Back to Basics: Memory leaks in managed systems

Someone contacted me over my blog about his managed application where the working set goes on increasing and ultimately leads to out of memory. In the email at one point he states that he is using .NET and hence there should surely be no leaks. I have also talked with other folks in the past where they think likewise.

However, this is not really true. To get to the bottom of this first we need to understand what the the GC does. Do read up http://blogs.msdn.com/abhinaba/archive/2009/01/20/back-to-basics-why-use-garbage-collection.aspx.

In summary GC keeps track of object usage and collects/removes those that are no longer referenced/used by any other objects. It ensures that it doesn’t leave dangling pointers. You can find how it does this at http://blogs.msdn.com/abhinaba/archive/2009/01/25/back-to-basic-series-on-dynamic-memory-management.aspx

However, there is some catch to the statement above. The GC can only remove objects that are not in use. Unfortunately it’s easy to get into a situation where your code can result in objects never being completely de-referenced.

Example 1: Event Handling (discussed in more detail here).

Consider the following code

EventSink sink = new EventSink();
EventSource src = new EventSource();

src.SomeEvent += sink.EventHandler;
src.DoWork();

sink = null;
// Force collection
GC.Collect();
GC.WaitForPendingFinalizers();

In this example at the point where we are forcing a GC there is no reference to sink (explicitly via sink = null ), however, even then sink will not be collected. The reason is that sink is being used as an event handler and hence src is holding an reference to sink (so that it can callback into sink.EventHandler once the src.SomeEvent is fired) and stopping it from getting collected

Example 2: Mutating objects in collection (discussed here)

There can be even more involved cases. Around 2 years back I saw an issue where objects were being placed inside a Dictionary and later retrieved, used and discarded. Now retrieval was done using the object key. The flow was something like

1. Create Object and put it in a Dictionary
   
2. Later get object using object key
   
3. Call some functions on the object
   
4. Again get the object by key and remove it

Now the object was not immutable and in using the object in step 3 some fields of that object got modified and the same field was used for calculating the objects hash code (used in overloaded GetHashCode). This meant the Remove call in step 4 didn’t find the object and it remained inside the dictionary. Can you guess why changing a field of an object that is used in GetHashCode fails it from being retrieved from the dictionary? Check out http://blogs.msdn.com/abhinaba/archive/2007/10/18/mutable-objects-and-hashcode.aspx to know why this happens.

There are many more examples where this can happen.

So we can conclude that memory leaks is common in managed memory as well but it typically happens a bit differently where some references are not cleared as they should’ve been and the GC finds these objects referenced by others and does not collect them.

Some things I have learnt amount SW development

Working in the developer division is very exciting because I can relate so well with the customers. However, that is also an issue. You relate so well that you tend to evolve some strong opinions that can cloud your view. While working in the Visual Studio Team System team and now in the .NET Compact Framework team I have learnt some lessons. I thought I’d share some of them

I am not *the* customer (or rather the only customer)
Even though I represent the customer in different avatars (sometimes as a developer, sometimes as an office worker, sometimes just as a geek) I am not THE only customer that the product targets. When your product is going to be used by 100,000 developers/testers the average or even the predominant usage is going to be very different from what you think it will be. Sitting though an usability study is a very humbling experience. Like I believed that everyone should just be using incremental search.

Consistency is important
Geeks and bloggers tend to over-tout coolness. While cool user experience (UX) seems awesome, it is frequently overdone and what seems cool on casual usage tends to tire soon. Consistency on the other hand lets your users get on-board faster and lets them spend time doing stuff they care about and not learning things that should work automatically.

Consistency doesn’t mean that every application needs to look exactly like notepad. Expression Blend is a great example which looks refreshingly cool (appeals to the designers) and at the same time provides an experience that is consistent to other windows apps.

Learn to let go
”If there’s not something you can actively do on a project, if it’s something you can only influence and observe, then you have to learn to provide your feedback, and then let go and allow other people to fail… People don’t learn if they never make any mistakes” ~ Raymond Chen on Microspotting

Corporate software development is very different from Indie development. Large software development projects have a bunch of people/teams involved. It is not necessary that the collective opinion matches yours. At some point you need to learn to let go and do what is required. As an example I can debate on what a Debug Assert dialog should look like or do. However, there are other folks to design and think about the UX, as a developer I need to give inputs and once the call has been made it’s my job to provide the best engineering solution that implements that UX**.

 

** Do note the debug assert dialog is a fictitious example, I never worked on the IDE side.

Indic Language Input

If you have tried inputting Indian languages in Windows you know it’s a major pain. That is particularly sad because Windows comes with very good support of Indian languages. I had almost given up using my native language Bengali on a computer due to this. Even when I was creating the About Page for this blog and wanted to have a version in Bengali, I had to cut it short a lot because typing it out was so painful.

There are web-based tools like the Google Transliteration tool that works well for entering text into web-pages where they are integrated (e.g. Orkut). However, I wanted a solution that pans the desktop, so that I can use it for say writing a post using Windows Live Writer.

Enter the Microsoft Indic Language Input tool. Head over to the link and install the desktop version. You can install the various languages individually (currently Bengali, Hindi, Kannada, Malayalam, Tamil and Telugu is supported). I personally installed the Bengali and Hindi versions.

Since I am on Windows 7 which comes pre-installed with Complex language support I needn’t do anything special. However, on older OS like XP you need to do some extra steps which are available through the Getting Started link on that page.

Once you are setup you can keep the Windows Language Bar floating on the desktop. The tool extends the language bar to allow you to enter Indic languages using an English keyboard via transliteration.

Go to the application where you want to enter Indic language and then switch to Bengali (or any of the other 6 supported Indic language) using this language bar. Start typing বেঙ্গলি using English keyboard and the tool will transliterates. The moment you’d hit a word terminator like space it inserts the Bengali word.

I tried some difficult words like কিংকর্তববিমূঢ় and it worked amazingly well

I had a very good experience with the tool. The only issue I faced was that the tool was extremely slow with some WPF apps like Seesmic twitter client. However, I got to know from the dev team that they are aware of the issue (it’s for some specific WPF apps and not WPF in general). I hope they fix it before they RTM (the tool is in Beta).

Tip: You can hit alt+shift to cycle the various languages in the toolbar without having to use your mouse (which is handy if you typing using a mix of languages).

NETCF: Count your bytes correctly

I got a stress related issue reported in which the code tried allocating a 5MB array and do some processing on it but that failed with OOM (Out of Memory). It also stated that there was way more than 5 MB available on the device and surely it’s some bug in the Execution Engine.

Here’s the code.

try{
    byte[] result;
    long GlobalFileSize = 5242660;  //5MB
    result = new byte[GlobalFileSize];
    string payload = Encoding.UTF8.GetString(result, 0, result.Length);
    System.Console.WriteLine("len " + payload.Length);
}
catch (Exception e)
{
    System.Console.WriteLine("Exception " + e); 
} 

The problem is that the user didn’t count his bytes well. The array is 5MB and it actually gets allocated correctly. The problem is with the memory complexity of the UTF8.GetString which allocates further memory based on it’s input. In this particular case the allocation pattern goes something like

  5MB  -- Byte Array allocation (as expected and works)

  5MB  -- Used by GetString call
 10MB  -- Used by GetString call
  5MB  -- Used by GetString call
 10MB  -- Used by GetString call

So GetString needed a whooping 30MB and the total allocation is around 35MB which was really not available.

Morale of the story: Count your bytes well, preferably through a tool like Remote Performance Monitor

Silverlight: Where are my colors

 

The Silverlight System.Windows.Media.Colors class is a trimmer counterpart of the WPF Colors class. E.g. Colors.AliceBlue is available in WPF but not present in Silverlight. However, these standard colors are indeed available in Silverlight XAML.

In effect in Silverlight XAML you can use

<Border Background="AliceBlue" BorderBrush="Coral" BorderThickness="2" CornerRadius="10" >

However, if you try that in code it fails to compile

foo.Background = new SolidColorBrush(Colors.Coral);

This means if you ever need to use standard colors in Silverlight code you have to use RGB values, which for Coral would be something like

foo.Background = new SolidColorBrush(Color.FromArgb(255,255,127,80));

This is kind of painful. Moreover, you need to frequently be able to convert between HTML color, RGB and XAML standard color names when you are developing an Silverlight application. To make the job easier I hacked up a small Silverlight 2 app at http://www.bonggeek.com/Ag/Colors/. You can do the following with this

1. Change colors with sliders, RGB, HTML, XAML standard color names to see what the color really looks like
   
2. All the format is kept in sync, this means if you change the sliders to get to the color Salmon then the RGB value and HTML will be updated and the drop down will switch to show the selected colors name
   
3. You can easily create variants of standard color. E.g. select Salmon using the drop down and then push the sliders to get to a slightly lighter/darker shade of the color

Enjoy and extend as you please. Sources are available here.

Global variables are bad

<This post is about C++ (C# folks are saved from this pain)>

One of our devs taking care of NETCF on Nokia S60 reported that after the latest code reached their branch things are working very slow. It was observed that Garbage Collector is getting kicked in way more than it should. Investigation showed something interesting.

I added a global var gcConfig which had a fairly complex constructor and that among other things sets the various member variables like the GC trigger threshold to default value (1mb). All of these works fine on Windows variants.

However, TCPL states “It is generally best to minimize the use of global variables and in particular to limit the use of global variables requiring complicated initialization.”. This is especially true for dlls. We tend to ignore good advice sometimes :)

For Symbian OS (S60) on device complex ctors of global objects are not run (without any warning). The members are all set to 0 (default member initialization). So in the gcConfig GC-trigger was being set to 0 instead of 1mb. The allocation byte count is compared against this value to figure out when it’s time to start a GC. Since it was 0 the collection condition is being met for every allocation and hence for every allocation request GC was being fired!!!

Actually considering that even after that the app was actually running shows that we have pretty good perf ;)

A good alternative of using global vars is as follows

MyClass& useMC()
{
    static MyClass mc; // static ensures objects are not created on each call
    return mc;
}

MyClass& mc = useMC();

Do note that this has some multi-threading issue. See http://blogs.msdn.com/oldnewthing/archive/2004/03/08/85901.aspx.

.NET Compact Framework BCL < .NET BCL

Over twitter some folks are regularly discussing about the fact that there are some chunks of desktop .NET Base class library (BCL) that are missing on the .NET Compact Framework (.NETCF). So I thought I’d post the rationale behind things that are missing and what criteria is used to figure out what makes it in.

First of all, .NET and .NETCF use completely different runtimes. So the BCL code doesn’t work as is. They need to be ported over. Something being available on the desktop reduces the effort of our BCL team but still significant cost is involved in making it work over NETCF. This means that its not as if everything is available on .NETCF BCL and we cut things out (elimination process), but the other way round where we start from 0 and we need to figure out whether we can take something in. In that process we use some of the following rationale.

1. ROI: Does the user scenario that it enables on a mobile device justify the cost
   System.CodeDom.Compiler. Yea right you expected to compile on the phone didn’t you :)
   
2. Easy workaround available: If there is an acceptable workaround for a given API then it drops in priority. A trivial example could be that we ship some method overloads but not the other. E.g. Instead of shipping all overloads of Enum.Parse we drop Enum.Parse(Type, String) and keep only Enum.Parse(Type, String, Bool).
   This applies at the level of namespace or Types as well.
   
3. Lower in priority list: It needs work to get it done and it’s lower in priority than other things that is keeping the engineering team busy.
   If there are a lot of request for these features and not good/performant workarounds available then it will go up the priority list and make it to future version of NETCF
   
4. Too large to fit: Simply too large to fit into our memory limitations
   E.g. Reflection.Emit which leads to other things missing like Dynamic Language Runtime, Linq-to-SQL
   
5. No native support: It uses some underlying OS feature which is either not present on devices or is not relevant to it
   WPF, Parallel computing support

With every release developers ask for new features and we also negotiate to increase NETCF footprint budget so that we can include some (but not all) from those requests. To choose what makes it in we use some of the criteria mentioned above.

Given the system constraints under which NETCF works a vast majority of the desktop APIs will continue to be missing from NETCF. Hopefully this gives you some context behind why those are missing. If you approach NETCF simply from trying to port a desktop application then you would face some frustration on the missing surface area.

BTW: Do post your comments on this blog or on twitter (use the #netcf hashtag).

<Update: I made some updates to this based on feedback />

NETCF: GC and thread blocking

One of our customers asked us a bunch of questions on the NETCF garbage collector that qualifies as FAQ and hence I thought I’d put them up here.

Question: What is the priority of the GC thread
Answer: Unlike some variants of the desktop GC and other virtual machines (e.g. JScript) we do not have any background or timer based GC. The CLR fires GC on the same thread that tried allocation and either the allocation failed or during pre-allocation checks the CLR feels that time to run GC has come. So the priority of the GC thread is same as that of the thread that resulted in GC.

Question: Can the GC freeze managed threads that are of higher priority than the GC thread?
Answer: Yes GC can freeze managed threads which has higher priority than itself. Before actually running GC the CLR tries to go into a “safe point”. Each thread has a suspend event associated with it and this event is checked by each thread regularly. Before starting GC the CLR enumerates all managed threads and in each of them sets this event. In the next point when the thread checks and finds this event set, it blocks waiting for the event to get reset (which happens when GC is complete). This mechanism is agnostic of the relative priority of the various threads.

Question: Does it freeze all threads or only Managed threads?
Answer: The NETCF GC belongs to the category “stop-the-world GC”. It needs to freeze threads so that when it is iterating and compacting managed heap nothing on it gets modified. This is unlike some background/incremental GC supported by other virtual machines.

GC freezes only managed threads and not other native threads (e.g. COM or host-threads). However, there are two exclusions even on the managed threads

1. It doesn’t freeze the managed thread that started GC because the GC will be run on that thread (see answer to first question)
2. Managed threads that are currently executing in native p/invoke code is not frozen either. However, the moment they try to return to managed execution they get frozen. (I actually missed this subtle point in my response and Brian caught it :))

Hope this answers some of your questions. Feel free to send me any .NET Compact Framework CLR questions. I will either answer them myself or get someone else to do it for you :)

I am the Empire

5 years back exactly on this very day I walked into Cyber Towers office of Microsoft India for the first time. If someone asked me at that time how long I plan to work for Microsoft, my answer would’ve been couple of years max. I was still a bit suspicious of the evil empire and it took some time for me to realize that “I am the empire”. The 5 years has been a mind blowing journey.

Even though in our industry working for any company for a 5 year stretch is a huge thing, it doesn’t seem so in Microsoft. Our team has 4 people over 20 years and at least 8 people over 10 years.

Some random thoughts on the last 5 years

1. Made my family relocate 3 cities before settling down in Hyderabad (my wife’s threat that the next relocation would be without her helped).
2. Bought an apartment
3. I have a daughter now who was born around 3 months after I joined MS.
4. Started working in a shared office with Ankur and then worked in various cubicles. Finally I have an office.
5. 3 job designations. SDE to SDEII to Senior SDE. I am fiercely protecting the SDE suffix.
6. It’s fantastic to walk into a book store and see books on your work and see screenshots of UI you’ve designed or description of algorithms you’ve come up with.
7. 327 blog posts on blogs.msdn.com
8. I still do not use Visual Studio to edit code
9. Finally gave up on Linux even at home. Bye bye Ubuntu, welcome Windows 7
10. I have 3 Xboxes in my office and none at home
11. I use Nokia phones
12. Made fantastic friends at work and sadly few of them are left with Microsoft India (either moved to Redmond or have left MS). Hello Amit, Srivatsn, Ankur :)
13. Always wanted to attend a BillG project review but he quit
14. Got into some email wars
15. I am no longer the fire-head I used to be. I actually think before I speak.
16. Went top down from n-tier architectures (TFS), desktop applications (VSTT) and now embedded (NETCF)
17. I prefer Starbucks coffee now instead of Darjeeling tea

How does the .NET Compact Memory allocator work

As I mentioned in one of my previous posts the .NET Compact Framework uses 5 separate heaps of which one is dedicated for managed data and is called the managed heap. The .NETCF allocator, allocates pools of data from this managed heap and then sub-allocates managed objects from this pool.

This is how the process goes

	At the very beginning the allocator allocates a 64Kb pool (called the obj-pool) and ties it with the current app-domain state (AppState).
	All object allocation requests are served from this pool (Pool 0). After each allocation an internal pointer (shown in yellow) is incremented to point to the end of the last allocated object.
	Subsequent allocation is just incrementing this pointer.
	This goes on until the point where there is no more space left in the current obj-pool
	Since there is no more space left in the current pool (pool 0) a new pool (pool 1) is allocated and all subsequent object allocation requests are serviced from the new pool.

The first question I get asked at this point is what happens when one object itself is bigger than 64Kb :). The answer is that the CLR considers such objects as “huge-objects” and they are placed in their own obj-pool. This essentially means that either obj-pools are 64Kb in size and has more than one object in them or are bigger than 64Kb and has only one huge-object in it.

Object allocation speed varies a lot between whether it is being allocated from the current pool (where it is just the cost of a pointer increment) or it needs a new pool to be allocated (where there is additional cost of a 64Kb allocation). On the average managed objects are 35 bytes in size and around 1800 can fit in one pool and hence on the whole allocation speed attained is pretty good.

Verifying what we discussed above

All of the things I said above can be easily verified using the Remote Performance monitor. I wrote a small application that allocates objects in a loop and launched it under Remote Performance monitor and published the data to perfmon so that I can observe how the managed heap grows. I used the process outlined in Steven Pratschner’s blog post at http://blogs.msdn.com/stevenpr/archive/2006/04/17/577636.aspx 

Using the above mentioned mechanism I am observing the managed bytes allocated (green line) and the GC Heap (red line). You can see that the green line increases linearly and after every 64Kb the red line or the GC Heap bumps up by 64Kb as we allocate a new object pool from the heap.

Memory leak via event handlers

One of our customers recently had some interesting out-of-memory issues which I thought I’d share.

The short version

The basic problem was that they had event sinks and sources. The event sinks were disposed correctly. However, in the dispose they missed removing the event sink from the event invoker list of the source. So in effect the disposed sinks were still reachable from the event source which is still in use. So GC did not de-allocate the event sinks and lead to leaked objects.

Now the not-so-long version

Consider the following code where there are EventSource and EventSink objects. EventSource exposes the event SomeEvent which the sink listens to.

EventSink sink = new EventSink();
EventSource src = new EventSource();

src.SomeEvent += sink.EventHandler;
src.DoWork();

sink = null;
// Force collection
GC.Collect();
GC.WaitForPendingFinalizers();

For the above code if you have a finalizer for EventSink and add a breakpoint/Console.WriteLine in it, you’d see that it is never hit even though sink is clearly set to null. The reason is the 3rd line where we added sink to the invoke list of source via the += operator. So even after sink being set to null the original object is still reachable (and hence not garbage) from src.

+= is additive so as the code executes and new sinks are added the older objects still stick around resulting in working set to grow and finally lead to crash at some point.

The fix is to ensure you remove the sink with –= as in

EventSink sink = new EventSink();
EventSource src = new EventSource();
src.SomeEvent += sink.EventHandler;
src.DoWork();
src.SomeEvent -= sink.EventHandler;
sink = null;

The above code is just sample and obviously you shouldn’t do event add removal in a scattered way. Make EventSink implement IDisposable and encapsulate the += –= in ctor/dispose.

Finalizers and Thread local storage

Writing finalizers is generally tricky. In the msdn documentation for finalizers the following limitations are mentioned.

1. The exact time when the finalizer executes during garbage collection is undefined
2. The finalizers of two objects are not guaranteed to run in any specific order
3. The thread on which the finalizer is run is unspecified.
4. Finalizers might not be run at all

#3 has interesting consequences. If a native resources is allocated by a managed ctor (or any other method) and the finalizers is used to de-allocate that then the allocation and de-allocation will not happen on the same thread. The reason being that the CLR uses one (maybe more than one) special thread to run all finalizers on. This is easily verified by the fact that if you query for the thread id inside the finalizers you will get a different id than the main thread the application is being run on.

The thread safety is generally easy to handle but might lead to some tricky and hard to locate problems. Consider the following code

class MyClass
{
    public MyClass()
    {
        tls = 42;
        Console.WriteLine("Ctor threadid = {0} TLS={1}", AppDomain.GetCurrentThreadId(), tls);
    }

    ~MyClass()
    {
        Console.WriteLine("Finalizer threadid = {0} TLS={1}", AppDomain.GetCurrentThreadId(), tls);
    }

    public void DoWork()
    {
        Console.WriteLine("DoWork threadid = {0} TLS={1}", AppDomain.GetCurrentThreadId(), tls);
    }

    [ThreadStatic]
    static int tls;
}

class Program
{
    static void Main(string[] args)
    {
        MyClass mc = new MyClass();
        mc.DoWork();
        mc = null;
        GC.Collect();
        GC.WaitForPendingFinalizers();
    }
}

Here we create a class, use it and finalize it. In all of these 3 methods we print the thread id and also use a special variable named tls.

If you see the tls is marked with the attribute ThreadStatic. The definition of ThreadStatic is “Indicates that the value of a static field is unique for each thread”.

I hope by now you have figured out the gotcha :). Under the hood the CLR uses a native OS concept called Thread Local Usage (TLS) to ensure that the value of tls is unique per thread. TLS uses special per thread data-structure to store that data. Now we have set the value in the ctor and used it in finalizer. Since they run on different threads each will get different values of the same field.

On my system the out put is as follows

Ctor threadid = 5904 TLS=42
DoWork threadid = 5904 TLS=42
Finalizer threadid = 4220 TLS=0
Press any key to continue . . .

As is evident the finalizer ran on a different thread (id is different) and the TLS value is also different from what was set.

So the moral of this story is “Be careful about thread safety of finalizers and do not use thread local storage in it”

What post are you known for?

I was just listening to Scot Hanselman’s podcast where he interviews Joel. They talk about how most bloggers get known for one post. E.g. Joel is known for “The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!)”.

Now obviously I’m not a famous blogger and I’m not known for any post but the fact that my most read post is “How many (.NET) types are loaded for Hello World” makes me feel a bit weird. IMO that is one of the least useful posts I’ve ever written.

.NET Code Pitching

The word pitch can have many meanings, but in case of the code pitching it is used in the sense of “to throw away”.

The desktop CLR never throws away or pitches native code that it JITs. However, the .NET Compact Framework CLR supports code pitching due to the following reasons

1. It is targeted towards embedded/mobile systems and therefore needs to be more sensitive towards memory usage. So in some situations it throws away JITed code to free up memory.
2. NETCF runs on RISC processors like ARM where code density is lower than that of x86. This means the JITed native code is larger in size for a given set of IL on say ARM vs that on say x86. Due to this the memory usage overhead is a bit aggravated. However, this isn’t really a primary motivator and there are other work around like using the newer ARM instruction set extensions.

Code pitching can happen due to various reasons like (note this is not an exhaustive list)

1. When the managed application gets WM_HIBERNATE message it fires a round of garbage collection and also pitches code
2. When the JITer fails to allocate memory it attempts code pitching to free up memory and re-tries allocation
3. Other native resource allocation failures also initiates pitching

Obviously code pitching has performance implications and can result in the same method being pitched multiple times. You can monitor code pitching in Remote Performance Monitor.

As the name suggests the GC does drive code pitching but they are not essentially related. E.g. if user code forces GC by System.GC.Collect() code pitching is not done. Code pitching is primarily driven by real low memory scenarios.

Caveat: While pitching the CLR ensures that it doesn’t throw away any JITed method on the current managed execution stack. No points for guessing why that is important.

Multiple calls to GC.ReRegisterForFinalize

What happens when there are multiple calls to GC.ReRegisterForFinalize for the same object?

Consider the following C# code

class MyClass
{
    ~MyClass()
    {
        Console.WriteLine("~MyClass");
    }
}

class Program
{
    static void Main(string[] args)
    {
        MyClass mc = new MyClass();
        GC.ReRegisterForFinalize(mc); // 1
        GC.ReRegisterForFinalize(mc); // 2
        GC.ReRegisterForFinalize(mc); // 3

        GC.Collect();
        GC.WaitForPendingFinalizers();
    }
}

Here for the same object we have called GC.ReRegisterForFinalize thrice.

This is one of the few cases where the behavior of .NET and .NET Compact Framework differs.

In case of .NET the MyClass finalizer is called 4 times. Since the object has a finalizer, it is anyway finalized once and for the additional 3 calls to re-register it is finalized 3 more times.

In case of .NET Compact Framework the finalizer is called only once. The reason being internally finalizable is just a bool flag. Each time GC.SuppressFinalize is called it is set to false and each time GC.ReRegisterForFinalize is called it is set to true. So multiple calls doesn’t have any effect.

Hopefully this is one piece of information which none of my readers ever find useful, but since I got a bug assigned for the difference in behavior I might as well let folks know.

.NET Compact Framework MemMaker

“Glen recently discovered that by keeping his application’s EXE empty and putting all the forms, code, resources, and data in managed DLLs, he reduced the amount of virtual memory his app uses inside its slot while at the same time taking advantage of memory outside the slot in the 1 GB shared memory area”

Read more about this very interesting finding at Rob Tiffany’s blog at http://blogs.msdn.com/robtiffany/archive/2009/04/09/memmaker-for-the-net-compact-framework.aspx

Object Resurrection using GC.ReRegisterForFinalize

I have been thinking about writing this post for some time, but Easter weekend provided the right context to do so.

When I first encountered GC.ReRegisterForFinalize I was a bit baffled. The context help “Requests that the system call the finalizer for the specified object for which System.GC.SuppressFinalize(System.Object) has previously been called.” didn’t provide much clue as to why I would want to finalize an object more than once.

This is when I found out about object resurrection.

res·ur·rec·tion   (rěz'ə-rěk'shən)
-noun

1. The act of rising from the dead or returning to life.
2. The state of one who has returned to life.
3. The act of bringing back to practice, notice, or use; revival.

This definition made perfect sense. The basic idea of using object resurrection is to handle scenarios where an object creation is very expensive for some reason (e.g. it makes system calls that take a lot of time or consumes a lot of resources). To avoid creating new objects and incur the creation cost again, you’d try to re-cycle older objects which have already done their job or in other words resurrect dead objects.

This can be done by using the following facts

1. If an object is garbage (not-reachable) and has a finalizer, it is not collected in the first pass of the GC but is placed in the finalizer queue
2. The finalizer thread calls the finalizers of each object in the finalizer queue and removes it from the queue.
3. The next pass of the GC actually reclaims the object (as in de-allocate the memory)

In #2 above when the finalizer is executing if a new reference to the object is created and GC.ReRegisterForFinalize is called then the object becomes reachable and hence non garbage and so in #3 the GC will not reclaim it. Since GC.ReRegisterForFinalize is called, in the next cycle when the same object is available for collection #1 through #3 will again follow (so we set up the cycle).

Consider the scenario where MyClass is a class which is expensive to create. We want to setup a pool of these objects so that we can just pick up objects from this pool and once the object is done with, it is automatically put into that pool.

class MyClass
{
    /// <summary>
    /// Expensive class
    /// </summary>
    /// <param name="pool" />Pool from which the objects are picked up</param>
    public MyClass(List pool)
    {
        Console.WriteLine("Expensive MyClass::ctor");
        this.pool = pool; // Store the pool so we can use it later 
    }

    public void DoWork()
    {
        Console.WriteLine("{0} Did some work", this.resurrectionCount);
    }

    /// <summary>
    /// Finalizer method
    /// </summary>
    ~MyClass()
    {
        resurrectionCount++;
        // automatically return to the pool, makes this object reachable as well
        pool.Add(this); 

        // Ensure next time this same finalizer is called again
        GC.ReRegisterForFinalize(this);
    }

    private List<MyClass> pool;
    private int resurrectionCount = 0;
}

// Create pool and add a bunch of these objects to the pool
List pool = new List();
pool.Add(new MyClass(pool));
pool.Add(new MyClass(pool));

// Client code
MyClass cl = pool[0]; // get the object at the head of the pool
pool.RemoveAt(0);     // remove the object
cl.DoWork();          // start using it. Once done it will automatically go back to pool

At the start a bunch of these objects are created and put in this list of objects (taking a hit in startup time). Then as and when required they are removed from this list and put to work. Once the work is done they automatically get en-queued back to the pool as the finalizer call will ensure that.

Don’t do this

Even though the above mechanism seems pretty tempting, it is actually a very bad idea to really use this method. The major reason being that the CLR never guarantees when GC is run and post that when the finalizer will be run. So in effect even though a bunch of these objects have done their job it will be not be deterministic on when they will land up back in the queue.

If you really need to use object pooling a much better approach is to implement your special interface where you provide a method which is explicitly called by client code and is therefore deterministically controllable.

Technical Presentation Tip

I’m no Scot Hanselman, but even then I guess I can share at least one tip for delivering technical presentation that has worked very well for me.

At the very beginning of your presentation always have a slide clearly calling out what the attendees will “Take Away” and what is the “Pre-requisites'” of attending your presentation. Presentation titles sometime mislead and is generally not enough to indicate what a presentation is all about.

Benefits of calling out key take-away

1. Helps you focus on what you want to convey and you can take feedback from the attendees on whether you met the expectation that you set.
2. If an attendee feels he already knows about the topic or is not really interested for some other reason he can leave at the very beginning. No point wasting anyone’s time.
3. You can direct discussion/questions. If someone tries venturing into an area you intentionally don’t want to cover you can get the discussion onto the right track. When I give my GC Theory talk I call out very clearly that the presentation is not about how either .NET or .NETCF implements GC and inevitably when someone ventures into some .NET implementation detail and I use this to get back to the main course.

Benefits of calling out pre-requisite

1. Just like you need to ensure you are not wasting the attendees time by calling out take aways, you should ensure that you are not wasting your time trying to explain garbage collection to device driver programmers :)
2. Explaining basic stuff to one attendee (who raises them) and wasting the time of all other attendees who already know the answer is not a good use of anyone’s time. _ASSERT on the pre-reqs and ensure that you are clear about what baseline knowledge you are expecting out of all the attendees.

You can learn more real presentation tips from Scot’s post and maybe then one day you’ll earn a feedback comment “The presentation was superb. I think this is the first presentation for which I was completely awake” and try to figure out who that one was ;)

Floating point operations in .NET Compact Framework on WinCE+ARM

There has been a some confusion on how .NETCF handles floating point operations. The major reason for this confusion is due to the fact that the answer differs across the platforms NETCF supports (e.g. S60/Xbox/Zune/WinCE). I made a post on this topic which is partially incorrect. As I followed that up I learnt a lot, especially from Brian Smith. Hopefully this post removes all the confusions floating around floating point handling in .NETCF on WinCE.

How does desktop CLR handle floating point operation

Consider the following floating point addition in C#

float a = 0.7F;
float b = 0.6F;
float c = a + b;

For this code the final assembly generated by CLR JITer on x86 platform is

            float a = 0.7F;
0000003f  mov         dword ptr [ebp-40h],3F333333h 
            float b = 0.6F;
00000046  mov         dword ptr [ebp-44h],3F19999Ah 
            float c = a + b;
0000004d  fld         dword ptr [ebp-40h] 
00000050  fadd        dword ptr [ebp-44h] 
00000053  fstp        dword ptr [ebp-48h] 

Here the JITter directly emits floating point instructions fld, fadd and fstp. It could do so because floating point unit and hence the floating point instructions are always available on x86.

Why does NETCF differ

Unfortunately NETCF targets a huge number of HW configs which vary across Xbox, Zune, WinCE on x86/MIPS/ARM/SH4, S60, etc.  There are even sub-flavors of these base configs (e.g. ARMv4, ARMv6, MIPS II, MIPS IV). On all of these platforms floating point unit (FPU) is not available.

This difference in FPU availability is taken care of by using different approaches on different platforms. This post is for WinCE+ARM and hence I’ll skip the other platforms.

Zune in a special WinCE platform

Zune is special because it is tied to a specific version of ARM with FPU built in (locked HW). NETCF on Zune was primarily targeted for XNA games and on games performance of floating point operation is critical. Hence the .NETCF JITer was updated to target ARM FPU. So for basic mathematical operation it emits inline native ARM FPU instructions very much like the desktop JITter shown above. The result is that the basic floating point operations are much faster.

WinCE in general

In general for WinCE on ARM, presence of FPU cannot be assumed because the least common subset targeted is ARMv4 which doesn’t essentially have a FPU.

To understand the implication it is important to understand that floating point operations can be basically classified into two categories:

1. BCL System.Math operations like sin/cos/tan
   
2. Simple floating point operations like +,/,- , *, conversion, comparison.

For the first category the JITer simply delegates the operation into WinCE by calling into CoreDll.dll, e.g. the sin, sinh, cos, cosh, etc.. available in CoreDll.dll.

For the second category the JITer calls into small worker functions implemented inside the NETCF CLR. These worker functions are native code and compiled for ARM. If we disassemble them we would see that for these the native ARM compiler emits calls into coredll.dll into say __imp___addd

It is evident from above that the performance of managed floating point operation is heavily dependent on whether the underlying WinCE uses the ARM FPU as in most scenarios floating point operations are finally delegated into it.

The whole thing can be summarized in the following table (courtesy Brian Smith)

	WinCE6 + ARMv4i		WinCE6 + ARMv6_FP
	#1 CE is NOT FPU optimized	#2 CE is FPU optimized	#3 CE is FPU optimized and so is NETCF (e.g. Zune JIT)
System.Math library calls	Delegated via pinvoke, emulated within CE (speed: slow)	Delegated via pinvoke, FPU instructions within CE (speed: medium)	Delegated via pinvoke, FPU instructions within CE (speed: medium)
FP IL opcodes (add, etc)	Delegated via JIT worker, emulated within CE (speed: slow)	Delegated via JIT worker, FPU instructions within CE (speed: medium)	FPU instructions inlined by JITed code (speed: fast)

#1 is the general case for NETCF + WinCE + ARM. #3 is the current scenario of NETCF + Zune + ARM.

#2 is based on the fact that WinCE 6.0 supports “pluggable” FP library. However, the NETCF team has not tried out this flavor and hence does not give any guidance on whether plugging in a FP library in WinCE will really have any performance improvement, however theoretically it does seems likely.

#3 today is only for Zune, but going forward it does seem likely that newer versions of WinCE will update it’s base supported HW spec, it will include FPU and then this feature will also make it to base NETCF for WinCE.

NETCF: Total Bytes in Use After GC – Perf counter

At least one of our customers were a bit confused with the .NET Compact Framework performance counter “Total Bytes in Use After GC” which is described as “The number of bytes of memory, native and managed, in use after the last garbage collection.”. The description lead him to believe it is the total memory in use by a given managed application. That is not true.

The memory inside a .NET Compact Framework managed application can be broadly classified into

1. Managed memory
2. Jitted code
3. Other native memory allocated by the CLR
4. Native memory allocated by other sub-systems like say a COM module or some other native modules which the managed application P/Invokes into.

The first three is managed by .NET Compact Framework by using 5 heaps. The counter mentioned above returns the sum of all bytes allocated in all of the first 3 categories. .NETCF doesn’t keep tab of memory allocated in category #4.

How many heaps does the .NET Compact framework use

While discussing the memory architecture with an internal customer, he inquired about how many heaps .NETCF creates. I’m not sure how it might be helpful to users, but the answer is 5. This has been touched upon in some blogs and presentations (e.g. MEDC 2005) but I thought I’d put up a handy list.

Heap	Description
JIT heap	This is the heap where the buffers of jitted code is placed. Unlike the desktop CLR under some circumstances the CLR does free jitted code by a processed named code-pitching (which I will cover in a later blog post)
GC heap	This is where all the managed objects resides. The garbage collector is responsible for managing all objects on this heap including compacting it when it gets fragmented.
Short-term heap	This is used for short-lived objects. E.g. while doing a number conversion for the System.Convert methods if temporary space is required by the underlying CLR native code then it is allocated from this heap only to be freed soon.
App-domain	This is used by the CLR to place things like assembly meta-data, application/appdomain/thread state information.
Process heap	This is the default heap where rest of the stuff goes in

.NET Compact Framework and ARM FPU

One of our customers was porting an application on to .NETCF from native code and observed that on ARM v7 the floating point performance of .NET CF was way below that of native code. In particular he was trying out sine function. Their micro-benchmark showed that doing sine over couple of million times was 27 times slower in .NETCF as compared to native code.

The reason behind this is that native code targets the Floating Point Unit (FPU) when available. However, .NETCF targets the lowest common denominator of ARMV4i and doesn’t use FPU even when run on a higher ARM version where FPU is available. It actually uses SW emulation for floating point operations and hence is slower.

If floating point math performance is critical for your application then the way to address that would be to write the math operation in native code (a native dll) and P/Invoke into it.

However, the cost of making P/Invoke calls is high (~5-6x slower than normal method calls) and you need to ensure that it doesn’t offset the savings in using the FPU. A common way to do that is to use bulking and reduce chattiness of P/Invoke calls. E.g., if you are evaluating an expression of the form func(a) + func(b) + func(c) instead of creating a P/Invokable function func and calling it thrice, its best to put the whole expression inside the native dll and call that one function which does the whole operation. If it is possible you can even try to go one step ahead and pass a buffer of input and output and get the native dll to do all the operation and return the processed buffer in one go.

Trivia: The .NET Compact Framework that runs on the Zune (which uses ARM as well) does use the FPU on it. So we do have the support in code and hopefully at some point in the future it will make it to the main-stream .NET Compact Framework.

Random GC fun

A friend came by and asked “Dude, are you free”. I replied back cautiously “Why? Are you the Garbage Collector?”. I do not even implement a finalizer and hence wouldn’t even get some extra time standing in the finalizer queue. So I’m not taking any chance…

Dangers of large object heap

Andrew Hunter has an interesting post on the issues with large object heap on the desktop CLR. Visit http://www.simple-talk.com/dotnet/.net-framework/the-dangers-of-the-large-object-heap/

Side Note: .NET Compact Framework doesn’t support large object heap simply because it is primarily targeted towards embedded applications which has far lesser memory requirements.

Small Basic

Today my father in law started teaching my 4 year old daughter how to write in Bengali which is her native language. Below is the screen shot of her attempt to write অ আ ই, the first character is written by her teacher and the rest by her.

This got me thinking around when the time comes how do I introduce her to programming languages. A friend suggested I check out Small Basic which is from Microsoft Dev Labs and is a free download.

I downloaded the Getting Started Guide and skimmed though it for around 5 mins. Then fired Small Basic IDE up and instantly loved the UI, it had KISS written all over it. However, I did feel that it made extra effort to look too cute.

I could easily code up the following by skimming through the guide. The only hiccup was figuring out how to get a color from RGB as I didn’t guess that the method would be inside GraphicsWindow.

GraphicsWindow.BackgroundColor = "Black"
GraphicsWindow.Width = 400
GraphicsWindow.Height = 256

For y = 0 To 255
  GraphicsWindow.PenColor = GraphicsWindow.GetColorFromRGB(y, 0, 0)
  GraphicsWindow.DrawLine(0, y, 400, y)
EndFor

GraphicsWindow.BrushColor = "White"
GraphicsWindow.FontSize = 24
GraphicsWindow.DrawText(10, 110, "Gradient")

The output is the following cool looking gradient effect

The whole language and the pre-packaged classes were very immersive. I really started having fun and could code up a small logo like application in around 10 mins. I could just use the following code (which is a sample from the getting started guide)

GraphicsWindow.BackgroundColor = "Black"
GraphicsWindow.MouseDown = OnMouseDown

' Event handler
Sub OnMouseDown
  ' Get a random image tagged  flower from flickr
  pic = Flickr.GetRandomPicture("Flower")
  GraphicsWindow.DrawResizedImage(pic, 0, 0, 640, 480)
EndSub

to create a UI where every click gets a random image from flickr

I just loved the whole experience, almost felt like Pop-fly, only way more powerful. I think Small Basic has a lot of potential especially after seeing the samples of what folks have created over at http://blogs.msdn.com/smallbasic/

Back To Basics: How does the GC find object references

This post is Part 9 in the series of posts on Garbage Collection (GC). Please see the index here.

Most garbage collection algorithms needs to know all other objects referenced by a given object to correctly traverse object hierarchy. All of reference counting, mark-sweep and even coping collectors need it. Consider the mark algorithm that I used in mark-sweep GC

void Mark(Object* pObj)
{
    if (!Marked(pObj)) // Marked returns the marked flag from object header
    {
        MarkBit(pObj); // Marks the flag in obj header
        // Get list of references that the current object has
        // and recursively mark them as well
        ObjectCollection children = pObj->GetChildren();
        for(int i = 0; i < children.Count(); ++i)
        {
            Mark(children[i]); // recursively call mark
        }
    }
}

Here a critical piece is the line in red. Given the object header we need to be able to locate all other objects referenced by it (or are children of it in object reference graph). There are two basic ways to do this

Conservative GC

In case a GC is being plugged into an existing system in the form of a library and there is no easy way to modify the runtime then a conservative (as opposed to precise) system is used. A conservative GC considers every bit pattern on the heap to be a valid reference to another heap object. For the above case given a object header the GC needs to have the following basic information

1. The size of the object
   
2. Alignment of pointers (lets assume it to be 32bit for our case)

So once the GC gets a object pointer pObj it considers all 32bit patterns in between pObj and (pObj + sizeof(Object)) to be references to other child objects. It then uses some basic elimination like whether the value points inside the current heap for the system. If it doesn’t match any elimination logic then it is considered to be a reference.

Even though this approach seems a bit extreme, it works out well and the only flip side is that it returns false positives and hence some garbage which should’ve been collected is left out (e.g. because an int containing a value accidentally matches the address of an object on the heap).

Precise GC

In precise GC the GC precisely knows every reference held in a given object. This is possible only if the runtime generates and preserves this information.

As a sample implementation I will describe how the .NET Compact Framework or .NETCF (which uses a precise garbage collector) goes doing about this.

One additional goal employed by .NETCF is that the GC should be independent of the type system (that is shouldn’t need to have any information of types of objects).

Every object in NETCF has an object header which in turn contains a pointer to it’s type descriptor or class descriptor. This class descriptor has a special 32-bit field named RefMap which is used to store reference information stored by a given type

So given an object pointer pObj the GC fetches the object header from before the pointer and then de-references the class descriptor pointer to get to the corresponding type’s Class descriptor. It then reads the RefMap stored into it. To do all of the above the GC needs to know only the object header and class descriptor layout and not any other type information.

All references in NETCF are 32-bit aligned and the refmap contains one bit per 32-bits of the object. If the bit is set it means that the corresponding 32-bit is a reference.

Let’s take the following class

class MyType
{
    public char a;
    public char b;
    public AnotherType t1;
    public int i;
    public AnotherType t2;
}

The memory layout of the class would be something like

The first two chars take 16 bits each. After that there are 3, 32-bit entities of which t1 and t2 (1st and 3rd) are references to other objects but i is not.

So the GC needs to know that given this object the 32-bits from 32nd and 96th are actually references. This is exactly what is encoded in RefMap field. It contains the bit-pattern 00000000000000000000000000001010 where the LSB is for the first 32 bits of the object (which is a+b in this case) and so on.

Since bit 1 of refmap is set it means (pObj + 1 * 32) is a reference. If that value is non-NULL then the GC can just de-reference it to get to the t1 object reference held by pObj.

Obviously it means that the maximum size of object that RefMap can encode is 32*4 = 128bytes. No points for guessing how it handles objects larger than that :)

Back To Basics: Generational Garbage Collection

This post is Part 8 in the series of posts on Garbage Collection (GC). Please see the index here.

One of the primary disadvantage discussed in the post on mark-sweep garbage collection is that it introduces very large system pauses when the entire heap is marked and swept. One of the primary optimization employed to solve this issue is employing generational garbage collection. This optimization is based on the following observations

1. Most objects die young
2. Over 90% garbage collected in a GC is newly created post the previous GC cycle
3. If an object survives a GC cycle the chances of it becoming garbage in the short term is low and hence the GC wastes time marking it again and again in each cycle

The optimization based on the above observations is to segregate objects by age into multiple generations and collect each with different frequencies.

This scheme has proven to work rather well and is widely used in many modern systems (including .NET).

Detailed algorithm

The objects can be segregated into age based generations in different ways, e.g. by time of creation. However one common way is to consider a newly created object to be in Generation 0 (Gen0) and then if it is not collected by a cycle of garbage collection then it is promoted to the next higher generation, Gen1. Similarly if an object in Gen1 survives a GC then that gets promoted to Gen2.

Lower generations are collected more often. This ensures lower system pauses. The higher generation collection is triggered fewer times.

How many generations are employed, varies from system to system. In .NET 3 generations are used. Here for simplicity we will consider a 2 generation system but the concepts are easily extended to more than 2.

Let us consider that the memory is divided into two contiguous blocks, one for Gen1 and the other for Gen0. At start memory is allocated only from Gen0 area as follows

So we have 4 objects in Gen0. Now one of the references is released

Now if GC is fired it will use mark and sweep on Gen0 objects and cleanup the two objects that are not reachable. So the final state after cleaning up is

The two surviving objects are then promoted to Gen1. Promotion includes copying the two objects to Gen1 area and then updating the references to them

Now assume a whole bunch of allocation/de-allocation has happened. Since new allocations are in Gen0 the memory layout looks like

 

The whole purpose of segregating into generations is to reduce the number of objects to inspect for marking. So the first root is used for marking as it points to a Gen0 object. While using the second root the moment the marker sees that the reference is into a Gen1 object it does not follow the reference, speeding up marking process.

Now if we only consider the Gen0 objects for marking then we only mark the objects indicated by ✓. The marking algorithm will fail to locate the Gen1 to Gen0 references (shown in red) and some object marking will be left out leading to dangling pointers.

One of the way to handle this is to somehow record all references from Gen1 to Gen0 (way to do that is in the next section) and then use these objects as new roots for the marking phase. If we use this method then we get a new set of marked objects as follows

This now gives the full set of marked objects. Post another GC and promotion of surviving objects to higher generation we get

At this point the next cycle as above resumes…

Tracking higher to lower generation references

In general applications there are very few (some studies show < 1% of all references) of these type of references. However, they all need to be recorded. There are two general approached of doing this

Write barrier + card-table

First a table called a card table is created. This is essentially an array of bits. Each bit indicates if a given range of memory is dirty (contains a write to a lower generation object). E.g. we can use a single bit to mark a 4KB block.

 

Whenever an reference assignment is made in user code, instead of directly doing the assignment it is redirected to a small thunk (incase .NET the JITter does this). The thunk compares the assignees address to that of the Gen1 memory range. If the range falls within, then the thunk updates the corresponding bit in the card table to indicate that the range which the bit covers is now dirty (shown as red).

First marking uses only Gen0 objects. Once this is over it inspects the card table to locate dirty blocks. Then it considers every object in that dirty block to be new roots and marks objects using it.

As you can see that the 4KB block is just an optimization to reduce the size of the card table. If we increase the granularity to be per object then we can save marking time by having to consider only one object (in contrast to all in 4KB range) but our card table size will also significantly increase.

One of the flip sides is that the thunk makes reference assignment slower.

HW support

Hardware support also uses card table but instead of using thunk it simply uses special features exposed by the HW+OS for notification of dirty writes. E.g. it can use the Win32 api GetWriteWatch to get the list of pages where write happened and use that information to get the card table entries.

However, these kind of support is not available on all platforms (or older version of platforms) and hence is less utilized.