Follow Techotopia on Twitter

On-line Guides
All Guides
eBook Store
iOS / Android
Linux for Beginners
Office Productivity
Linux Installation
Linux Security
Linux Utilities
Linux Virtualization
Linux Kernel
System/Network Admin
Programming
Scripting Languages
Development Tools
Web Development
GUI Toolkits/Desktop
Databases
Mail Systems
openSolaris
Eclipse Documentation
Techotopia.com
Virtuatopia.com

How To Guides
Virtualization
General System Admin
Linux Security
Linux Filesystems
Web Servers
Graphics & Desktop
PC Hardware
Windows
Problem Solutions

  




 

 

Thinking in Java
Prev Contents / Index Next

How a garbage collector works

If you come from a programming language where allocating objects on the heap is expensive, you may naturally assume that Java’s scheme of allocating everything (except primitives) on the heap is also expensive. However, it turns out that the garbage collector can have a significant impact on increasing the speed of object creation. This might sound a bit odd at first—that storage release affects storage allocation—but it’s the way some JVMs work, and it means that allocating storage for heap objects in Java can be nearly as fast as creating storage on the stack in other languages.

For example, you can think of the C++ heap as a yard where each object stakes out its own piece of turf. This real estate can become abandoned sometime later and must be reused. In some JVMs, the Java heap is quite different; it’s more like a conveyor belt that moves forward every time you allocate a new object. This means that object storage allocation is remarkably rapid. The “heap pointer” is simply moved forward into virgin territory, so it’s effectively the same as C++’s stack allocation. (Of course, there’s a little extra overhead for bookkeeping, but it’s nothing like searching for storage.)

Now you might observe that the heap isn’t in fact a conveyor belt, and if you treat it that way, you’ll eventually start paging memory a lot (which is a big performance hit) and later run out. The trick is that the garbage collector steps in, and while it collects the garbage it compacts all the objects in the heap so that you’ve effectively moved the “heap pointer” closer to the beginning of the conveyor belt and farther away from a page fault. The garbage collector rearranges things and makes it possible for the high-speed, infinite-free-heap model to be used while allocating storage.

To understand how this works, you need to get a little better idea of the way different garbage collector (GC) schemes work. A simple but slow garbage collection technique is is called reference counting. This means that each object contains a reference counter, and every time a reference is attached to an object, the reference count is increased. Every time a reference goes out of scope or is set to null, the reference count is decreased. Thus, managing reference counts is a small but constant overhead that happens throughout the lifetime of your program. The garbage collector moves through the entire list of objects, and when it finds one with a reference count of zero it releases that storage. The one drawback is that if objects circularly refer to each other they can have nonzero reference counts while still being garbage. Locating such self-referential groups requires significant extra work for the garbage collector. Reference counting is commonly used to explain one kind of garbage collection, but it doesn’t seem to be used in any JVM implementations.

In faster schemes, garbage collection is not based on reference counting. Instead, it is based on the idea that any nondead object must ultimately be traceable back to a reference that lives either on the stack or in static storage. The chain might go through several layers of objects. Thus, if you start in the stack and the static storage area and walk through all the references, you’ll find all the live objects. For each reference that you find, you must trace into the object that it points to and then follow all the references in that object, tracing into the objects they point to, etc., until you’ve moved through the entire web that originated with the reference on the stack or in static storage. Each object that you move through must still be alive. Note that there is no problem with detached self-referential groups—these are simply not found, and are therefore automatically garbage.

In the approach described here, the JVM uses an adaptive garbage-collection scheme, and what it does with the live objects that it locates depends on the variant currently being used. One of these variants is stop-and-copy. This means that—for reasons that will become apparent—the program is first stopped (this is not a background collection scheme). Then, each live object that is found is copied from one heap to another, leaving behind all the garbage. In addition, as the objects are copied into the new heap, they are packed end-to-end, thus compacting the new heap (and allowing new storage to simply be reeled off the end as previously described).

Of course, when an object is moved from one place to another, all references that point at (i.e., that reference) the object must be changed. The reference that goes from the heap or the static storage area to the object can be changed right away, but there can be other references pointing to this object that will be encountered later during the “walk.” These are fixed up as they are found (you could imagine a table that maps old addresses to new ones).

There are two issues that make these so-called “copy collectors” inefficient. The first is the idea that you have two heaps and you slosh all the memory back and forth between these two separate heaps, maintaining twice as much memory as you actually need. Some JVMs deal with this by allocating the heap in chunks as needed and simply copying from one chunk to another.

The second issue is the copying. Once your program becomes stable, it might be generating little or no garbage. Despite that, a copy collector will still copy all the memory from one place to another, which is wasteful. To prevent this, some JVMs detect that no new garbage is being generated and switch to a different scheme (this is the “adaptive” part). This other scheme is called mark-and-sweep, and it’s what earlier versions of Sun’s JVM used all the time. For general use, mark-and-sweep is fairly slow, but when you know you’re generating little or no garbage, it’s fast.

Mark-and-sweep follows the same logic of starting from the stack and static storage and tracing through all the references to find live objects. However, each time it finds a live object, that object is marked by setting a flag in it, but the object isn’t collected yet. Only when the marking process is finished does the sweep occur. During the sweep, the dead objects are released. However, no copying happens, so if the collector chooses to compact a fragmented heap, it does so by shuffling objects around.

The “stop-and-copy” refers to the idea that this type of garbage collection is not done in the background; instead, the program is stopped while the garbage collection occurs. In the Sun literature you’ll find many references to garbage collection as a low-priority background process, but it turns out that the garbage collection was not implemented that way, at least in earlier versions of the Sun JVM. Instead, the Sun garbage collector ran when memory got low. In addition, mark-and-sweep requires that the program be stopped.

As previously mentioned, in the JVM described here memory is allocated in big blocks. If you allocate a large object, it gets its own block. Strict stop-and-copy requires copying every live object from the source heap to a new heap before you could free the old one, which translates to lots of memory. With blocks, the garbage collection can typically copy objects to dead blocks as it collects. Each block has a generation count to keep track of whether it’s alive. In the normal case, only the blocks created since the last garbage collection are compacted; all other blocks get their generation count bumped if they have been referenced from somewhere. This handles the normal case of lots of short-lived temporary objects. Periodically, a full sweep is made—large objects are still not copied (they just get their generation count bumped), and blocks containing small objects are copied and compacted. The JVM monitors the efficiency of garbage collection and if it becomes a waste of time because all objects are long-lived, then it switches to mark-and-sweep. Similarly, the JVM keeps track of how successful mark-and-sweep is, and if the heap starts to become fragmented, it switches back to stop-and-copy. This is where the “adaptive” part comes in, so you end up with a mouthful: “Adaptive generational stop-and-copy mark-and-sweep.”

There are a number of additional speedups possible in a JVM. An especially important one involves the operation of the loader and what is called a just-in-time (JIT) compiler. A JIT compiler partially or fully converts a program into native machine code so that it doesn’t need to be interpreted by the JVM and thus runs much faster. When a class must be loaded (typically, the first time you want to create an object of that class), the .class file is located, and the byte codes for that class are brought into memory. At this point, one approach is to simply JIT compile all the code, but this has two drawbacks: it takes a little more time, which, compounded throughout the life of the program, can add up; and it increases the size of the executable (byte codes are significantly more compact than expanded JIT code), and this might cause paging, which definitely slows down a program. An alternative approach is lazy evaluation, which means that the code is not JIT compiled until necessary. Thus, code that never gets executed might never be JIT compiled. The Java HotSpot technologies in recent JDKs take a similar approach by increasingly optimizing a piece of code each time it is executed, so the more the code is executed, the faster it gets.
Thinking in Java
Prev Contents / Index Next


 
 
   Reproduced courtesy of Bruce Eckel, MindView, Inc. Design by Interspire