mono/web/performance

* Writing better performing .NET and Mono applications

<center>
Miguel de Icaza ([email protected])<br>
Ben Maurer ([email protected])
</center>

	The following document contains a few hints on how to improve
	the performance of your Mono/.NET applications.

	These are just guidelines, and you should still profile your
	code to find the actual performance problems in your
	application. It is never a smart idea to make a change with the
	hopes of improving the performance of your code without first
	measuring. In general, these guidelines should serve as ideas
	to help you figure out `how can I make this method run faster'.
	
	It is up to you to figure out, `Which method is running slowly.'

** Using the Mono profiler
	
	So, how does one measure what method are running slowly? A profiler
	helps with this task. Mono includes a profiler that is built
	into the runtime system. You can invoke this profiler on your program
	by running with the --profile flag.

<pre class="shell">
	mono --profile program.exe
</pre>

	The above will instruct Mono to instrument your application
	for profiling.  The default Mono profiler will record the time
	spent on a routine, the number of times the routine called,
	the memory consumed by each method broken down by invoker, and
	the total amount of memory consumed.

	It does this by asking the JIT to insert a call to the profiler
	every time a method is entered or left. The profiler times the
	amount of time elapsed between the beginning and the end of the
	call. The profiler is also notified of allocations.
	
	When the program has finished executing, the profiler prints the
	data in human readable format. It looks like:

<pre class="shell">
Total time spent compiling 227 methods (sec): 0.07154
Slowest method to compile (sec): 0.01893: System.Console::.cctor()
Time(ms) Count   P/call(ms) Method name
########################
  91.681       1   91.681   .DebugOne::Main()
  Callers (with count) that contribute at least for 1%:
           1  100 % .DebugOne::Main(object,intptr,intptr)
...
Total number of calls: 3741
...
Allocation profiler
Total mem Method
########################
     406 KB .DebugOne::Main()
         406 KB     1000 System.Int32[]                                  
  Callers (with count) that contribute at least for 1%:
           1  100 % .DebugOne::Main(object,intptr,intptr)
Total memory allocated: 448 KB
</pre>

	At the top, it shows each method that is called. The data is sorted
	by the total time that the program spent within the method. Then
	it shows how many times the method was called, and the average time
	per call.
	
	Below this, it shows the top callers of the method. This is very useful
	data. If you find, for example, that the method Data::Computate () takes
	a very long time to run, you can look to see if any of the calls can be
	avoided.
	
	Two warnings must be given about the method data. First,
	the profiler has an overhead associated with it. As such,
	a high number of calls to a method may show up as consuming
	lots of time, when in reality they do not consume much time
	at all. If you see a method that has a very high number of
	calls, you may be able to ignore it. However, do consider
	removing calls if possible, as that will sometimes help
	performance. This problem is often seen with the use
	of built in collection types.
	
	Secondly, due to the nature of the profiler, recursive calls
	have extremely large times (because the profiler double counts
	when the method calls itself). One easy way to see this problem
	is that if a method is shown as taking more time than the Main
	method, it is very likely recursive, and causing this problem.
	
	Below the method data, allocation data is shown. This shows
	how much memory each method allocates. The number beside
	the method is the total amount of memory. Below that, it
	is broken down into types. Then, the caller data is given. This
	data is again useful when you want to figure out how to eliminate calls.
	
	You might want to keep a close eye on the memory consumption
	and on the method invocation counts.   A lot of the
	performance gains in MCS for example came from reducing its
	memory usage, as opposed to changes in the execution path.

** Profiling without JIT instrumentation

	You might also be interested in using mono --aot to generate
	precompiled code, and then use a system like `oprofile' to 
	profile your programs. 

** Memory Management in the .NET/Mono world.

	Since Mono and .NET offer automatic garbage collection, the
	programmer is freed from having to track and dispose the
	objects it consumes (except for IDispose-like classes).   This
	is a great productivity gain, but if you create thousands of
	objects, that will make the garbage collector do more work,
	and it might slow down your application.
	
	Remember, each time you allocate an object, the GC is forced
	to find space for the object. Each object has an 8 byte overhead
	(4 to tell what type it is, then 4 for a sync block). If
	the GC finds that it is running out of room, it will scan every
	object for pointers, looking for unreferenced objects. If you allocate
	extra objects, the GC then must take the effort to free the objects.
	
	Mono uses the Boehm GC, which is a conservative collector,
	and this might lead to some memory fragmentation and unlike
	generational GC systems, it has to scan the entire allocated
	memory pool.
	
*** Boxing
	The .NET framework provides a rich hierarchy of object types.
	Each object not only has value information, but also type
	information associated with it. This type information makes
	many types of programs easier to write. It also has a cost
	associated with it. The type information takes up space.
	
	In order to reduce the cost of type information, almost every
	Object Oriented language has the concept of `primitives'.
	They usually map to types such as integers and booleans. These
	types do not have any type information associated with them.
	
	However, the language also must be able to treat primitives
	as first class datums -- in the class with objects. Languages
	handle this issue in different ways. Some choose to make a
	special class for each primitive, and force the user to do an
	operation such as:
<pre class="shell">
// This is Java
list.add (new Integer (1));
System.out.println (list.get (1).intValue ());
</pre>

	The C# design team was not satisfied with this type 
	of construct. They added a notion of `boxing' to the language.
	
	Boxing preforms the same thing as Java's <code>new Integer (1)</code>.
	The user is not forced to write the extra code. However,
	behind the scenes the <em>same thing</em> is being done
	by the runtime. Each time a primitive is cast to an object,
	a new object is allocated.
	
	You must be careful when casting a primitive to an object.
	Note that because it is an implicit conversion, you will
	not see it in your code. For example, boxing is happening here:

<pre class="shell">
ArrayList foo = new ArrayList ();
foo.Add (1);
</pre>
	
	In high performance code, this operation can be very costly.

*** Using structs instead of classes for small objects

	For small objects, you might want to consider using value
	types (structs)	instead of object (classes).
	
	However, you must be careful that you do not use the struct
	as an object, in that case it will actually be more costly.
	
	As a rule of thumb, only use structs if you have a small
	number of fields (totaling less than 32 bytes), and
	need to pass the item `by value'. You should not box the object.

*** Assisting the Garbage Collector

	Although the Garbage Collector will do the right thing in
	terms of releasing and finalizing objects on time, you can
	assist the garbage collector by clearing the fields that
	points to objects.  This means that some objects might be
	eligible for collection earlier than they would, this can help
	reduce the memory consumption and reduce the work that the GC
	has to do.

** Common problems with <tt>foreach</tt>

	The <tt>foreach</tt> C# statement handles various kinds of
	different constructs (about seven different code patterns are
	generated).   Typically foreach generates more efficient code
	than loops constructed manually, and also ensures that objects
	which implement IDispose are properly released.

	But foreach sometimes might generate code that under stress
	performs badly.  Foreach performs badly when its used in tight
	loops, and its use leads to the creation of many enumerators.
	Although technically obtaining an enumerator for some objects
	like ArrayList is more efficient than using the ArrayList
	indexer, the pressure introduced due to the extra memory
	requirements and the demands on the garbage collector make it
	more inefficient.

	There is no straight-forward rule on when to use foreach, and
	when to use a manual loop.  The best thing to do is to always
	use foreach, and only when profile shows a problem, replace
	foreach with for loops.