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Java Memory Leak - find all objects of class X not referenced by class Y

I have a memory leak in Java in which I have 9600 ImapClients in my heap dump and only 7800 MonitoringTasks. This is a problem since every ImapClient should be owned by a MonitoringTask, so those extra 1800 ImapClients are leaked.
One problem is I can't isolate them in the heap dump and see what's keeping them alive. So far I've only been able to pinpoint them by using external evidence to guess at which ImapClients are dangling. I'm learning OQL which I believe can solve this but it's coming slowly, and it'll take a while before I can understand how to perform something recursive like this in a new query language.
Determining a leak exists is difficult, so here is my full situation:
this process was spewing OOMEs a week ago. I thought I fixed it and I'm trying to verify whether my fixed worked without waiting another full week to see if it spews OOMEs again.
This task creates 7000-9000 ImapClients on start then under normal operation connects and disconnects very few of them.
I checked another process running older pre-OOME code, and it showed numbers of 9000/9100 instead of 7800/9600. I do not know why old code will be different from new code but this is evidence of a leak.
The point of this question is so I can determine if there is a leak. There is a business rule that every ImapClient should be a referee of a MonitoringTask. If this query I am asking about comes up empty, there is not a leak. If it comes up with objects, together with this business rule, it is not only evidence of a leak but conclusive proof of one.

Your expectations are incorrect, there is no actual evidence of any leaks occuring
The Garbage Collector's goal is to free space when it is needed and
only then, anything else is a waste of resources. There is absolutely
no benefit in attempting to keep as much free space as possible
available all the time and only down sides.
Just because something is a candidate for garbage collection doesn't
mean it will ever actually be collected, and there is no way to
force garbage collection either.
I don't see any mention of OutOfMemoryError anywhere.
What you are concerned about you can't control, not directly anyway
What you should focus on is what in in your control, which is making sure you don't hold on to references longer than you need to, and that you are not duplicating things unnecessarily. The garbage collection routines in Java are highly optimized, and if you learn how their algorithms work, you can make sure your program behaves in the optimal way for those algorithms to work.
Java Heap Memory isn't like manually managed memory in other languages, those rules don't apply
What are considered memory leaks in other languages aren't the same thing/root cause as in Java with its garbage collection system.
Most likely in Java memory isn't consumed by one single uber-object that is leaking ( dangling reference in other environments ).
Intermediate objects may be held around longer than expected by the garbage collector because of the scope they are in and lots of other things that can vary at run time.
EXAMPLE: the garbage collector may decide that there are candidates, but because it considers that there is plenty of memory still to be had that it might be too expensive time wise to flush them out at that point in time, and it will wait until memory pressure gets higher.
The garbage collector is really good now, but it isn't magic, if you are doing degenerate things, it will cause it to not work optimally. There is lots of documentation on the internet about the garbage collector settings for all the versions of the JVMs.
These un-referenced objects may just have not reached the time that the garbage collector thinks it needs them to for them to be expunged from memory, or there could be references to them held by some other object ( List ) for example that you don't realize still points to that object. This is what is most commonly referred to as a leak in Java, which is a reference leak more specifically.
I don't see any mention of OutOfMemoryError
You probably don't have a problem in your code, the garbage collection system just might not be getting put under enough pressure to kick in and deallocate objects that you think it should be cleaning up. What you think is a problem probably isn't, not unless your program is crashing with OutOfMemoryError. This isn't C, C++, Objective-C, or any other manual memory management language / runtime. You don't get to decide what is in memory or not at the detail level you are expecting you should be able to.

Check your code for finalizers, especially anything relating to IMapclient.
It could be that your MonitoringTasks are being easily collected whereas your IMapclient's are finalized, and therefore stay on the heap (though dead) until the finalizer thread runs.

The obvious answer is to add a WeakHashMap<X, Object> (and Y) to your code -- one tracking all instances of X and another tracking all instances of Y (make them static members of the class and insert every object into the map in the constructor with a null 'value'). Then you can at any time iterate over these maps to find all live instances of X and Y and see which Xs are not referenced by Ys. You might want to trigger a full GC first, to ignore objects that are dead and not yet collected.

Garbage Collector taking too much CPU Time

I've developed a Web Application which process a huge amount of data and takes a lot of time to complete?
So now I am doing profiling of my application and I noticed one very bad thing about GC.
When a Full GC occurred it stops all process for 30 - 40 secs.
I wonder if there is any way to improve this. I don't want to waist my CPU's that much time only in GC. Below are some details that can be useful:
I am using Java 1.6.0.23
My Application takes 20 GB max memory.
A full GC occur after every 14 minutes.
Memory Before GC is 20 GB and after GC is 7.8 GB
Memory used in CPU (i.e. shown in task manager) is 41 GB.
After process completed(JVM is still running) Used memory 5 GB and free memory 15 GB.

There are many algorithms that modern JVM's use for garbage collection. Some algorithms such as reference counting are so fast, and some such as memory copying are so slow. You could change your code so that help the JVM to use the faster algorithms most of the time.
One of the fastest algorithms is reference counting, and as the name describes, it counts references to an object, and when it reaches zero, it is ready for garbage collection, and after that it decreases reference count to objects referenced by the current GCed object.
To help JVM to use this algorithm, avoid having circular references (object A references B, then B references C, C references D ...., and Z references A again). Because even when the whole object graph is not reachable, none of the object's reference counters reaches zero.
You could only just break the circle when you don't need the objects in the circle any more (by assigning null to one of references)....

If you use 64 bit architecture add:
-XX:+UseCompressedOops 64bit addresses are converted to 32bit
Use G1GC instead of CMS:
-XX:+UseG1GC - it use incremental steps
Set the same initial and max size: -Xms5g -Xmx5g
Tune parameters (just example):
-XX:MaxGCPauseMillis=100 -XX:GCPauseIntervalMillis=1000
See Java HotSpot VM Options Performance Options

Either improve app by reusing resources or kick-in System.gc() yourself in some critical regions of the app (which is not guaranteed to help you). Most likely you have a memory leak somewhere that you have to investigate and consequently restructure the code.

The fewer things you new, the fewer things need to be collected.
Suppose you have class A.
You can include in it a reference to another instance of class A.
That way you can make a "free list" of instances of A.
Whenever you need an A, just pop one off the free list.
If the free list is empty, then new one.
When you no longer need it, push it on the free list.
This can save a lot of time.

The amount of time spent in GC depends on two factors:
How many objects are live (= can be reached from anyone)
How many dead objects implement finalize()
Objects which can't be reached and which don't use finalize() cost nothing to clean up in Java which is why Java is usually on par with other languages like C++ (and often much better because C++ spends a lot of time to delete objects).
So what you need to do in your app is cut down on the number of objects that survive and/or cut references to objects (that you no longer need) earlier in the code. Example:
When you have a very long method, you will keep all the objects alive that you reference from local variables. If you split that method in many smaller methods, the references will be lost faster and the GC won't have to deal with those objects.
If you put everything that you might need in huge hash maps, the maps will keep all those instances alive until your code completes. So even when you don't need those anymore, the GC will still have to spend time on them.

Why does java wait so long to run the garbage collector?

I am building a Java web app, using the Play! Framework. I'm hosting it on playapps.net. I have been puzzling for a while over the provided graphs of memory consumption. Here is a sample:
The graph comes from a period of consistent but nominal activity. I did nothing to trigger the falloff in memory, so I presume this occurred because the garbage collector ran as it has almost reached its allowable memory consumption.
My questions:
Is it fair for me to assume that my application does not have a memory leak, as it appears that all the memory is correctly reclaimed by the garbage collector when it does run?
(from the title) Why is java waiting until the last possible second to run the garbage collector? I am seeing significant performance degradation as the memory consumption grows to the top fourth of the graph.
If my assertions above are correct, then how can I go about fixing this issue? The other posts I have read on SO seem opposed to calls to System.gc(), ranging from neutral ("it's only a request to run GC, so the JVM may just ignore you") to outright opposed ("code that relies on System.gc() is fundamentally broken"). Or am I off base here, and I should be looking for defects in my own code that is causing this behavior and intermittent performance loss?
UPDATE
I have opened a discussion on PlayApps.net pointing to this question and mentioning some of the points here; specifically #Affe's comment regarding the settings for a full GC being set very conservatively, and #G_H's comment about settings for the initial and max heap size.
Here's a link to the discussion, though you unfortunately need a playapps account to view it.
I will report the feedback here when I get it; thanks so much everyone for your answers, I've already learned a great deal from them!
Resolution
Playapps support, which is still great, didn't have many suggestions for me, their only thought being that if I was using the cache extensively this may be keeping objects alive longer than need be, but that isn't the case. I still learned a ton (woo hoo!), and I gave #Ryan Amos the green check as I took his suggestion of calling System.gc() every half day, which for now is working fine.

Any detailed answer is going to depend on which garbage collector you're using, but there are some things that are basically the same across all (modern, sun/oracle) GCs.
Every time you see the usage in the graph go down, that is a garbage collection. The only way heap gets freed is through garbage collection. The thing is there are two types of garbage collections, minor and full. The heap gets divided into two basic "areas." Young and tenured. (There are lots more subgroups in reality.) Anything that is taking up space in Young and is still in use when the minor GC comes along to free up some memory, is going to get 'promoted' into tenured. Once something makes the leap into tenured, it sits around indefinitely until the heap has no free space and a full garbage collection is necessary.
So one interpretation of that graph is that your young generation is fairly small (by default it can be a fairly small % of total heap on some JVMs) and you're keeping objects "alive" for comparatively very long times. (perhaps you're holding references to them in the web session?) So your objects are 'surviving' garbage collections until they get promoted into tenured space, where they stick around indefinitely until the JVM is well and good truly out of memory.
Again, that's just one common situation that fits with the data you have. Would need full details about the JVM configuration and the GC logs to really tell for sure what's going on.

Java won't run the garbage cleaner until it has to, because the garbage cleaner slows things down quite a bit and shouldn't be run that frequently. I think you would be OK to schedule a cleaning more frequently, such as every 3 hours. If an application never consumes full memory, there should be no reason to ever run the garbage cleaner, which is why Java only runs it when the memory is very high.
So basically, don't worry about what others say: do what works best. If you find performance improvements from running the garbage cleaner at 66% memory, do it.

I am noticing that the graph isn't sloping strictly upward until the drop, but has smaller local variations. Although I'm not certain, I don't think memory use would show these small drops if there was no garbage collection going on.
There are minor and major collections in Java. Minor collections occur frequently, whereas major collections are rarer and diminish performance more. Minor collections probably tend to sweep up stuff like short-lived object instances created within methods. A major collection will remove a lot more, which is what probably happened at the end of your graph.
Now, some answers that were posted while I'm typing this give good explanations regarding the differences in garbage collectors, object generations and more. But that still doesn't explain why it would take so absurdly long (nearly 24 hours) before a serious cleaning is done.
Two things of interest that can be set for a JVM at startup are the maximum allowed heap size, and the initial heap size. The maximum is a hard limit, once you reach that, further garbage collection doesn't reduce memory usage and if you need to allocate new space for objects or other data, you'll get an OutOfMemoryError. However, internally there's a soft limit as well: the current heap size. A JVM doesn't immediately gobble up the maximum amount of memory. Instead, it starts at your initial heap size and then increases the heap when it's needed. Think of it a bit as the RAM of your JVM, that can increase dynamically.
If the actual memory use of your application starts to reach the current heap size, a garbage collection will typically be instigated. This might reduce the memory use, so an increase in heap size isn't needed. But it's also possible that the application currently does need all that memory and would exceed the heap size. In that case, it is increased provided that it hasn't already reached the maximum set limit.
Now, what might be your case is that the initial heap size is set to the same value as the maximum. Suppose that would be so, then the JVM will immediately seize all that memory. It will take a very long time before the application has accumulated enough garbage to reach the heap size in memory usage. But at that moment you'll see a large collection. Starting with a small enough heap and allowing it to grow keeps the memory use limited to what's needed.
This is assuming that your graph shows heap use and not allocated heap size. If that's not the case and you are actually seeing the heap itself grow like this, something else is going on. I'll admit I'm not savvy enough regarding the internals of garbage collection and its scheduling to be absolutely certain of what's happening here, most of this is from observation of leaking applications in profilers. So if I've provided faulty info, I'll take this answer down.

As you might have noticed, this does not affect you. The garbage collection only kicks in if the JVM feels there is a need for it to run and this happens for the sake of optimization, there's no use of doing many small collections if you can make a single full collection and do a full cleanup.
The current JVM contains some really interesting algorithms and the garbage collection itself id divided into 3 different regions, you can find a lot more about this here, here's a sample:
Three types of collection algorithms
The HotSpot JVM provides three GC algorithms, each tuned for a specific type of collection within a specific generation. The copy (also known as scavenge) collection quickly cleans up short-lived objects in the new generation heap. The mark-compact algorithm employs a slower, more robust technique to collect longer-lived objects in the old generation heap. The incremental algorithm attempts to improve old generation collection by performing robust GC while minimizing pauses.
Copy/scavenge collection
Using the copy algorithm, the JVM reclaims most objects in the new generation object space (also known as eden) simply by making small scavenges -- a Java term for collecting and removing refuse. Longer-lived objects are ultimately copied, or tenured, into the old object space.
Mark-compact collection
As more objects become tenured, the old object space begins to reach maximum occupancy. The mark-compact algorithm, used to collect objects in the old object space, has different requirements than the copy collection algorithm used in the new object space.
The mark-compact algorithm first scans all objects, marking all reachable objects. It then compacts all remaining gaps of dead objects. The mark-compact algorithm occupies more time than the copy collection algorithm; however, it requires less memory and eliminates memory fragmentation.
Incremental (train) collection
The new generation copy/scavenge and the old generation mark-compact algorithms can't eliminate all JVM pauses. Such pauses are proportional to the number of live objects. To address the need for pauseless GC, the HotSpot JVM also offers incremental, or train, collection.
Incremental collection breaks up old object collection pauses into many tiny pauses even with large object areas. Instead of just a new and an old generation, this algorithm has a middle generation comprising many small spaces. There is some overhead associated with incremental collection; you might see as much as a 10-percent speed degradation.
The -Xincgc and -Xnoincgc parameters control how you use incremental collection. The next release of HotSpot JVM, version 1.4, will attempt continuous, pauseless GC that will probably be a variation of the incremental algorithm. I won't discuss incremental collection since it will soon change.
This generational garbage collector is one of the most efficient solutions we have for the problem nowadays.

I had an app that produced a graph like that and acted as you describe. I was using the CMS collector (-XX:+UseConcMarkSweepGC). Here is what was going on in my case.
I did not have enough memory configured for the application, so over time I was running into fragmentation problems in the heap. This caused GCs with greater and greater frequency, but it did not actually throw an OOME or fail out of CMS to the serial collector (which it is supposed to do in that case) because the stats it keeps only count application paused time (GC blocks the world), application concurrent time (GC runs with application threads) is ignored for those calculations. I tuned some parameters, mainly gave it a whole crap load more heap (with a very large new space), set -XX:CMSFullGCsBeforeCompaction=1, and the problem stopped occurring.

Probably you do have memory leaks that's cleared every 24 hours.

Question about java garbage collection

I have this class and I'm testing insertions with different data distributions. I'm doing this in my code:
...
AVLTree tree = new AVLTree();
//insert the data from the first distribution
//get results
...
tree = new AVLTree();
//inser the data from the next distribution
//get results
...
I'm doing this for 3 distributions. Each one should be tested an average of 14 times, and the 2 lowest/highest values removed from to compute the average. This should be done 2000 times, each time for 1000 elements. In other words, it goes 1000, 2000, 3000, ..., 2000000.
The problem is, I can only get as far as 100000. When I tried 200000, I ran out of heap space. I increased the available heap space with -Xmx in the command line to 1024m and it didn't even complete the tests with 200000. I tried 2048m and again, it wouldn't work.
What I'm thinking is that the garbage collector isn't getting rid of the old trees once I do tree = new AVL Tree(). But why? I thought that the elements from the old trees would no longer be accessible and their memory would be cleaned up.

The garbage collector should have no trouble cleaning up your old tree objects, so I can only assume there's some other allocation that you're doing that's not being cleaned up.

Java has a good tool to watch the GC in progress (or not in your case), JVisualVM, which comes with the JDK.
Just run that and it will show you which objects are taking up the heap, and you can both trigger and see the progress of GC's. Then you can target those for pools so they can be re-used by you, saving the GC the work.
Also look into this option, which will probably stop the error you're getting that stops the program, and you program will finish, but it may take a long time because your app will fill up the heap then run very slowly.
-XX:-UseGCOverheadLimit

Which JVM you are using and what JVM parameters you have used to configure GC?
Your explaination shows there is a memory leak in your code. If you have any tool like jprofiler then use it to find out where is the memory leak.

There's no reason those trees shouldn't be collected, although I'd expect that before you ran out of memory you should see long pauses as the system ran a full GC. As it's been noted here that that's not what you're seeing, you could try running with flags like -XX:-PrintGC, -XX:-PrintGCDetails,-XX:-PrintGCTimeStamps to give you some more information on exactly what's going on, along with perhaps some sort of running count of roughly where you are. You could also explicitly tell the garbage collector to use a different garbage-collection algorithm.
However, it still seems unlikely to me. What other code is running? is it possible there's something in the AVLTree class itself that's keeping its instances from being GC'd? What about manually logging the finalize() on that class to insure that (some of them, at least) are collectible (e.g. make a few and manually call System.gc())?
GC params here, a nice ref on garbage collection from sun here that's well worth reading.

The Java garbage collector isn't guaranteed to garbage collect after each object's refcount becomes zero. So if you're writing code that is only creating and deleting a lot of objects, it's possible to expend all of the heap space before the gc has a chance to run. Alternatively, Pax's suggestion that there is a memory leak in your code is also a strong possibility.
If you are only doing benchmarking, then you may want to use the java gc function (in the System class I think) between tests, or even re-run you program for each distribution.

We noticed this in a server product. When making a lot of tiny objects that quickly get thrown away, the garbage collector can't keep up. The problem is more pronounced when the tiny objects have pointers to larger objects (e.g. an object that points to a large char[]). The GC doesn't seem to realize that if it frees up the tiny object, it can then free the larger object. Even when calling System.gc() directly, this was still a huge problem (both in 1.5 and 1.6 VMs)!
What we ended up doing and what I recommend to you is to maintain a pool of objects. When your object is no longer needed, throw it into the pool. When you need a new object, grab one from the pool or allocate a new one if the pool is empty. This will also save a small amount of time over pure allocation because Java doesn't have to clear (bzero) the object.
If you're worried about the pool getting too large (and thus wasting memory), you can either remove an arbitrary number of objects from the pool on a regular basis, or use weak references (for example, using java.util.WeakHashMap). One of the advantages of using a pool is that you can track the allocation frequency and totals, and you can adjust things accordingly.
We're using pools of char[] and byte[], and we maintain separate "bins" of sizes in the pool (for example, we always allocate arrays of size that are powers of two). Our product does a lot of string building, and using pools showed significant performance improvements.
Note: In general, the GC does a fine job. We just noticed that with small objects that point to larger structures, the GC doesn't seem to clean up the objects fast enough especially when the VM is under CPU load. Also, System.gc() is just a hint to help schedule the finalizer thread to do more work. Calling it too frequently causes a significant performance hit.

Given that you're just doing this for testing purposes, it might just be good housekeeping to invoke the garbage collector directly using System.gc() (thus forcing it to make a pass). It won't help you if there is a memory leak, but if there isn't, it might buy you back enough memory to get through your test.

Why Java and Python garbage collection methods are different?

Python uses the reference count method to handle object life time. So an object that has no more use will be immediately destroyed.
But, in Java, the GC(garbage collector) destroys objects which are no longer used at a specific time.
Why does Java choose this strategy and what is the benefit from this?
Is this better than the Python approach?

There are drawbacks of using reference counting. One of the most mentioned is circular references: Suppose A references B, B references C and C references B. If A were to drop its reference to B, both B and C will still have a reference count of 1 and won't be deleted with traditional reference counting. CPython (reference counting is not part of python itself, but part of the C implementation thereof) catches circular references with a separate garbage collection routine that it runs periodically...
Another drawback: Reference counting can make execution slower. Each time an object is referenced and dereferenced, the interpreter/VM must check to see if the count has gone down to 0 (and then deallocate if it did). Garbage Collection does not need to do this.
Also, Garbage Collection can be done in a separate thread (though it can be a bit tricky). On machines with lots of RAM and for processes that use memory only slowly, you might not want to be doing GC at all! Reference counting would be a bit of a drawback there in terms of performance...

Actually reference counting and the strategies used by the Sun JVM are all different types of garbage collection algorithms.
There are two broad approaches for tracking down dead objects: tracing and reference counting. In tracing the GC starts from the "roots" - things like stack references, and traces all reachable (live) objects. Anything that can't be reached is considered dead. In reference counting each time a reference is modified the object's involved have their count updated. Any object whose reference count gets set to zero is considered dead.
With basically all GC implementations there are trade offs but tracing is usually good for high through put (i.e. fast) operation but has longer pause times (larger gaps where the UI or program may freeze up). Reference counting can operate in smaller chunks but will be slower overall. It may mean less freezes but poorer performance overall.
Additionally a reference counting GC requires a cycle detector to clean up any objects in a cycle that won't be caught by their reference count alone. Perl 5 didn't have a cycle detector in its GC implementation and could leak memory that was cyclic.
Research has also been done to get the best of both worlds (low pause times, high throughput):
http://cs.anu.edu.au/~Steve.Blackburn/pubs/papers/urc-oopsla-2003.pdf

Darren Thomas gives a good answer. However, one big difference between the Java and Python approaches is that with reference counting in the common case (no circular references) objects are cleaned up immediately rather than at some indeterminate later date.
For example, I can write sloppy, non-portable code in CPython such as
def parse_some_attrs(fname):
return open(fname).read().split("~~~")[2:4]
and the file descriptor for that file I opened will be cleaned up immediately because as soon as the reference to the open file goes away, the file is garbage collected and the file descriptor is freed. Of course, if I run Jython or IronPython or possibly PyPy, then the garbage collector won't necessarily run until much later; possibly I'll run out of file descriptors first and my program will crash.
So you SHOULD be writing code that looks like
def parse_some_attrs(fname):
with open(fname) as f:
return f.read().split("~~~")[2:4]
but sometimes people like to rely on reference counting to always free up their resources because it can sometimes make your code a little shorter.
I'd say that the best garbage collector is the one with the best performance, which currently seems to be the Java-style generational garbage collectors that can run in a separate thread and has all these crazy optimizations, etc. The differences to how you write your code should be negligible and ideally non-existent.

I think the article "Java theory and practice: A brief history of garbage collection" from IBM should help explain some of the questions you have.

One big disadvantage of Java's tracing GC is that from time to time it will "stop the world" and freeze the application for a relatively long time to do a full GC. If the heap is big and the the object tree complex, it will freeze for a few seconds. Also each full GC visits the whole object tree over and over again, something that is probably quite inefficient. Another drawback of the way Java does GC is that you have to tell the jvm what heap size you want (if the default is not good enough); the JVM derives from that value several thresholds that will trigger the GC process when there is too much garbage stacking up in the heap.
I presume that this is actually the main cause of the jerky feeling of Android (based on Java), even on the most expensive cellphones, in comparison with the smoothness of iOS (based on ObjectiveC, and using RC).
I'd love to see a jvm option to enable RC memory management, and maybe keeping GC only to run as a last resort when there is no more memory left.

Garbage collection is faster (more time efficient) than reference counting, if you have enough memory. For example, a copying gc traverses the "live" objects and copies them to a new space, and can reclaim all the "dead" objects in one step by marking a whole memory region. This is very efficient, if you have enough memory. Generational collections use the knowledge that "most objects die young"; often only a few percent of objects have to be copied.
[This is also the reason why gc can be faster than malloc/free]
Reference counting is much more space efficient than garbage collection, since it reclaims memory the very moment it gets unreachable. This is nice when you want to attach finalizers to objects (e.g. to close a file once the File object gets unreachable). A reference counting system can work even when only a few percent of the memory is free. But the management cost of having to increment and decrement counters upon each pointer assignment cost a lot of time, and some kind of garbage collection is still needed to reclaim cycles.
So the trade-off is clear: if you have to work in a memory-constrained environment, or if you need precise finalizers, use reference counting. If you have enough memory and need the speed, use garbage collection.

Reference counting is particularly difficult to do efficiently in a multi-threaded environment. I don't know how you'd even start to do it without getting into hardware assisted transactions or similar (currently) unusual atomic instructions.
Reference counting is easy to implement. JVMs have had a lot of money sunk into competing implementations, so it shouldn't be surprising that they implement very good solutions to very difficult problems. However, it's becoming increasingly easy to target your favourite language at the JVM.

The latest Sun Java VM actually have multiple GC algorithms which you can tweak. The Java VM specifications intentionally omitted specifying actual GC behaviour to allow different (and multiple) GC algorithms for different VMs.
For example, for all the people who dislike the "stop-the-world" approach of the default Sun Java VM GC behaviour, there are VM such as IBM's WebSphere Real Time which allows real-time application to run on Java.
Since the Java VM spec is publicly available, there is (theoretically) nothing stopping anyone from implementing a Java VM that uses CPython's GC algorithm.

Late in the game, but I think one significant rationale for RC in python is its simplicity. See this email by Alex Martelli, for example.
(I could not find a link outside google cache, the email date from 13th october 2005 on python list).