit other threads to run. You can't assume priority levels 1 and 2 are different. They might not be after NT has mapped Java's 10 levels into its 7 levels. You can't assume that a priority level 2 thread will always be higher priority than one that runs at level 1.

Subsequent articles will get into considerable detail about various thread-related programming problems and solutions. Here's the roadmap for the rest of the series:

  1. Deadlock, starvation, and nested-monitor lockout
  2. Roll-your-own mutexes and a deadlock-handling lock manager
  3. Counting semaphores, condition variables, and singletons
  4. Event notification in a multithreaded environment (the mysteries of the AWTEventMulticaster)
  5. Reader/writer locks
  6. Timers
  7. Synchronous-dispatching: multithreading without threads
  8. Implementing the active-object pattern
Allen Holub has been working in the computer industry since 1979. He is widely published in magazines (Dr. Dobb's Journal, Programmers Journal, Byte, and MSJ, among others). He has seven books to his credit, and is currently working on an eighth that will present the complete sources for a Java compiler written in Java. Allen abandoned C++ for Java in early 1996 and now looks at C++ as a bad dream, the memory of which is mercifully fading. He's been teaching programming (first C, then C++ and MFC, now OO-Design and Java) both on his own and for the University of California, Berkeley Extension, since 1982. Allen offers both public classes and in-house training in Java and object-oriented design topics. He also does object-oriented design consulting. Get information, and contact Allen, via his Web site

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