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Garbage Collectionâ"Part 2: Automatic Memory Management in the Microsoft .NET Framework
|This article assumes youï¿½re familiar with C and C++|
|Level of Difficulty 1 2 3 |
|SUMMARY The first part of this two-part article explained how the garbage collection algorithm works, how resources can clean up properly when the garbage collector decides to free a resource's memory, and how to force an object to clean up when it is freed. The conclusion of this series explains strong and weak object references that help to manage memory for large objects, as well as object generations and how they improve performance. In addition, the use of methods and properties for controlling garbage collection, resources for monitoring collection performance, and garbage collection for multithreaded applications are covered. |
| ast month, I described the motivation for garbage-collected environments: to simplify memory management for the developer. I also discussed the general algorithm used by the common language runtime (CLR) and some of the internal workings of this algorithm. In addition, I explained how the developer must still explicitly handle resource management and cleanup by implementing Finalize, Close, and/or Dispose methods. This month, I will conclude my discussion of the CLR garbage collector.|
I'll start by exploring a feature called weak references, which you can use to reduce the memory pressure placed on the managed heap by large objects. Then, I'll discuss how the garbage collector uses generations as a performance enhancement. Finally, I'll wrap up by discussing a few other performance enhancements offered by the garbage collector, such as multithreaded collections and performance counters exposed by the CLR that allow you to monitor the garbage collector's real-time behavior.
Weak References When a root points to an object, the object cannot be collected because the application's code can reach the object. When a root points to an object, it's called a strong reference to the object. However, the garbage collector also supports weak references. Weak references allow the garbage collector to collect the object, but they also allow the application to access the object. How can this be? It all comes down to timing.
If only weak references to an object exist and the garbage collector runs, the object is collected and when the application later attempts to access the object, the access will fail. On the other hand, to access a weakly referenced object, the application must obtain a strong reference to the object. If the application obtains this strong reference before the garbage collector collects the object, then the garbage collector can't collect the object because a strong reference to the object exists. I know this all sounds somewhat confusing, so let's clear it up by examining the code in Figure 1.
Why might you use weak references? Well, there are some data structures that are created easily, but require a lot of memory. For example, you might have an application that needs to know all the directories and files on the user's hard drive. You can easily build a tree that reflects this information and as your application runs, you'll refer to the tree in memory instead of actually accessing the user's hard disk. This procedure greatly improves the performance of your application.
The problem is that the tree could be extremely large, requiring quite a bit of memory. If the user starts accessing a different part of your application, the tree may no longer be necessary and is wasting valuable memory. You could delete the tree, but if the user switches back to the first part of your application, you'll need to reconstruct the tree again. Weak references allow you to handle this scenario quite easily and efficiently.
When the user switches away from the first part of the application, you can create a weak reference to the tree and destroy all strong references. If the memory load is low for the other part of the application, then the garbage collector will not reclaim the tree's objects. When the user switches back to the first part of the application, the application attempts to obtain a strong reference for the tree. If successful, the application doesn't have to traverse the user's hard drive again.
The WeakReference type offers two constructors:
The target parameter identifies the object that the WeakReference object should track. The trackResurrection parameter indicates whether the WeakReference object should track the object after it has had its Finalize method called. Usually, false is passed for the trackResurrection parameter and the first constructor creates a WeakReference that does not track resurrection. (For an explanation of resurrection, see part 1 of this article at http://msdn.microsoft.com/msdnmag/issues/1100/GCI/GCI.asp.)
WeakReference(Object target, Boolean trackResurrection);
For convenience, a weak reference that does not track resurrection is called a short weak reference, while a weak reference that does track resurrection is called a long weak reference. If an object's type doesn't offer a Finalize method, then short and long weak references behave identically. It is strongly recommended that you avoid using long weak references. Long weak references allow you to resurrect an object after it has been finalized and the state of the object is unpredictable.
Once you've created a weak reference to an object, you usually set the strong reference to the object to null. If any strong reference remains, the garbage collector will be unable to collect the object.
To use the object again, you must turn the weak reference into a strong reference. You accomplish this simply by calling the WeakReference object's Target property and assigning the result to one of your application's roots. If the Target property returns null, then the object was collected. If the property does not return null, then the root is a strong reference to the object and the code may manipulate the object. As long as the strong reference exists, the object cannot be collected.
Weak Reference Internals From the previous discussion, it should be obvious that WeakReference objects do not behave like other object types. Normally, if your application has a root that refers to an object and that object refers to another object, then both objects are reachable and the garbage collector cannot reclaim the memory in use by either object. However, if your application has a root that refers to a WeakReference object, then the object referred to by the WeakReference object is not considered reachable and may be collected.
To fully understand how weak references work, let's look inside the managed heap again. The managed heap contains two internal data structures whose sole purpose is to manage weak references: the short weak reference table and the long weak reference table. These two tables simply contain pointers to objects allocated within the managed heap.
Initially, both tables are empty. When you create a WeakReference object, an object is not allocated from the managed heap. Instead, an empty slot in one of the weak reference tables is located; short weak references use the short weak reference table and long weak references use the long weak reference table.
Once an empty slot is found, the value in the slot is set to the address of the object you wish to trackâ"the object's pointer is passed to the WeakReference's constructor. The value returned from the new operator is the address of the slot in the WeakReference table. Obviously, the two weak reference tables are not considered part of an application's roots or the garbage collector would not be able to reclaim the objects pointed to by the tables.
Now, here's what happens when a garbage collection (GC) runs:
Once you understand the logic of the garbage collection process, it's easy to understand how weak references work. Accessing the WeakReference's Target property causes the system to return the value in the appropriate weak reference table's slot. If null is in the slot, the object was collected.
- The garbage collector builds a graph of all the reachable objects. Part 1 of this article discussed how this works.
- The garbage collector scans the short weak reference table. If a pointer in the table refers to an object that is not part of the graph, then the pointer identifies an unreachable object and the slot in the short weak reference table is set to null.
- The garbage collector scans the finalization queue. If a pointer in the queue refers to an object that is not part of the graph, then the pointer identifies an unreachable object and the pointer is moved from the finalization queue to the freachable queue. At this point, the object is added to the graph since the object is now considered reachable.
- The garbage collector scans the long weak reference table. If a pointer in the table refers to an object that is not part of the graph (which now contains the objects pointed to by entries in the freachable queue), then the pointer identifies an unreachable object and the slot is set to null.
- The garbage collector compacts the memory, squeezing out the holes left by the unreachable objects.
A short weak reference doesn't track resurrection. This means that the garbage collector sets the pointer to null in the short weak reference table as soon as it has determined that the object is unreachable. If the object has a Finalize method, the method has not been called yet so the object still exists. If the application accesses the WeakReference object's Target property, then null will be returned even though the object actually still exists.
A long weak reference tracks resurrection. This means that the garbage collector sets the pointer to null in the long weak reference table when the object's storage is reclaimable. If the object has a Finalize method, the Finalize method has been called and the object was not resurrected.
Generations When I first started working in a garbage-collected environment, I had many concerns about performance. After all, I've been a C/C++ programmer for more than 15 years and I understand the overhead of allocating and freeing memory blocks from a heap. Sure, each version of WindowsÂ® and each version of the C runtime has tweaked the internals of the heap algorithms in order to improve performance.
Well, like the developers of Windows and the C runtime, the GC developers are tweaking the garbage collector to improve its performance. One feature of the garbage collector that exists purely to improve performance is called generations. A generational garbage collector (also known as an ephemeral garbage collector) makes the following assumptions:
Of course, many studies have demonstrated that these assumptions are valid for a very large set of existing applications. So, let's discuss how these assumptions have influenced the implementation of the garbage collector.
- The newer an object is, the shorter its lifetime will be.
- The older an object is, the longer its lifetime will be.
- Newer objects tend to have strong relationships to each other and are frequently accessed around the same time.
- Compacting a portion of the heap is faster than compacting the whole heap.
When initialized, the managed heap contains no objects. Objects added to the heap are said to be in generation 0, as you can see in Figure 2. Stated simply, objects in generation 0 are young objects that have never been examined by the garbage collector.
Figure 2 Generation 0
Now, if more objects are added to the heap, the heap fills and a garbage collection must occur. When the garbage collector analyzes the heap, it builds the graph of garbage (shown here in purple) and non-garbage objects. Any objects that survive the collection are compacted into the left-most portion of the heap. These objects have survived a collection, are older, and are now considered to be in generation 1 (see Figure 3).
Figure 3 Generations 0 and 1
As even more objects are added to the heap, these new, young objects are placed in generation 0. If generation 0 fills again, a GC is performed. This time, all objects in generation 1 that survive are compacted and considered to be in generation 2 (see Figure 4). All survivors in generation 0 are now compacted and considered to be in generation 1. Generation 0 currently contains no objects, but all new objects will go into generation 0.
Figure 4 Generations 0, 1, and 2
Currently, generation 2 is the highest generation supported by the runtime's garbage collector. When future collections occur, any surviving objects currently in generation 2 simply stay in generation 2.
Generational GC Performance Optimizations As I stated earlier, generational garbage collecting improves performance. When the heap fills and a collection occurs, the garbage collector can choose to examine only the objects in generation 0 and ignore the objects in any greater generations. After all, the newer an object is, the shorter its lifetime is expected to be. So, collecting and compacting generation 0 objects is likely to reclaim a significant amount of space from the heap and be faster than if the collector had examined the objects in all generations.
This is the simplest optimization that can be obtained from generational GC. A generational collector can offer more optimizations by not traversing every object in the managed heap. If a root or object refers to an object in an old generation, the garbage collector can ignore any of the older objects' inner references, decreasing the time required to build the graph of reachable objects. Of course, it is possible that an old object refers to a new object. So that these objects are examined, the collector can take advantage of the system's write-watch support (provided by the Win32Â® GetWriteWatch function in Kernel32.dll). This support lets the collector know which old objects (if any) have been written to since the last collection. These specific old objects can have their references checked to see if they refer to any new objects.
If collecting generation 0 doesn't provide the necessary amount of storage, then the collector can attempt to collect the objects from generations 1 and 0. If all else fails, then the collector can collect the objects from all generationsâ"2, 1, and 0. The exact algorithm used by the collector to determine which generations to collect is one of those areas that Microsoft will be tweaking forever.
Most heaps (like the C runtime heap) allocate objects wherever they find free space. Therefore, if I create several objects consecutively, it is quite possible that these objects will be separated by megabytes of address space. However, in the managed heap, allocating several objects consecutively ensures that the objects are contiguous in memory.
One of the assumptions stated earlier was that newer objects tend to have strong relationships to each other and are frequently accessed around the same time. Since new objects are allocated contiguously in memory, you gain performance from locality of reference. More specifically, it is highly likely that all the objects can reside in the CPU's cache. Your application will access these objects with phenomenal speed since the CPU will be able to perform most of its manipulations without having cache misses which forces RAM access.
Microsoft's performance tests show that managed heap allocations are faster than standard allocations performed by the Win32 HeapAlloc function. These tests also show that it takes less than 1 millisecond on a 200Mhz Pentium to perform a full GC of generation 0. It is Microsoft's goal to make GCs take no more time than an ordinary page fault.
Direct Control with System.GC The System.GC type allows your application some direct control over the garbage collector. For starters, you can query the maximum generation supported by the managed heap by reading the GC.MaxGeneration property. Currently, the GC.MaxGeneration property always returns 2.
It is also possible to force the garbage collector to perform a collection by calling one of the two methods shown here:
The first method allows you to specify which generation to collect. You may pass any integer from 0 to GC.MaxGeneration, inclusive. Passing 0 causes generation 0 to be collected; passing 1 causes generation 1 and 0 to be collected; and passing 2 causes generation 2, 1, and 0 to be collected. The version of the Collect method that takes no parameters forces a full collection of all generations and is equivalent to calling:
void GC.Collect(Int32 Generation)
Under most circumstances, you should avoid calling any of the Collect methods; it is best to just let the garbage collector run on its own accord. However, since your application knows more about its behavior than the runtime does, you could help matters by explicitly forcing some collections. For example, it might make sense for your application to force a full collection of all generations after the user saves his data file. I imagine Internet browsers performing a full collection when pages are unloaded. You might also want to force a collection when your application is performing other lengthy operations; this hides the fact that the collection is taking processing time and prevents a collection from occurring when the user is interacting with your application.
The GC type also offers a WaitForPendingFinalizers method. This method simply suspends the calling thread until the thread processing the freachable queue has emptied the queue, calling each object's Finalize method. In most applications, it is unlikely that you will ever have to call this method.
Lastly, the garbage collector offers two methods that allow you to determine which generation an object is currently in:
The first version of GetGeneration takes an object reference as a parameter, and the second version takes a WeakReference reference as a parameter. Of course, the value returned will be somewhere between 0 and GC.MaxGeneration, inclusive.
Int32 GetGeneration(Object obj)
Int32 GetGeneration(WeakReference wr)
The code in Figure 5 will help you understand how generations work. It also demonstrates the use of the garbage collection methods just discussed.
Performance for Multithreaded Applications In the previous section, I explained the GC algorithm and optimizations. However, there was a big assumption made during that discussion: only one thread is running. In the real world, it is quite likely that multiple threads will be accessing the managed heap or at least manipulating objects allocated within the managed heap. When one thread sparks a collection, other threads must not access any objects (including object references on its own stack) since the collector is likely to move these objects, changing their memory locations.
So, when the garbage collector wants to start a collection, all threads executing managed code must be suspended. The runtime has a few different mechanisms that it uses to safely suspend threads so that a collection may be done. The reason there are multiple mechanisms is to keep threads running as long as possible and to reduce overhead as much as possible. I don't want to go into all the details here, but suffice it to say that Microsoft has done a lot of work to reduce the overhead involved with performing a collection. Microsoft will continue to modify these mechanisms over time to help ensure efficient garbage collections.
The following paragraphs describe a few of the mechanisms that the garbage collector employs when applications have multiple threads:
Fully Interruptible Code When a collection starts, the collector suspends all application threads. The collector then determines where a thread got suspended and using tables produced by the just-in-time (JIT) compiler, the collector can tell where in a method the thread stopped, what object references the code is currently accessing, and where those references are held (in a variable, CPU register, and so on).
Hijacking The collector can modify a thread's stack so that the return address points to a special function. When the currently executing method returns, this special function will execute, suspending the thread. Stealing the thread's execution path this way is referred to as hijacking the thread. When the collection is complete, the thread will resume and return to the method that originally called it.
Safe Points As the JIT compiler compiles a method, it can insert calls to a special function that checks if a GC is pending. If so, the thread is suspended, the GC runs to completion, and the thread is then resumed. The position where the compiler inserts these method calls is called a GC safe point.
Note that thread hijacking allows threads that are executing unmanaged code to continue execution while a garbage collection is occurring. This is not a problem since unmanaged code is not accessing objects on the managed heap unless the objects are pinned and don't contain object references. A pinned object is one that the garbage collector is not allowed to move in memory. If a thread that is currently executing unmanaged code returns to managed code, the thread is hijacked and is suspended until the GC completes.
In addition to the mechanisms I just mentioned, the garbage collector offers some additional improvements that enhance the performance of object allocations and collections when applications have multiple threads.
Synchronization-free Allocations On a multiprocessor system, generation 0 of the managed heap is split into multiple memory arenas using one arena per thread. This allows multiple threads to make allocations simultaneously so that exclusive access to the heap is not required.
Scalable Collections On a multiprocessor system running the server version of the execution engine (MSCorSvr.dll), the managed heap is split into several sections, one per CPU. When a collection is initiated, the collector has one thread per CPU; all threads collect their own sections simultaneously. The workstation version of the execution engine (MSCorWks.dll) doesn't support this feature.
Garbage-collecting Large Objects There is one more performance improvement that you might want to be aware of. Large objects (those that are 20,000 bytes or larger) are allocated from a special large object heap. Objects in this heap are finalized and freed just like the small objects I've been talking about. However, large objects are never compacted because shifting 20,000-byte blocks of memory down in the heap would waste too much CPU time.
Note that all of these mechanisms are transparent to your application code. To you, the developer, it looks like there is just one managed heap; these mechanisms exist simply to improve application performance.
Monitoring Garbage Collections The runtime team at Microsoft has created a set of performance counters that provide a lot of real-time statistics about the runtime's operations. You can view these statistics via the Windows 2000 System Monitor ActiveXÂ® control. The easiest way to access the System Monitor control is to run PerfMon.exe and select the + toolbar button, causing the Add Counters dialog box to appear (see Figure 6).
Figure 6 Adding Performance Counters
To monitor the runtime's garbage collector, select the COM+ Memory Performance object. Then, you can select a specific application from the instance list box. Finally, select the set of counters that you're interested in monitoring and press the Add button followed by the Close button. At this point, the System Monitor will graph the selected real-time statistics. Figure 7 describes the function of each counter.
Conclusion So that's just about the full story on garbage collection. Last month I provided the background on how resources are allocated, how automatic garbage collection works, how to use the finalization feature to allow an object to clean up after itself, and how the resurrection feature can restore access to objects. This month I explained how weak and strong references to objects are implemented, how classifying objects in generations results in performance benefits, and how you can manually control garbage collection with System.GC. I also covered the mechanisms the garbage collector uses in multithreaded applications to improve performance, what happens with objects that are larger than 20,000 bytes, and finally, how you can use the Windows 2000 System Monitor to track garbage collection performance. With this information in hand, you should be able to simplify memory management and boost performance in your applications.
| For related articles see:|
For background information see:
Garbage Collection: Algorithms for Automatic Dynamic Memory Management by Richard Jones and Rafael Lins (John Wiley & Son, 1996)
Programming Applications for Microsoft Windows by Jeffrey Richter (Microsoft Press, 1999)
| Jeffrey Richter (http://www.JeffreyRichter.com) is the author of Programming Applications for Microsoft Windows (Microsoft Press, 1999), and is a co-founder of Wintellect (http://www.Wintellect.com), a software education, debugging, and consulting firm. He specializes in programming/design for .NET and Win32. Jeff is currently writing a Microsoft .NET Framework programming book and offers .NET technology seminars.|
From the December 2000 issue of MSDN Magazine