How I Improved Hxcpp Speed 6x

Let me start by qualifying the title with the obligatory phrase in some situations. TL;DR – it was probably too slow to start with.

The performance story starts, like all good performance stories do, with a benchmark. I was looking for a benchmark to measure the allocation system, but also mix in a little calculation, in as few lines as possible – so I settled on the Mandelbrot program. But with a little “deoptimization” to use classes for the complex arithmetic rather than inlining the components. On a whim, I also decided to store the resulting image as an array of RGB classes, rather that an array of Int, which turned out to also have significant implications.

You can see the code in the haxe repo. You may note there are 2 versions controlled by a define, a class based one and an anonymous object (typedef) based one. The typedef option is for future optimization attempts. Also, the performance figures I will be talking about have the “SIZE” parameter increased to 40, to allow more accurate timing. The results are predictable, if not beautiful.


This benchmark is all about allocation and garbage collection (GC). The allocation part calls the object “new” routine to find some space on one of the memory blocks, and the collection part must scan the allocation space to reclaim object space that is no longer used. These phases have quite different optimization characteristics – the allocation part gets called literally millions of times, and therefore micro-optimizations at the code level may be appropriate, while the collection code get run less frequently, but must do quite a lot of work, so algorithmic optimizations might be more appropriate.

The initial results with hxcpp 3.2.102 were really quite bad. Hxcpp 3.2.102 takes approximately 35 seconds on a windows 32 build. Compare this to the node/js solution of 6.1 second. It should be noted that this test is measuring mostly allocations, and code base is small, so node can quite efficiently inline the code and use its highly-tuned single-thread allocation code. But still, it should be possible to improve things.

Before even looking at the low-level code, one thing that has a big effect on speed is the amount of free space available. The Gc allocates into its free memory, and when it’s full, it reclaims/collects the unused portions. The speed of the reclaiming depends mostly on the the amount of “live” memory after the collection. By increasing the amount of free memory, the time between collections increases, while keeping the collection time roughly the same, thereby improving the overall speed. Hxcpp uses a heuristic to determine the free space it should use depending on the “live” size at last collection. There was a fixed ratio in 3.2.103, but since then I have exposed the settings and increased the minimum working memory to 20M on desktop and 8M on mobile. This alone gives a nice improvement.

After creating a benchmark, the next step in optimization is to run a profiler. And the profiler did indeed show that most of the time was spent in the allocation code. It did not appear to be in one particular part of the code, but instead spread over the whole routine. This implies that I really need to get the line count down, and to that end I decided to simplify some the auxiliary data structures at the expense of memory. The hxcpp Gc uses conservative marking on the stack. That is, it scans the memory and looks for things that look like pointers to Gc objects. It then needs to know with 100% certainty that the thing pointed to is (or was recently) an actual object, otherwise the marking process could trash memory. The means that some extra information needs to be used to track this. Initially, I used what I thought was a clever trick to reuse the byte I was already using to indicate whether a line was marked as a head for a mini linked-list of live-objects. This worked and did not require extra memory, but the maintenance of the list complicated the allocation routine. I switched to using a extra bitmap, which uses 1 bit per 4 bytes of Gc memory to indicate whether an object starts at the particular location. This is 3% extra memory, but it greatly simplifies the allocation logic, allowing for a neater implementation.

The next point of simplification was pre-calculating the “gaps” in the Gc memory blocks. Initially, this was done in the allocation code, but by doing it in advance, it simplifies allocation code that is getting run millions of times. Theoretically, there are a similar number of operations going on here, but practically the separation makes the code much neater and therefore faster. Later, I moved this calculation to a separate thread so it can be done in parallel with the normal code.

I also rearranged the object header to allow the marking code to test whether an object needs marking with a single bit test, and therefore avoid an expensive marking function call in some circumstances. The header also includes a neater row count which makes the actual row marking faster.

These changes sped the allocations up considerably, but the profiler still showed most of the time running the allocation code. The main issue then became the function call depth. Modern c++ compilers can do a pretty good job at inlining code, but they are not magic, so some extra help is required. First a quick review of what happens when you call “new Complex” in haxe. Hxcpp generates a standard c++ “new” call, which calls into hx::Object “operator new” which is where the Gc memory allocation routine is called. C++ then initialises the object by inserting the vtable pointer and hxcpp then calls “__construct” on the object – which is the “new” function defined in haxe. It is split like this to allow the haxe code to call “super” in ways that c++ does not allow with its native constructors. The “operator new” code takes a parameter defining whether the resulting object contains pointers to other object – this allows the marking code to make some optimizations. In the original code, this then called an “InternalNew” function which checked the object size and delegated to either a thread-local allocator, or a large allocator. This allows independent threads to allocate at full speed without needing locks. The problem with this arrangement is that several function calls are required per allocation. The solution here was it inline quite a lot of code into the “operator new” function, and also to observe that haxe objects always allocate from the local allocator, so some some checks can be avoided. Inlining means that some of the guts of the allocator need to be exposed to the haxe code. However, this is kept under control by keeping the complex case (when there is not enough room) in the separate file, and only inlining the “easy” case, which is also helped by the simplifications made earlier. You can see the code here.

There are still a few things that could be done. There are some checks that could be avoided in single threaded app. Another idea I had was to move some of the housekeeping (setting of the header and start bits) to a separate thread to be done later. There are still 2 function calls going on here – the “operator new” (which gets inlined) and the “__construct” call (which does not). It should be possible to inline the construct call if it is simple enough.

These were the changes to the allocation code. As mentioned earlier, there were some improvements to the marking code. Additional things were also done in the collector. The reclaimed memory is cleared in a background thread. This is actually is quite a nice improvement because up to 7% of the time was spent doing the clear. One thing this particular benchmark highlighted was the marking of a single large array (the image array) actually got slower when using multi-threaded marking, due to a slight overhead. I’m not sure how applicable this is to general code, however there was a solution. The multi-threaded array marking code now splits large array marking up if there are idle marking threads.

So with all these improvements, did it beat the node/js speed? Just about – it was a close thing. One more change was really required – compile a 64 bit target instead of a 32 bit target. Then finally the time came in at 5.6 seconds, less than 1/6 the original time. Node is doing a pretty awesome job here and I’m sure it is only going to get better so it will take some work to keep up. If I change the benchmark to use member functions instead of static-inline functions, then hxcpp becomes significantly faster than node, which is nice to know. It should also be noted that I’m cheating a bit here by using multiple threads to get the job done. But I’m not above a bit of cheating.

This brings me to the Java target – 2 seconds. Wow. I’m not actually sure if it is doing any allocations here at all – maybe it has completely inlined the code. Or maybe it just really really good. That sets a seriously high benchmark

Is there still room for improvement? I think there is – particularly, a generational Gc would reduce the marking time to practically nothing in this case, but it requires a bit of effort on the haxe compiler side. Inlining the constructor for simple classes (like the “Complex” class in this code) should also be a nice win.

It has been an interesting experience getting the code faster. I think that it highlights one key thing – if you want the hxcpp code to run faster, then create a simple benchmark.

9 Replies to “How I Improved Hxcpp Speed 6x”

  1. Not only don’t I see any improvement on Android (game still stutters because of GC every 3-4 seconds and loses 2-3 frames), but with your new hxcpp version my Windows target no longer compiles as it somehow clashes with my Audio class’s PlaySound(s:String):
    ./src/Audio.cpp(249) : error C2039: ‘PlaySoundA_dyn’ : is not a member of ‘Audio_obj’
    C:UsersozdyappsBlueBallBenExportwindowscppobjincludeAudio.h(17) : see declaration of ‘Audio_obj’

    1. Unfortunately, these changes do not significantly address the pause time, as you have noted.
      The windows issues has been fixed in the git version, and I will do another release in the next day or two. Meanwhile, you could try version 3.2.177 from

  2. These constants can really only tune the frequency of the collections, not the duration. If you increase the working memory, your pauses will happen less frequently. If you reduce your allocations enough and increase your memory enough, you may be able to go quite a while without a collection, and if force a collection at the beginning of the level you may be able to hide this issue. You could do something like “cpp.vm.Gc.setMinimumWorkingMemory(40*1024*1024)” and see if you get any change. Note that you will need the git version of haxe to call this function, but instead you can just copy the body of that linked function, eg “untyped __global__.__hxcpp_set_minimum_working_memory(40*1024*1024);”
    The pause time is more or less proportional to the amount of “live data” you have, so one way of reducing the pause time is to reduce the amount of data you have – eg, do not cache large xml files and that sort of thing.

  3. Very interesting read, I was lately trying to understand how the Haxe/hxcpp/C++ layers were working. I found this (I can be wrong, please correct me):

    1) Every Haxe object is converted to a C++ class than inherits from hx::Object (I think String is a special case)

    2) Every class member (static or not) is registered in a dictionary for reflection, if we don’t use reflection the classes still call the reflection methods at construction: MyClass_obj::__register, MyClass_obj::__Mark, MyClass_obj::__Visit.

    3) __Mark and __Visit seem to do very little in release mode

    4) There is no way to avoid the reflection overhead (something like :@noreflection in Haxe)

    5) Translated classes have empty constructors and no destructors, allocation is done overloading the new operator from the hx::Object that uses the IMMIX allocator, this can be done using thread workers.

    6) IMMIX uses the class GlobalAllocator to reclaim memory system in case it needs more.

    7) I’m still lost in the destruction phase, I see some overloaded delete declarations but that is.

    8) Variables that are of type Int and Float are fine inside class methods because they are not added to the GC and just translated to their C++ counterpart function stack variables. Anything else goes inside the GC.

    9) Fragmentation can make the GC consume more memory than needed, bigger memory needs more time to traverse hence more time spent searching for object to trash.

    10) Using pools can help as long the pools are short, bigger pools increase the GC memory used and can be the same as not using any pooling at all.

    11) The only way to really minimize the overhead of the GC is to reduce at the minimum the use of the “new” keyboard in the Haxe code.

    I was testing using functions that received a parameter to pass returning values instead of functions creating a new object to return (like a 3D vector) and could observe some better performance, still not sure if its just an illusion, I need to do more profiling.

    I know this is hard problem to optimize but if you have any corrections or points for me to check I would like to know them.

    1. This is largely correct. 1) Yes, String is not really an object, but is a bit special.
      3) Mark and visit do stuff if you have member variables that are themselves objects or strings.
      4) You can avoid registering the members with the @:unreflective meta data.
      7) the destructors are inline and empty – ie, they do nothing. c++ needs these to match the new operators or it complains.
      11) you also have to watch out for some cases that implicitly use allocations – such as anonymous objects (“{x:2.1}”) and values passed to closure functions.
      Filling existing objects, rather than creating new ones, sounds like a good idea.
      You can use the ‘hxScout’ project to help finding hot-spots for allocation.

  4. Thanks for the tips Hugh,

    4) @:unreflective, I was totally unaware of this, my bad!
    Testing on my little test here:
    I can see __Field, __SetField, __SetStatic, static ::String sMemberFields being pruned from the generated code, however the __GetFields and __register functions are still present. I guess they are still necessary. Is this OK?

    11) Yes, I avoid to use anonymous objects (even Dynamic objects)
    I also try to avoid closures defined in the same function, but referencing a class member for events is the same, isn’t it?
    We use that for callbacks.

    Thanks again for your tips.
    I’m planing to check the fragmentation logs later and try to understand the deallocation process.

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