Tag Archives: garbage collection

Digging Into .NET Object Allocation Fundamentals

[Note: this article also appeared on CodeProject]

Introduction

While understanding garbage collection fundamentals is vital to working with .NET, it is also important to understand how object allocation works. It shows you just how simple and performant it is, especially compared to the potentially blocking nature of native heap allocations. In a large, native, multi-threaded application, heap allocations can be major performance bottleneck which requires you to perform all sorts of custom heap management techniques. It’s also harder to measure when this is happening because many of those details are hidden behind the OS’s allocation APIs. More importantly, understanding this will give you clues to how you can mess up and make object allocation far less efficient.

In this article, I want to go through an example taken from Chapter 2 of Writing High-Performance .NET Code and then take it further with some additional examples that weren’t covered in the book.

Viewing Object Allocation in a Debugger

Let’s start with a simple object definition: completely empty.

class MyObject 
{
}

static void Main(string[] args)
{
    var x = new MyObject();
}

In order to examine what happens during allocation, we need to use a “real” debugger, like Windbg. Don’t be afraid of this. If you need a quick primer on how to get started, look at the free sample chapter on this page, which will get you up and running in no time. It’s not nearly as bad you think.

Build the above program in Release mode for x86 (you can do x64 if you’d like, but the samples below are x86).

In Windbg, follow these steps to start and debug the program:

  1. Ctrl+E to execute a program. Navigate to and open the built executable file.
  2. Run command: sxe ld clrjit (this tells the debugger to break on loading any assembly with clrjit in the name, which you need loaded before the next steps)
  3. Run command: g (continues execution)
  4. When it breaks, run command: .loadby sos clr (loads .NET debugging tools)
  5. Run command: !bpmd ObjectAllocationFundamentals Program.Main (Sets a breakpoint at the beginning of a method. The first argument is the name of the assembly. The second is the name of the method, including the class it is in.)
  6. Run command: g

Execution will break at the beginning of the Main method, right before new() is called. Open the Disassembly window to see the code.

Here is the Main method’s code, annotated for clarity:

; Copy method table pointer for the class into
; ecx as argument to new()
; You can use !dumpmt to examine this value.
mov ecx,006f3864h
; Call new
call 006e2100 
; Copy return value (address of object) into a register
mov edi,eax

Note that the actual addresses will be different each time you execute the program. Step over (F10, or toolbar) a few times until call 006e2100 (or your equivalent) is highlighted. Then Step Into that (F11). Now you will see the primary allocation mechanism in .NET. It’s extremely simple. Essentially, at the end of the current gen0 segment, there is a reserved bit of space which I will call the allocation buffer. If the allocation we’re attempting can fit in there, we can update a couple of values and return immediately without more complicated work.

If I were to outline this in pseudocode, it would look like this:

if (object fits in current allocation buffer)
{
   Increment a pointer, return address;
}
else
{
   call JIT_New to do more complicated work in CLR
}

The actual assembly looks like this:

; Set eax to value 0x0c, the size of the object to
; allocate, which comes from the method table
006e2100 8b4104          mov     eax,dword ptr [ecx+4] ds:002b:006f3868=0000000c
; Put allocation buffer information into edx
006e2103 648b15300e0000  mov     edx,dword ptr fs:[0E30h]
; edx+40 contains the address of the next available byte
; for allocation. Add that value to the desired size.
006e210a 034240          add     eax,dword ptr [edx+40h]
; Compare the intended allocation against the
; end of the allocation buffer.
006e210d 3b4244          cmp     eax,dword ptr [edx+44h]
; If we spill over the allocation buffer,
; jump to the slow path
006e2110 7709            ja      006e211b
; update the pointer to the next free
; byte (0x0c bytes past old value)
006e2112 894240          mov     dword ptr [edx+40h],eax
; Subtract the object size from the pointer to
; get to the start of the new obj
006e2115 2b4104          sub     eax,dword ptr [ecx+4]
; Put the method table pointer into the
; first 4 bytes of the object.
; eax now points to new object
006e2118 8908            mov     dword ptr [eax],ecx
; Return to caller
006e211a c3              ret
; Slow Path - call into CLR method
006e211b e914145f71      jmp     clr!JIT_New (71cd3534)

In the fast path, there are only 9 instructions, including the return. That’s incredibly efficient, especially compared to something like malloc. Yes, that complexity is traded for time at the end of object lifetime, but so far, this is looking pretty good!

What happens in the slow path? The short answer is a lot. The following could all happen:

  • A free slot somewhere in gen0 needs to be located
  • A gen0 GC is triggered
  • A full GC is triggered
  • A new memory segment needs to be allocated from the operating system and assigned to the GC heap
  • Objects with finalizers need extra bookkeeping
  • Possibly more…

Another thing to notice is the size of the object: 0x0c (12 decimal) bytes. As covered elsewhere, this is the minimum size for an object in a 32-bit process, even if there are no fields.

Now let’s do the same experiment with an object that has a single int field.

class MyObjectWithInt { int x; }

Follow the same steps as above to get into the allocation code.

The first line of the allocator on my run is:

00882100 8b4104          mov     eax,dword ptr [ecx+4] ds:002b:00893874=0000000c

The only interesting thing is that the size of the object (0x0c) is exactly the same as before. The new int field fit into the minimum size. You can see this by examining the object with the !DumpObject command (or the abbreviated version: !do). To get the address of the object after it has been allocated, step over instructions until you get to the ret instruction. The address of the object is now in the eax register, so open up the Registers view and see the value. On my computer, it has a value of 2372770. Now execute the command: !do 2372770

You should see similar output to this:

0:000> !do 2372770
Name:        ConsoleApplication1.MyObjectWithInt
MethodTable: 00893870
EEClass:     008913dc
Size:        12(0xc) bytes
File:        D:\Ben\My Documents\Visual Studio 2013\Projects\ConsoleApplication1\ConsoleApplication1\bin\Release\ConsoleApplication1.exe
Fields:
      MT    Field   Offset                 Type VT     Attr    Value Name
70f63b04  4000001        4         System.Int32  1 instance        0 x

This is curious. The field is at offset 4 (and an int has a length of 4), so that only accounts for 8 bytes (range 0-7). Offset 0 (i.e., the object’s address) contains the method table pointer, so where are the other 4 bytes? This is the sync block and they are actually at offset -4 bytes, before the object’s address. These are the 12 bytes.

Try it with a long.

class MyObjectWithLong { long x; }

The first line of the allocator is now:

00f22100 8b4104          mov     eax,dword ptr [ecx+4] ds:002b:00f33874=00000010

Showing a size of 0x10 (decimal 16 bytes), which we would expect now. 12 byte minimum object size, but 4 already in the overhead, so an extra 4 bytes for the 8 byte long. And an examination of the allocated object shows an object size of 16 bytes as well.

0:000> !do 2932770
Name:        ConsoleApplication1.MyObjectWithLong
MethodTable: 00f33870
EEClass:     00f313dc
Size:        16(0x10) bytes
File:        D:\Ben\My Documents\Visual Studio 2013\Projects\ConsoleApplication1\ConsoleApplication1\bin\Release\ConsoleApplication1.exe
Fields:
      MT    Field   Offset                 Type VT     Attr    Value Name
70f5b524  4000002        4         System.Int64  1 instance 0 x

If you put an object reference into the test class, you’ll see the same thing as you did with the int.

Finalizers

Now let’s make it more interesting. What happens if the object has a finalizer? You may have heard that objects with finalizers have more overhead during GC. This is true–they will survive longer, require more CPU cycles, and generally cause things to be less efficient. But do finalizers also affect object allocation?

Recall that our Main method above looked like this:

mov ecx,006f3864h
call 006e2100 
mov edi,eax

If the object has a finalizer, however, it looks like this:

mov     ecx,119386Ch
call    clr!JIT_New (71cd3534)
mov     esi,eax

We’ve lost our nifty allocation helper! We have to now jump directly to JIT_New. Allocating an object that has a finalizer is a LOT slower than a normal object. More internal CLR structures need to be modified to track this object’s lifetime. The cost isn’t just at the end of object lifetime.

How much slower is it? In my own testing, it appears to be about 8-10x worse than the fast path of allocating a normal object. If you allocate a lot of objects, this difference is considerable. For this, and other reasons, just don’t add a finalizer unless it really is required.

Calling the Constructor

If you are particularly eagle-eyed, you may have noticed that there was no call to a constructor to initialize the object once allocated. The allocator is changing some pointers, returning you an object, and there is no further function call on that object. This is because memory that belongs to a class field is always pre-initialized to 0 for you and these objects had no further initialization requirements. Let’s see what happens if we change to the following definition:

class MyObjectWithInt { int x = 13; }

Now the Main function looks like this:

mov     ecx,0A43834h
; Allocate memory
call    00a32100
; Copy object address to esi
mov     esi,eax
; Set object + 4 to value 0x0D (13 decimal)
mov     dword ptr [esi+4],0Dh

The field initialization was inlined into the caller!

Note that this code is exactly equivalent:

class MyObjectWithInt { int x; public MyObjectWithInt() { this.x = 13; } }

But what if we do this?

class MyObjectWithInt 
{ 
    int x; 

    [MethodImpl(MethodImplOptions.NoInlining)]  
    public MyObjectWithInt() 
    { 
        this.x = 13; 
    } 
}

This explicitly disables inlining for the object constructor. There are other ways of preventing inlining, but this is the most direct.

Now we can see the call to the constructor happening after the memory allocation:

mov     ecx,0F43834h
call    00f32100
mov     esi,eax
mov     ecx,esi
call    dword ptr ds:[0F43854h]

Exercise for the Reader

Can you get the allocator shown above to jump to the slow path? How big does the allocation request have to be to trigger this? (Hint: Try allocating arrays of various sizes.) Can you figure this out by examining the registers and other values from the running code?

Summary

You can see that in most cases, allocation of objects in .NET is extremely fast and efficient, requiring no calls into the CLR and no complicated algorithms in the simple case. Avoid finalizers unless absolutely needed. Not only are they less efficient during cleanup in a garbage collection, but they are slower to allocate as well.

Play around with the sample code in the debugger to get a feel for this yourself. If you wish to learn more about .NET memory handling, especially garbage collection, take a look at the book Writing High-Performance .NET Code.


Check out my latest book, the essential, in-depth guide to performance for all .NET developers:

Writing High-Performance.NET Code by Ben Watson. Available now in print and as an eBook at:

Short vs. Long Weak References and Object Resurrection

Last time, I talked about the basics of using WeakReference, what they meant and how the CLR treats them. Today in part 2, I’ll discuss some important subtleties. Part 3 of this series can be found here.

Short vs. Long Weak References

First, there are two types of weak references in the CLR:

  • Short – Once the object is reclaimed by garbage collection, the reference is set to null. All of the examples in the previous article, with WeakReference and WeakReference<T>, were examples of short weak references.
  • Long – If the object has a finalizer AND the reference is created with the correct options, then the reference will point to the object until the finalizer completes.

Short weak references are fairly easy to understand. Once the garbage collection happens and the object has been collected, the reference gets set to null, the end. A short weak reference can only be in one of two states: alive or collected.

Using long weak references is more complicated because the object can be in one of three states:

  1. Object is still fully alive (has not been promoted or garbage collected).
  2. Object has been promoted and the finalizer has been queued to run, but has not yet run.
  3. The object has been cleaned up fully and collected.

With long weak references, you can retrieve a reference to the object during stages 1 and 2. Stage 1 is the same as with short weak references, but stage 2 is tricky. Now the object is in a possibly undefined state. Garbage collection has started, and as soon as the finalizer thread starts running pending finalizers, the object will be cleaned up. This can happen at any time, so using the object is very tricky. The weak reference to the target object remains non-null until the target object’s finalizer completes.

To create a long weak reference, use this constructor:

WeakReference<MyObject> myRefWeakLong 
    = new WeakReference<MyObject>(new MyObject(), true);

The true argument specifies that you want to track resurrection. That’s a new term and it is the whole point of long weak references.

Aside: Resurrection

First, let me say this up front: Don’t do this. You don’t need it. Don’t try it. You’ll see why. I don’t know if there is a special reason why resurrection is allowed in .NET, or it’s just a natural consequence of how garbage collection works, but there is no good reason to do something like this.

So here’s what not to do:

class Program
{
    class MyObject
    {
        ~MyObject()
        {
        myObj = this;
        }
    }

    static MyObject myObj = new MyObject();

    static void Main(string[] args)
    {
        myObj = null;
        GC.Collect();
        GC.WaitForPendingFinalizers();
    }
}

By setting the myObj reference back to an object, you are resurrecting that object. This is bad for a number of reasons:

  • You can only resurrect an object once. Because the object has already been promoted to gen 1 by the garbage collector, it has a guaranteed limited lifetime.
  • The finalizer will not run again, unless you call GC.ReRegisterForFinalize() on the object.
  • The state of the object can be indeterminate. Objects with native resources will have released those resources and they will need to be reinitialized. It can be tricky picking this apart.
  • Any objects that the resurrected object refers to will also be resurrected. If those objects have finalizers they will also have run, leaving you in a questionable state.

So why is this even possible? Some languages consider this a bug, and you should to. Some people use this technique for object pooling, but this is a particularly complex way of doing it, and there are many better ways. You should probably consider object resurrection a bug as well. If you do happen upon a legitimate use case for this, you should be able to fully justify it enough to override all of the objections here.

Weak vs. Strong vs. Finalizer Behavior

There are two dimensions for specifying a WeakReference<T>: the weak reference’s creation parameters and whether the object has a finalizer. The WeakReference’s behavior based on these is described in this table:

  No finalizer Has finalizer
trackResurrection = false short short
trackResurrection = true short long

An interesting case that isn’t explicitly specified in the documentation is when trackResurrection is false, but the object does have a finalizer. When does the WeakReference get set to null? Well, it follows the rules for short weak references and is set to null when the garbage collection happens. Yes, the object does get promoted to gen 1 and the finalizer gets put into the queue. The finalizer can even resurrect the object if it wants, but the point is that the WeakReference isn’t tracking it–because that’s what you said when you created it. WeakReference’s creation parameters do not affect how the garbage collector treats the target object, only what happens to the WeakReference.

You can see this in practice with the following code:

class MyObjectWithFinalizer 
{ 
    ~MyObjectWithFinalizer() 
    { 
        var target = myRefLong.Target as MyObjectWithFinalizer; 
        Console.WriteLine("In finalizer. target == {0}", 
            target == null ? "null" : "non-null"); 
        Console.WriteLine("~MyObjectWithFinalizer"); 
    } 
} 

static WeakReference myRefLong = 
    new WeakReference(new MyObjectWithFinalizer(), true); 

static void Main(string[] args) 
{ 
    GC.Collect(); 
    MyObjectWithFinalizer myObj2 = myRefLong.Target 
          as MyObjectWithFinalizer; 
    
    Console.WriteLine("myObj2 == {0}", 
          myObj2 == null ? "null" : "non-null"); 
    
    GC.Collect(); 
    GC.WaitForPendingFinalizers(); 
    
    myObj2 = myRefLong.Target as MyObjectWithFinalizer; 
    Console.WriteLine("myObj2 == {0}", 
         myObj2 == null ? "null" : "non-null"); 
}

The output is:

myObj2 == non-null 
In finalizer. target == non-null 
~MyObjectWithFinalizer 
myObj2 == null 

Finding Weak References in a Debugger

Windbg can show you how to find where your weak references, both short and long.

Here is some sample code to show you what’s going on:

using System; 
using System.Diagnostics; 

namespace WeakReferenceTest 
{ 
    class Program 
    { 
        class MyObject 
        { 
            ~MyObject() 
            { 
            } 
        } 

        static void Main(string[] args) 
        { 
            var strongRef = new MyObject(); 
            WeakReference<MyObject> weakRef = 
                new WeakReference<MyObject>(strongRef, trackResurrection: false); 
            strongRef = null; 

            Debugger.Break(); 

            GC.Collect(); 

            MyObject retrievedRef; 

            // Following exists to prevent the weak references themselves 
            // from being collected before the debugger breaks 
            if (weakRef.TryGetTarget(out retrievedRef)) 
            { 
                Console.WriteLine(retrievedRef); 
            } 
        } 
    } 
} 

Compile this program in Release mode.

In Windbg, do the following:

  1. Ctrl+E to execute. Browse to the compiled program and open it.
  2. Run command: sxe ld clrjit (this tells the debugger to break when the clrjit.dll file is loaded, which you need before you can execute .loadby)
  3. Run command: g
  4. Run command .loadby sos clr
  5. Run command: g
  6. The program should now break at the Debugger.Break() method.
  7. Run command !gchandles

You should output similar to this:

0:000> !gchandles
  Handle Type          Object     Size     Data Type
011112f4 WeakShort   02d324b4       12          WeakReferenceTest.Program+MyObject
011111d4 Strong      02d31d70       36          System.Security.PermissionSet
011111d8 Strong      02d31238       28          System.SharedStatics
011111dc Strong      02d311c8       84          System.Threading.ThreadAbortException
011111e0 Strong      02d31174       84          System.Threading.ThreadAbortException
011111e4 Strong      02d31120       84          System.ExecutionEngineException
011111e8 Strong      02d310cc       84          System.StackOverflowException
011111ec Strong      02d31078       84          System.OutOfMemoryException
011111f0 Strong      02d31024       84          System.Exception
011111fc Strong      02d3142c      112          System.AppDomain
011113ec Pinned      03d333a8     8176          System.Object[]
011113f0 Pinned      03d32398     4096          System.Object[]
011113f4 Pinned      03d32178      528          System.Object[]
011113f8 Pinned      02d3121c       12          System.Object
011113fc Pinned      03d31020     4424          System.Object[]

Statistics:
      MT    Count    TotalSize Class Name
70e72554        1           12 System.Object
01143814        1           12 WeakReferenceTest.Program+MyObject
70e725a8        1           28 System.SharedStatics
70e72f0c        1           36 System.Security.PermissionSet
70e724d8        1           84 System.ExecutionEngineException
70e72494        1           84 System.StackOverflowException
70e72450        1           84 System.OutOfMemoryException
70e722fc        1           84 System.Exception
70e72624        1          112 System.AppDomain
70e7251c        2          168 System.Threading.ThreadAbortException
70e35738        4        17224 System.Object[]
Total 15 objects

Handles:
    Strong Handles:       9
    Pinned Handles:       5
    Weak Short Handles:   1

The weak short reference is called a “Weak Short Handle” in this output.

Next Time

The first article explained how WeakReference works, and this one explained a few of the subtleties, including some behavior you probably don’t want to use. Next time, I’ll go into why you would want to use WeakReference in the first place, and provide a sample application.


Check out my latest book, the essential, in-depth guide to performance for all .NET developers:

Writing High-Performance.NET Code by Ben Watson. Available now in print and as an eBook at:

How To Debug GC Issues Using PerfView

Update: If you find this article useful, you can find a lot more information about garbage collection, debugging, PerfView, and .NET performance in my book Writing High-Performance .NET Code.

In my previous artlcle, I discussed 4 ways to optimize your server application for good garbage collection performance. An essential part of that process is being able to analyze your GC performance to know where to focus your efforts. One of the first tools I always turn to is a little utility that has been publically released by Microsoft.

PerfView Overview

PerfView is a stand-alone, no-install utility that can help you debug CPU and memory problems. It’s so light-weight and non-intrusive that it can be used to diagnose production applications with minimal impact.

I’ve never used it for CPU performance, so I can’t comment on that aspect of it, but that is the primary use for it (which is helpful to keep in mind when trying to grok the “quirky” UI).

PerfView collects data in two ways (as far as memory analysis is concerned):

  1. ETW tracing – This is the heart and soul of PerfView. It’s primarily an event analyzer with advanced grouping abilities to show you only the important things. If you want to know more about ETW, see this series at the ntdebugging blog.
  2. Heap dump – PerfView can dump the heap of your process and apply the same analysis and views that it does for events.

The basic view of the utility is a spreadsheet-like UI with function names and associated inclusive/exclusive costs – just like you would expect to see in a typical CPU profiler. The same paradigm is useful for memory analysis as well.

There are other views that summarize the collected events for you in easy-to-understand reports. We’ll take a quick look at all of this.

In this article, I’ll use PerfView to show you how to see the following:

  • How frequently garbage collections occur and how long they take.
  • The cause for Gen2 collections.
  • The source of large-object allocations.
  • The roots of all the memory in the heap to see who’s holding on to it.
  • A diff of the heap to see what’s changing most frequently.

Test Program

When using a new utility like this, it’s often extremely helpful to create your own test programs with known behavior to ensure that you can use the utility to see what you expect. I’ve created a very simple one, here:

class Program
{
    private static List<int[]> arrays = new List<int[]>();
    private static Random rand = new Random();

    static void Main(string[] args)
    {            
        Console.WriteLine("Press any key to exit...");
        while (!Console.KeyAvailable)
        {
            int size = rand.Next(1024, 100000);
            int[] newArray = new int[size];
            arrays.Add(newArray);
            System.Threading.Thread.Sleep(10);
        }
        Console.WriteLine("Done, exiting");
    }
}

This program “leaks” memory by continually creating arrays and storing them in a list that never gets cleared.

I also make it use server GC, to match what I discussed in the first article.

You can download the sample solution here.

Taking a Trace

When you startup PerfView, you’ll see a window like this:

image

The manual is completely integrated into the program and can be accessed using the links in the menu bar. It’s a fairly dense information dump, but you can learn quite a bit about how to really get the most of out this utility.

First, start the test program and let it run in the background until we’re done taking the trace.

In PerfView, open the Collect menu and select the Collect command. A collection dialog will appear. Don’t change any setting for the moment and just hit Start Collection.You’ll see some status indicating the size and duration of the data collected. Let it go for at least 30 seconds. Note that you don’t specify which process you’re interested in at this stage – PerfView collects events for the machine as a whole.

image

When you’re done click Stop Collection. PerfView will process the collected events for a few seconds or minutes, and then a window will pop up asking you to select a process. Just cancel this (it wants to show you a CPU profile, which we’re not interested in right now) to get back to main screen.

You’ll now see a file show up: PerfViewData.etl (unmerged). Click on the little arrow next to this and you’ll see:

image

From this, we’ll find all the data we’re interested in.

Get GC Stats (pause times and more)

The first place to start is just to get an overall picture of GC performance for your app. There is a view for just that. Double-click the GCStats report, and that will bring up a window with tables for each app. Find MemoryLeak.exe

My test run yields this summary table:

image

Every garbage collection was a generation 2 collection (that’s generally a bad thing), but at least they were fast (to be expected in such a simple program).

Reason for Gen 2 Collection

Gen 2 GCs can happen for two reasons—surviving a gen 1 collection, or allocating on the large object heap. This view will also tell us, further down, which of these is the reason:

image

The collections happened because of large object allocation. You can also see that the second GC happened about 14 seconds after the first, and the next about 32 seconds after that. There are tons of other stats in this view, so look around and see what you can divine about the program’s behavior from this.

Get Source of Large Allocations

From the main PerfView screen, open the GC Heap Alloc Stacks view and find the correct process. This shows you a list of objects which represent the tops of allocation stacks.

image

PerfView has helpfully organized all large-object allocations under the LargeObject entry. Double click this to see all such all allocations:

image

Important: If you see entries like this:

OTHER <<clr?>>

Then right-click on the list and click on Lookup Symbols. Follow the instructions to get the symbol server setup so you can see CLR and Windows function names.

From the above entry view, it’s apparent that the vast majority of large objects are arrays being allocated in Main()—exactly what we expect given our predictable leaky program.

A note on the strange column names: remember how I said this program is designed for CPU profiling? These are typical columns for showing% of CPU time in various parts of a stack, repurposed for memory analysis. Inc % is the percent of bytes allocated on this object compared to all recorded allocations, Inc is the number of bytes allocated, and Inc Ct is the number of objects allocated.

In the above example, this reads: Allocated 6589 arrays for a total of 3.9 GB, accounting for 98% of the memory allocated in the process.

By the way, these are not 100% accurate numbers. There is some sampling going on because of how the events work, but it should be fairly close in most applications.

Who’s Referencing Leaking Memory?

One of the few ways to “leak” memory in C# is to hold onto it unknowingly. By taking a heap dump, we can see the path of object references for who’s holding onto memory.

We’ll need to do a different type of collection. In the main PerfView window. Go to the Memory menu and click Take Heap Snapshot.

image

Find your process and click Dump GC Heap. This performs a live heap walk (that is, the application continues running, so it’s possible the view is slightly inconsistent—not usually an issue), sampling what it finds, and presenting the results in the same type of view as before:

image

Right away you can see that the static variable MemoryLeak.Program.arrays is holding onto 100% of memory in our application. The stack to the root isn’t that interesting in this case because all static variables are rooted directly, but if this were a member field, you would see the hierarchy of objects that are holding onto these references.

Use Two Heap Dumps to see What’s Increasing Fastest

While the test program is still running, take another heap dump, ensuring you save it to a different file. Open both dump views and in the second one, go to the Diff menu and there will be an option to use the other file as a baseline for the diff. This will bring up another window showing you the changes between the two dump files—extremely helpful for narrowing down the most likely areas for leaks.

Important: If you want to analyze the perf trace on a different computer than the one you took it on, you must tell PerfView to merge the file—this will cause all the different files it generated to be combined and symbols reconciled. Just right-click on the ETL file and select Merge. You can also optionally Zip the file (which implies a Merge).

Next Time

Next time, we’ll look at some more drastic measures for protecting yourself against expensive GCs—for when all else fails.

Resources

  • Download the sample test program here.
  • Get PerfView here.

Check out my latest book, the essential, in-depth guide to performance for all .NET developers:

Writing High-Performance.NET Code by Ben Watson. Available now in print and as an eBook at:

4 Essential Tips for High-Performance Garbage Collection on Servers

Garbage CollectionUpdate: If you find this useful, you can read a much more complete treatment of garbage collection and performance in my book Writing High-Performance .NET Code.

Update: Part 2 – How to Debug GC Issues with PerfView is now available.

On this blog, I’ve alluded to the fact that I work on high-performance server applications, most recently in .Net. Writing these in .Net is just as possible as it is in native code, but it does come with its own set of challenges. In particular, one of the biggest things you need to learn how to deal with is garbage collection.

There is a lot out there already written about the CLR’s garbage collector, so I’m not going to go over many of the details. If you need a primer on it, MSDN has some documentation:

Read that first. For the rest of this article series, I will assume that you understand how the GC basically works.

In this and future articles, I’ll cover a lot of the stuff I’ve learned to improve application performance in the face of garbage collection.

Tip 1: Use Server GC

There are two modes of garbage collection (GC): workstation and server. As long as you’re running multiple processors, you almost certainly want server mode collection. With workstation mode, a GC happens on the thread that makes the allocation that causes the GC. The collection happens at normal priority.

With server GC, a thread for every core is created just for doing GC. There is also a small object heap and a large object heap created for each GC thread. All of the program’s allocations are spread among these heaps (more on large object heaps later). When no GC is happening, these threads are blocked and do nothing. When a GC is triggered, all of the user threads get paused, and all the GC threads wake up at highest priority and do collection in parallel. All of these optimizations lead to server GC usually being much faster than workstation GC.

A word about concurrent collections: In workstation GC, concurrent collections are enabled by default. However, this only applies to generation 2 collections. Generations 0 and 1 are always blocking. However, given that it’s concurrent, that means that it will compete with your own threads that are trying to get actual work done. In a high-performance server scenario, that may not be acceptable. A better strategy is to ensure that generation 2 collections never (or extremely rarely) happen.

You enable server GC by putting this in app.config:

<configuration>
   <runtime>
      <gcServer enabled="true"/>
   </runtime>
</configuration>

 

Tip 2: Objects Live Briefly or Forever

A histogram of object lifetimes in your app should look essentially like this:

image

Object last either a vanishingly brief amount of time, or they last forever – it’s the stuff in the middle that will kill your performance.

This has everything to do with the generations of garbage collection and object survivorship. There are three generations: 0, 1, and 2. Generation 0 happens most often and is the fastest—ideally lasting only a couple of milliseconds, if that. Objects that didn’t get cleaned up in generation 0 are put into generation 1. Generation 1 collections are also very fast, usually as fast as generation 0. The problem, though, is that objects that make it to generation 1 have a fair chance of surviving this generation, and being put into generation 2.

Generation 2 is the problem. A generation 2 collection is much slower than 0 or 1—often on the order of hundreds of milliseconds or even seconds—that means your process is paused completely for that time. You do not want objects to survive to generation 2.

So how often do collections happen? There is no hard-and-fast rule: it all depends on your allocation rate, memory pressure, and patterns that you’ve trained into the GC. The GC will adapt over time, training itself on your memory usage patterns. All of this completely depends on your application and I’ll look at ways to measure all of this in a future article.

Tip 3: All Long-Lived Objects Must Be Pooled

It may be that you can’t ensure all objects for a given request are cleaned up in the first generation 0 collection that occurs. If requests are in memory longer than the time between collections, then you’re guaranteed to have survivorship.

For these types of objects, first see if you can factor them so that not all parts them have to live that long. Control object lifetime very closely and null out references once you’re done.

Once you’ve done that, hopefully there are only a handful of objects that really must last the entire length of a request. For those, create a pool of them with reinitialization semantics—effectively move them to the far end of that histogram above, where they live forever.

This works because of the adaptive nature of the garbage collector – it learns over time that if it does a collection and doesn’t free up much memory, it will schedule that generation of collection to happen less frequently. In my own case, at one point, our server had trained the GC to do a generation 2 collection less often than once per day, under a constant load. With enough work, we could probably get that to essentially never.

You may be able to get quite far without the need to implement object pooling. Or you may need to pool only a small number of objects, and the survivorship of the remaining objects is not enough to cause problematic garbage collections—only measurement and observation will tell you for sure.

Tip 4: All Large Objects Must Be Pooled

There is a way to cause an object to automatically be in generation 2: make it at least 85000 bytes in size. Anything at least that size gets put into a Large Object Heap. Only generation 2 collections service that type of heap.

Want to cause a generation 2 collection? Do this:

byte[] buffer = new byte[85000];

If you want high-performance, you absolutely cannot do this per request on a server. These types of buffers, or other large objects, must be pooled. There is no built-in pooling mechanism in .Net—you must write your own. There are usually not too many large objects you’ll need to pool: strings and byte buffers are the usual suspects, if you need to do much serialization/deserialization, but also look out for collections of any type.

If you want to know more about the Large Object Heap and why 85000 bytes is the threshold, read this great article: Large Object Heap Uncovered.

Pooling collection objects comes with its own set of challenges:

  • You can’t assume the full collection is valid (the difference between length and capacity). If you use pooled arrays, for example, you have to track the length separately, since only a small portion of the array may be valid. This can drastically affect the interfaces between components.
  • Pooled collections that can grow over time will cause your memory to rise indefinitely unless you put limits on the size of the pool and/or the size of collections within the pool.
  • Large Object Heaps are not compacted during collection, which means that you can fragment the heap such that it’s wasting a lot of memory. It all depends on your allocation and collection pattern. I may talk about heap fragmentation in another article.

Once you solve those, you’re good to go… no more generation 2 collections!

Next Time…

In my next article, I’ll cover tools you can use to measure garbage collection statistics, and how you can use that knowledge to improve your performance.


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Writing High-Performance.NET Code by Ben Watson. Available now in print and as an eBook at: