This morning Google Reader brought up this
blog post about a technique used internally by the MS CLR
version 4.0.
It is basically a way to share complex code between
different architectures, thus reducing the maintenance
burden and allowing better compatibility in the tricky area
of marshalling (when the runtime goes from managed code to
unmanaged code it needs to perform many operations,
including massaging the data exchanged between the two
worlds).
At the beginning Mono had specialized x86 chunks of code to
do the work, but we soon realized the problems with that.
At the time I proposed to use a small, specialized bytecode
set (after all the operations involved are very few), which
would have been great for the interpreter I was working on.
Dietmar instead pushed for the use of IL bytecode: this
would have been a slower solution for the interpreter, but
it would allow access to a wider set of operations and it
would reduce the burden on the JIT backend, which, after
all, already had an IL frontend.
We went for the IL solution (the time frame was the
beginning of 2002) and we're glad to see that MS choose to
adopt the same technique a few years later.
There are two important considerations about this story in
the context of Mono development
As an extension, we later used the same IL-based technique
to implement many other runtime helper methods that would
otherwise have been to be written in low-level
architecture-specific code: remoting helpers, garbage
collection fast paths, delegate runtime methods etc.
For the curious, most of this code is in
metadata/marshal.c
in the Mono source code.
One of the things that have been sitting in my TODO list for a few years is to improve the performance of the Regex engine in Mono, both by speeding up the interpreter and by compiling the regular expressions to IL code so that the JIT could optimize it. This seemed like a good project for hack week and Zoltan joined me doing the implementation.
I worked on a new compiler/interpreter combination that uses simplified opcodes, with the aim of making their execution faster and also making the translation to IL code easier. As an example, the old interpreter used a 'Char' opcode to match a single char, but this opcode has several runtime options to ignore the case, negate the assertion and go backward in the string. Each option required decoding and conditional branching at runtime, so removing this overhead should improve performance significantly.
Zoltan made a clever observation: he could reuse the new interpreter for bytecodes that the IL engine he was working on couldn't yet handle and so he used dynamic methods with the same signature as the method in the interpreter that evaluates the regex bytecode. This design has also the nice property that compiled regular expressions will be garbage collected (as opposed to the MS runtime that, at least in the 1.1 version, will leak in this case).
IL-compiling regular expressions has several benefits: it completely removes dispatch and decoding overhead and the JIT will use very fast instructions like compare with immediate to implement the 'Char' bytecode.
I used a few microbenchmarks to test the speed of the new engines, but I'll report here just the results of running the regexdna test from the language shootout: in other cases the speedup is even bigger.
Most of the new code is in svn, though it's not enabled since it's still incomplete. We'd need another week or so to make it usable instead of the old engine and we haven't allocated the time yet to complete this work, but it sure looks promising.
C# as a programming language is still young and has evolved nicely in the last few years: most of the new features take it closer to a high-level language, allowing shorter and nicer code for common patterns. A language needs to be able to cover a wide spectrum of usages to be considered mature but there is one side of the spectrum that has been neglected in C# for far too long: the low level side close to IL code.
Someone already hacked up a program that uses a preprocessor and ilasm to inject IL into C# code, but this approach has many drawbacks (too many to list them all here).
Inline IL code should integrate with the rest of the C# program as closely as possible, allowing, for example, to reference labels defined in C# code from branches in IL code or using the names of local variables and arguments for opcodes like ldloc and ldarg.
The proposal here is to allow IL code as a new statement in the form:
unsafe LITERAL_STRING;This is similar to the traditional way inline assembly has been done (gcc's __asm__ ("code") statement), it's very unlikely to clash with other possible uses of the unsafe keyword and also conveys the notion that IL code may break type safety, IL language rules etc. It has also the added property that all the code needed to implement this support could be easily copied in a separate library and used in standalone programs to, say, simplify code emission for Reflection.Emit (this inline IL support has been implemented inside the Mono C# compiler, so it's C# code that uses Reflection.Emit as the backend).
So, without further ado, the standard sample program written with inline IL:
class InlineIL {
static void Main () {
unsafe @"
ldstr ""Hello, world!""
call void class [mscorlib]System.Console::WriteLine(string)
";
}
}
Ok, that kind of code is written more easily in C# proper,
so what about things that IL code can do, but that C# code
can't? Ever wanted to be able to change the value of a boxed
integer? In C# you can't, but this is very easy with inline IL:
static void ChangeInt (object intval, int new_value) {
unsafe @"
ldarg.0
unbox [mscorlib]System.Int32
ldarg.1
stind.i4
";
}
The following code will print 2:
object boxed = 1; ChangeInt (boxed, 2); Console.WriteLine (boxed);Of course you can access types and fields defined by the C# program currently being compiled, consider the following example:
class InlineIL {
static int field1;
static void Main () {
int localvar = 1;
unsafe @"
ldloc localvar
stsfld int32 class InlineIL::field1
";
}
}
Note that in this case, the compiler won't emit warnings
about field1 and localvar being never used and of course
you'll get an error if you mispell the field name in IL code
as you would in C# code.
The main usage of the new feature would be for some corlib methods in mono or for more easily implementing test cases for the JIT and runtime test suites: some specific IL code patterns (that may not be expressed in C# or that there is no guarantee C# will compile to the effecting code) can be easily written while the rest of the boilerplate code needed by the unit testing program can be written in much more readable C#. That said, this opens many possibilities for a creative hacker, finally free of the constraints of C#.
Happy hacking!
Mono is a JIT compiler and as such it compiles a method only when needed: the moment the execution flow requires the method to execute. This mode of execution greatly improves startup time of applications and is implemented with a simple trick: when a method call is compiled, the generated native code can't transfer execution to the method's native code address, because it hasn't been compiled yet. Instead it will go through a magic trampoline: this chunk of code knows which method is going to be executed, so it will compile it and jump to the generated code.
The way the trampoline knows which method to compile is pretty simple: for each method a small specific trampoline is created that will pass the pointer to the method to execute to the real worker, the magic trampoline.
Different architectures implement this trampoline in different ways, but each with the aim to reduce its size: the reason is that many trampolines are generated and so they use quite a bit of memory.
Mono in svn has quite a few improvements in this area compared to mono 1.2.5 which was released just a few weeks ago. I'll try to detail the major changes below.
The first change is related to how the memory for the specific trampolines is allocated: this is executable memory so it is not allocated with malloc, but with a custom allocator, called Mono Code Manager. Since the code manager is used primarily for methods, it allocates chunks of memory that are aligned to multiples of 8 or 16 bytes depending on the architecture: this allows the cpu to fetch the instructions faster. But the specific trampolines are not performance critical (we'll spend lots of time JITting the method anyway), so they can tolerate a smaller alignment. Considering the fact that most trampolines are allocated one after the other and that in most architectures they are 10 or 12 bytes, this change alone saved about 25% of the memory used (they used to be aligned up to 16 bytes).
To give a rough idea of how many trampolines are generated I'll give a few examples:
So reducing the size of the trampolines is great, but it's
really not possible to reduce them much further in size, if
at all. The next step is trying just not to create them.
There are two primary ways a trampoline is generated: a
direct call to the method is made or a virtual table slot is
filled with a trampoline for the case when the method is
invoked using a virtual call. I'll note here than in both
cases, after compiling the method, the magic trampoline will
do the needed changes so that the trampoline is not
executed again, but execution goes directly to the newly
compiled code. In one case the callsite is changed so that
the branch or call instruction will transfer control to the
new address. In the virtual call case the magic trampoline
will change the virtual table slot directly.
The sequence of instructions used by the JIT to implement a virtual call are well-known and the magic trampoline (inspecting the registers and the code sequence) can easily get the virtual table slot that was used for the invocation. The idea here then is: if we know the virtual table slot we know also the method that is supposed to be compiled and executed, since each vtable slot is assigned a unique method by the class loader. This simple fact allows us to use a completely generic trampoline in the virtual table slots, avoiding the creation of many method-specific trampolines.
In the cases above, the number of generated trampolines goes from 21000 to 7700 for MonoDevelop (saving 160 KB of memory), from 17000 to 5400 for the IronPython case and from 800 to 150 for the hello world case.
I'll describe more optimizations (both already committed and forthcoming) in the next blog posts.
I guess some of you expected a blog entry about the
generational GC in Mono, given the title. From my
understanding many have the expectation that the new GC will
solve all the issues they think are caused by the GC so they
await with trepidation.
As a matter of fact, from my debugging of all or almost all
those issues, the existing GC is not the culprit. Sometimes
there is an unmanaged leak, sometimes a managed or unmanaged
excessive retention of objects, but basically 80% of those
issues that get attributed to the GC are not GC issues at
all.
So, instead of waiting for the holy grail, provide test
cases or as much data as you can for the bugs you
experience, because chances are that the bug can be fixed
relatively easily without waiting for the new GC to
stabilize and get deployed.
Now, this is not to say that the new GC won't bring great
improvements, but that those improvements are mainly in
allocation speed and mean pause time, both of which, while
measurable, are not bugs per-se and so are not part of the
few issues that people hit with the current Boehm-GC based
implementation.
After the long introduction, let's go to the purpose of this entry: svn Mono now can perform an object allocation entirely in managed code. Let me explain why this is significant.
The Mono runtime (including the GC) is written in C code and
this is called unmanaged code as opposed to managed code
which is all the code that gets JITted from IL opcodes.
The JIT and the runtime cooperate so that managed code is
compiled in a way that lets the runtime inspect it, inject
exceptions, unwind the stack and so on. The unmanaged code,
on the other hand, is compiled by the C compiler and on most
systems and architectures, there is no info available on it
that would allow the same operations. For this reason,
whenever a program needs to make a transition from managed
code to unmanaged (for example for an internal call
implementation or for calling into the GC) the runtime needs
to perform some additional bookeeping, which can be relatively
expensive, especially if the amount of code to execute in
unmanaged land is tiny.
Since a while we have made use of the Boehm GC's ability to
allocate objects in a thread-local fast-path, but we
couldn't take the full benefit of it because the cost of the
managed to unmanaged and back transition was bigger than the
allocation cost itself.
Now the runtime can create a managed method that performs
the allocation fast-path entirely in managed code, avoiding
the cost of the transition in most cases. This
infrastructure will be also used for the generational GC
where it will be more important: the allocation fast-path
sequence there is 4-5 instructions vs the dozen or more of
the Boehm GC thread local alloc.
As for actual numbers, a benchmark that repeatedly allocates small objects is now more than 20% faster overall (overall includes the time spent collecting the garbage objects, the actual allocation speed increase is much bigger).
I uploaded version 0.2 of the monocov coverage tool for Mono here. It is also available from the monocov svn module from the usual Mono svn server.
The release features an improved Gtk# GUI, fixes to html
rendering and other minor improvements.
The usage is pretty simple, just run you program or test
suite with the following command after having installed monocov:
mono --debug --profile=monocov program.exeThe coverage information will be output to the program.exe.cov file. Now you can load this file in the GUI with:
monocov program.exe.covand browse the namespaces for interesting types you want to check code coverage for. Double clicking on a method will bring up a viewer with the source file of the method with the lines of code not reached by execution highlighted in red.
To limit the collection of data to a specific assembly you can specify it as an argument to the profiler. For example, to consider only the code in mscorlib, use:
mono --debug --profile=monocov:+[mscorlib] test-suite.exeTo be able to easily collect coverage information from the unit tests in the mono mcs directory you can also run the test suite as follows, for example in mcs/class/corlib:
make run-test RUNTIME_FLAGS="--profile=monocov:outfile=corlib.cov,+[mscorlib]"Monocov can also generate a set of HTML pages that display the coverage data. Here are the files generated when running the nunit-based test suite for mono's mscorlib with the following command:
monocov --export-html=/tmp/corlib-cov corlib.cov
Hopefully this tool will help both new and old contributors
to easily find untested spots in our libraries and
contribute tests for them.I just committed to svn a small function that can be used to help debug deadlocks that result from the incorrect use of managed locks.
Managed locks (implemented in the Monitor class and usually invoked with the lock () construct in C#) are subject to the same incorrect uses of normal locks, though they can be safely taken recursively by the same thread.
One of the obviously incorrect way to use locks is to have multiple locks and acquire them in different orders in different codepaths. Here is an example:
using System; using System.Threading;I added an explicit Sleep () call to make the race condition happen almost every time you run such a program. The issue with such deadlocks is that usually the race time window is very small and it will go unnoticed during testing. The new feature in the mono runtime is designed to help find the issue when a process is stuck and we don't know why.
class TestDeadlock {
static object lockA = new object (); static object lockB = new object ();
static void normal_order () { lock (lockA) { Console.WriteLine ("took lock A"); // make the deadlock more likely Thread.Sleep (500); lock (lockB) { Console.WriteLine ("took lock B"); } } } static void reverse_order () { lock (lockB) { Console.WriteLine ("took lock B"); // make the deadlock more likely Thread.Sleep (500); lock (lockA) { Console.WriteLine ("took lock A"); } } } static void Main () { TestDeadlock td = new TestDeadlock (); lock (td) { lock (td) { // twice for testing the nest level Thread t1 = new Thread ( new ThreadStart (normal_order)); Thread t2 = new Thread ( new ThreadStart (reverse_order)); t1.Start (); t2.Start (); t1.Join (); t2.Join (); } } } }
Now you can attach to the stuck process using gdb and issue the following command:
(gdb) call mono_locks_dump (0)which results in output like this:
Lock 0x824f108 in object 0x2ffd8 held by thread 0xb7d496c0,
nest level: 2
Lock 0x824f150 in object 0x2ffe8 held by thread 0xb7356bb0,
nest level: 1
Waiting on semaphore 0x40e: 1
Lock 0x824f1b0 in object 0x2ffe0 held by thread 0xb7255bb0,
nest level: 1
Waiting on semaphore 0x40d: 1
Total locks (in 1 array(s)): 16, used: 8, on freelist: 8, to
recycle: 0
We can see that there are three locks currently held by
three different threads. The first has been recursively
acquired 2 times. The other two are more interesting because
they each have a thread waiting on a semaphore associated
with the lock structure: they must be the ones involved in
the deadlock.Once we know the threads that are deadlocking and the objects that hold the lock we might have a better idea of where exactly to look in the code for incorrect ordering of lock statements.
In this particular case it's pretty easy since the objects used for locking are static fields. The easy way to get the class is to notice that the object which is locked twice (0x2ffd8) is of the same class as the static fields:
(gdb) call mono_object_describe (0x2ffd8) TestDeadlock object at 0x2ffd8 (klass: 0x820922c)Now we know the class (0x820922c) and we can get a list of the static fields and their values and correlate with the objects locked in the mono_locks_dump () list:
(gdb) call mono_class_describe_statics (0x820922c) At 0x26fd0 (ofs: 0) lockA: System.Object object at 0x2ffe8 (klass: 0x820beac) At 0x26fd4 (ofs: 4) lockB: System.Object object at 0x2ffe0 (klass: 0x820beac)Note that the lockA and lockB objects are the ones listed above as deadlocking.
Starting with Mono version 1.2.1, the Mono JIT supports the new ARM ABI (also called gnueabi or armel). This is the same ABI used by the 2006 OS update of the Nokia 770 and it should be good news for all the people that asked me about having Mono run on their newly-flashed devices.
The changes involved enhancing the JIT to support soft-float targets (this work will also help people porting mono to other embedded architectures without a hardware floating point instruction set) as well as the ARM-specific call convention changes. There was also some hair-pulling involved, since the gcc version provided with scratchbox goes into an infinite loop while compiling the changed mini.c sources when optimizations are enabled, but I'm sure you don't want to know the details...
This was not enough, though, to be able to run Gtk# applications on the Nokia 770. When I first ran a simple Gtk# test app I got a SIGILL inside gtk_init() in a seemlingly simple instruction. Since this happened inside a gcc-compiled binary I had no idea what the JIT could have been doing wrong. Then this morning I noticed that the instructions in gtk_init() were two bytes long: everything became clear again, I needed to implement interworking with Thumb code in the JIT. This required a few changes in how the call instructions are emitted and at callsite patching. The result is that now Mono can P/Invoke shared libraries compiled in Thumb mode (mono itself must still be compiled in ARM mode: this should be easy to fix, but there is no immediate need now for it). Note that this change didn't make it to the mono 1.2.1 release, you'll have to use mono from svn.
As part of this work, I also added an option to mono's configure to disable the compilation of the mcs/ directory, which would require running mono in emulation by qemu inside scratchbox. The new option is --disable-mcs-build. This can also be useful when building the runtime on slow boxes, if the building of the mcs/ dir is not needed (common for embedded environments where the managed assemblies are simply copied from an x86 box).
There are not yet packages ready for the Nokia 770, though I'll provide a rough tarball of binaries soon: the issue is that at least my version of scratchbox has a qemu build that fails to emulate some syscalls used by mono, so it's hard to build packages that require mono or mcs to be run inside scratchbox. I'm told this bug has been fixed in more recent versions, so I'll report how well jitted code runs in qemu when I'll install a new scratchbox. This is not the best way to handle this, though, because even if qemu can emulate everything mono does, it would be very slow and silly to run it that way: we should run mono on the host, just like we run the cross-compiling gcc on the host from inside scratchbox and make it appear as a native compiler. From a quick look at the documentation, it should be possible to build a mono devkit for scratchbox that does exactly this. This would be very nice for building packages like Gtk# that involve both managed assemblies and unmanaged shared libraries (the Gtk# I used for testing required lots of painful switches between scratchbox for compiling with gcc and another terminal for running the C#-based build helper tools and mcs...). So, if anyone has time and skills to develop such a devkit, it will be much appreciated! Alternatively, we could wait for debian packages to be built as part of the debian project's port to armel, which will use armel build boxes.
On mono, using the simple array was about 3 times slower than using the generics implementation, so I ran the profiler to find out why.
It turns out that in the implementation of the IList<T> interface methods we used a special generic internal call to access the array elements: this internal call is implemented by a C function that needs to cope with any array element type. But since it is an internal call and the JIT knows what it is supposed to do, I quickly wrote the support to recognize it and inline the instructions to access the array elements. This makes the two versions of the code run with about the same speed (with mono from svn, of course).
The interesting fact is that the MS runtime behaves similarly, with the simple array test running about 3 times slower than the IList<T> implementation. If you're curious about why the MS implementation is so slow, follow the link above: I guess sooner or later some MS people will explain it.
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