3 ms·

Author of the article here. I don't think system allocators are clever enough to process and allocate 100-500k of very small objects each minute when Python is

Author of the article here. I don't think system allocators are clever enough to process and allocate 100-500k of very small objects each minute when Python is performing something very intensive. It's a pretty standard way to speedup allocation for dynamic languages. Game developers use similar techniques as well. I have some stats on Python's allocator: https://rushter.com/blog/python-object-allocation-statistics/ https://rushter.com/blog/python-object-allocation-statistics...

rurban 7y ago
Can confirm for perl. We are doing the very same. It's a huge win. Differences: We never free empty pools. Our arenas are just single linked lists, no need for the prev. Notes: For a statically compiled perl the biggest win is to avoid arena allocation (mmap) at all. Data and code is made static. That's around 10-20% of the runtime (for shortrunning programs). Also we rarely free at the end. The OS does it much better than free(). Only the mandatory DESTROY calls and FileIO finalizers are executed.
amelius 7y ago
Can Python actually return memory pages back to the OS, e.g. by sbrk() with negative argument? I'm currently having a problem with this, where I load a large deep learning model into "CPU" memory then move it to the GPU, but I can't get rid of the memory reserved by the process.
- CogitoCogito 7y ago
  I can't answer your exact question, but any large allocations/deallocations should be handled by mmap under the hood and in those cases the memory should be returned. In your case you should first consider the possibility that there is a pointer to your model's objects that is for some reason not being released. It might simply be that even though you are moving your model the GPU and maybe removing any of your own references, there might be internal references to your model's data that is hidden from you. At least something to consider. edit: To add to this, I'm now quite sure (though I could be wrong!), that whether python does or does not use sbrk with a negative value is beyond the scope of python. Python is making use of malloc/free under the hood: https://github.com/python/cpython/blob/master/Objects/obmalloc.c#L124-L128 https://github.com/python/cpython/blob/master/Objects/obmall... There's some flexibility for wrapping free in different ways in that file, but it seems like it'll basically always be using free at the core. At least on my system in a debugger I just verified that. So if it's true that python by default uses malloc/free, then the question of whether sbrk with a negative number comes into play is more a question of how your libc implements malloc/free. Of course I might be wrong, but I think that you should probably stop worrying about it at that level and instead look into object references first as I detailed above.
- rushter 7y ago
  Yes, it can, by calling the free function. Which framework do you use for deep learning? It can allocate some object on its own. Can you give me some stats when using a model and after it's no longer in use and can't be accessible? You can get it by calling the sys._debugmallocstats() function.

rurban 7y ago

Can confirm for perl. We are doing the very same. It's a huge win. Differences: We never free empty pools. Our arenas are just single linked lists, no need for the prev. Notes: For a statically compiled perl the biggest win is to avoid arena allocation (mmap) at all. Data and code is made static. That's around 10-20% of the runtime (for shortrunning programs). Also we rarely free at the end. The OS does it much better than free(). Only the mandatory DESTROY calls and FileIO finalizers are executed.

amelius 7y ago

Can Python actually return memory pages back to the OS, e.g. by sbrk() with negative argument? I'm currently having a problem with this, where I load a large deep learning model into "CPU" memory then move it to the GPU, but I can't get rid of the memory reserved by the process.

CogitoCogito 7y ago
I can't answer your exact question, but any large allocations/deallocations should be handled by mmap under the hood and in those cases the memory should be returned. In your case you should first consider the possibility that there is a pointer to your model's objects that is for some reason not being released. It might simply be that even though you are moving your model the GPU and maybe removing any of your own references, there might be internal references to your model's data that is hidden from you. At least something to consider. edit: To add to this, I'm now quite sure (though I could be wrong!), that whether python does or does not use sbrk with a negative value is beyond the scope of python. Python is making use of malloc/free under the hood: https://github.com/python/cpython/blob/master/Objects/obmalloc.c#L124-L128 https://github.com/python/cpython/blob/master/Objects/obmall... There's some flexibility for wrapping free in different ways in that file, but it seems like it'll basically always be using free at the core. At least on my system in a debugger I just verified that. So if it's true that python by default uses malloc/free, then the question of whether sbrk with a negative number comes into play is more a question of how your libc implements malloc/free. Of course I might be wrong, but I think that you should probably stop worrying about it at that level and instead look into object references first as I detailed above.
rushter 7y ago
Yes, it can, by calling the free function. Which framework do you use for deep learning? It can allocate some object on its own. Can you give me some stats when using a model and after it's no longer in use and can't be accessible? You can get it by calling the sys._debugmallocstats() function.