The reason between Java and Python threads is
Java is designed to lock on the resources
Python is designed to lock the thread itself(GIL)
So Python's implementation performs better on a machine with singe core processor. This is fine 10-20 years before. With the increasing computing capacity, if we use multiprocessor machine with same piece of code, it performs very badly.
Is there any hack to disable GIL and use resource locking in Python(Like Java implementation)?
P.S. My application is currently running on Python 2.7.12. It is compute intensive with less I/O and network blocking. Assume that I can't use multiprocessing for my use case.
I think the most straight way for you, that will give you also a nice performance increase is to use Cython.
Cython is a Python superset that compiles Python-like code to C code (which makes use of the cPython API), and from there to executable. It allows one to optionally type variables, that then can use native C types instead of Python objects - and also allows one direct control of the GIL.
It does support a with nogil: statement in which the with block runs with the GIL turned off - if there are other threads running (you use the normal Python threading library), they won't be blocked while code is running on the marked with block.
Just keep in mind that the GIL is there for a reason: it is thanks to it that global complex objects like lists and dictionaries work without the danger of getting into an inconsistent state between treads. But if your "nogil" blocks restrict themselves to local data structures, you should have no problems.
Check the Cython project - and here is an specific example of turning off the GIL:
https://lbolla.info/blog/2013/12/23/python-threads-cython-gil
Related
I am using Python languages and I use CPU threads from the threading thread.Threading wrapper. In some way, the Python interpreter converts my code into PYC byte code with its JIT. (Please provide a reference to Python bytecode standard, but as far as I know standard does not exist. As well it does not exist a standard for a language).
Then these virtual commands are executed. The real commands for Intel'd CPUs are x86/x64 instructions, and for ARM's CPU are AArch64/AArch32 instructions.
My problem - I want to make an action within the Python programming language, that enforces an ordering constraint between the memory operations.
What I want to know:
Q1: How I can emit a command
mfence if Python program is running in x86/x64 CPU
Or instruction like atomic_thread_fence() for LLVM-IR
Q2: How I can specify that some memory is volatile and should not be put into the CPU register for optimization purposes.
CPython does not have a JIT - though it may do one day.
So your code is only converted into bytecode, which will be interpreted, and not into actual Intel/etc. machine code.
Additionally, Python has what's known as the GIL - Global Interpreter Lock - meaning that even if you have multiple Python threads in a process, only one of them can be interpreting code at once - though this may also change one day. Threads were frequently useful for doing I/O, because I/O operations are able to happen at the same time, but these days asyncio is a good competitor for doing that.
So, in response to Q1, it doesn't make any sense to "put an mfence in Python code" (or the like).
Instead, what you probably want to do, if you want to enforce ordering constraints between one bit of code being executed and another, is use more high-level strategies, such as Python threading.Lock, queue.Queue, or similar equivalents in the asyncio world.
As for Q2, are you thinking of the C/C++ keyword volatile? This is frequently mistakenly thought of as a way to make memory access atomic for use in multithreading - it isn't. All that C/C++ volatile does is ensure that memory reads and writes happen exactly as specified rather than being possibly optimised out. What is your use case? There are all sorts of strategies one can use in Python to optimise code, but it's an impossible question to answer without knowing what you're actually trying to do.
Answers to comments
The CPU executes instructions. Somebody should emit this instruction. I'm calling a JIT a part inside a Python interpreter that emits instructions at the end of the day.
CPython is an interpreter - it does not emit instructions. JITs do emit instructions, but as stated above, CPython does not have a JIT. When you run a Python script, CPython will compile your text-based .py file into bytecode, and then it will spend the rest of its time working through the bytecode and doing what the bytecode says. The only machine instructions being executed are those that are emitted by whoever compiled CPython.
If you compile a Python script to a .pyc and then execute that, CPython will do exactly the same, it will just skip the "compile your text-based .py file into bytecode" part as it's already done it - the result of that step is stored in the .pyc file.
I was a bit vague in naming. Do you mean in Python, the instruction is reexecuted each time the interpreter meats the instruction?
A real CPU executing machine code will "re-execute" each instruction as it reads it, sure. CPython will do the same thing with Python bytecode. (CPython will execute a whole bunch of real machine code instructions each time it reads one Python instruction.)
Thanks, I have found this notice https://docs.python.org/3/extending/extending.html "CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once ". Ok, so when Python thread will go with C++/C bindings into native code then what will happen? Q1-A - can in that time Python another thread be executed? Q1-B - If inside C++ code I will create another thread what will happen?
Native code can release the GIL but must lock it again before returning to Python code.
Typically, native code that does some CPU-intensive work or does some I/O that requires waiting would release the GIL while it does that work or waits on that I/O. This allows Python code on another Python thread to run at the same time. But at no point does Python code run on two threads at once. That's why it makes no sense to put native ordering instructions and the like in Python code.
Native code that needs to use native ordering instructions for its own purposes will obviously do that, but that is C/C++ code, not Python code. If you want to know how to put native ordering instructions in a C/C++ Python extension, then look up how to do it in any C/C++ code - the fact that it's a Python extension is irrelevant.
Basically, either write Python code and use high-level strategies like what I mentioned above, or write a native extension in C/C++ and do whatever you need to do in that language.
I need to learn more about GIL and seems that there is a good study of GIL from David Beazley https://dabeaz.com/python/UnderstandingGIL.pdf But #Keiji - you maybe can be wrong with Q1 - CPython Threads seems to be a real thread, and if implementor of C++/C extensions (Almost all libraries for Python) will decide to release GIL lock - it's possible to do... So Q1 still has sense...
I've covered this above. Python code can't interact with native code in a way that would require putting native ordering instructions in Python code.
Back to the question - I mean volatile in sense of C++ getting rid of compiler optimization to not allow optimized variables to be put into the register. In C++ it does not guarantee atomicity and it does not guarantee memory fence. So question regarding volatile how I can specify for integer variable or user-defined type?
If you want to make something in C/C++ be volatile, use the volatile keyword. If you're writing Python code, it doesn't make any sense to make something volatile.
About Python Threads:
The Python thread first of all is tricky. Interpreters use real POSIX/WinAPI threads. Threads thread.Threading under the hood is the real threads.
The thread execution model is pretty specific and can be called "cooperative multitasking" due to one enthusiast (David Beazley, https://www.dabeaz.com/about.html)
As David Beazley stated https://www.dabeaz.com/python/UnderstandingGIL.pdf
When a thread is in the I/O waiting for the Thread release global lock (called GIL lock). David Beazley stated that there is a way to release the lock after the system call.
Next, there is a "tick" instruction in Python VM instructions. If some thread is CPU-bound then the thread will execute that "tick" instruction. (Roughly speaking it occurs every 100ms)
In tick, each thread tries to release GIL and acquire "tick" one more time.
There is no thread scheduler in Python
Multithreading in Python is in fact hurts performance.
I'm hoping someone can provide some insight as to what's fundamentally different about the Java Virtual Machine that allows it to implement threads nicely without the need for a Global Interpreter Lock (GIL), while Python necessitates such an evil.
Python (the language) doesn't need a GIL (which is why it can perfectly be implemented on JVM [Jython] and .NET [IronPython], and those implementations multithread freely). CPython (the popular implementation) has always used a GIL for ease of coding (esp. the coding of the garbage collection mechanisms) and of integration of non-thread-safe C-coded libraries (there used to be a ton of those around;-).
The Unladen Swallow project, among other ambitious goals, does plan a GIL-free virtual machine for Python -- to quote that site, "In addition, we intend to remove the GIL and fix the state of multithreading in Python. We believe this is possible through the implementation of a more sophisticated GC system, something like IBM's Recycler (Bacon et al, 2001)."
The JVM (at least hotspot) does have a similar concept to the "GIL", it's just much finer in its lock granularity, most of this comes from the GC's in hotspot which are more advanced.
In CPython it's one big lock (probably not that true, but good enough for arguments sake), in the JVM it's more spread about with different concepts depending on where it is used.
Take a look at, for example, vm/runtime/safepoint.hpp in the hotspot code, which is effectively a barrier. Once at a safepoint the entire VM has stopped with regard to java code, much like the python VM stops at the GIL.
In the Java world such VM pausing events are known as "stop-the-world", at these points only native code that is bound to certain criteria is free running, the rest of the VM has been stopped.
Also the lack of a coarse lock in java makes JNI much more difficult to write, as the JVM makes less guarantees about its environment for FFI calls, one of the things that cpython makes fairly easy (although not as easy as using ctypes).
There is a comment down below in this blog post http://www.grouplens.org/node/244 that hints at the reason why it was so easy dispense with a GIL for IronPython or Jython, it is that CPython uses reference counting whereas the other 2 VMs have garbage collectors.
The exact mechanics of why this is so I don't get, but it does sounds like a plausible reason.
In this link they have the following explanation:
... "Parts of the Interpreter aren't threadsafe, though mostly because making them all threadsafe by massive lock usage would slow single-threaded extremely (source). This seems to be related to the CPython garbage collector using reference counting (the JVM and CLR don't, and therefore don't need to lock/release a reference count every time). But even if someone thought of an acceptable solution and implemented it, third party libraries would still have the same problems."
Python lacks jit/aot and the time frame it was written at multithreaded processors didn't exist. Alternatively you could recompile everything in Julia lang which lacks GIL and gain some speed boost on your Python code. Also Jython kind of sucks it's slower than Cpython and Java. If you want to stick to Python consider using parallel plugins, you won't gain an instant speed boost but you can do parallel programming with the right plugin.
Is there a way of doing parallel processing in Python using concepts similar to those of Apple's Grand Central Dispatch? Grand Central Dispatch looks, from the outset, like a nice way of handling parallel processing.
If Python does not have a mostly equivalent module, what are the fundamental concepts behind Grand Central Dispatch that could usefully be implemented in Python?
I don't know much about Grand Central Dispatch, hence this question: I would love to know whether Grand Central Dispatch uses paradigms that (1) are not yet available in Python, and/or (2) could be implemented in Python.
The main problem here is the compiler and OS part of GCD. In order to have GCD running, you need the compiler to understand Blocks. You could create something that works similarly when programming, but it wouldn't have the same performance at all. With GCD you can create and enqueue thousands of Blocks, and still, there will only be 2 or 4 threads executing this blocks. If you implement the high level functionality of Blocks without the compiler accepting them, the only way I see is to use threads for "simulating" Blocks. Then, using thousands of threads in a system with 2 to 4 CPU cores, would be an spectacular performance mess, due to context switching, and memory use.
Not only you need the proper compiler extension to support GCD, but also you need a proper OS extension to manage the GCD queues where Blocks are enqueued. You need the OS to behave in a way that it controls how many threads are executing, and when and how many of them to activate when CPU cores are available, for the program using GCD. With GCD, threads and queues are independent. The threads just grab Blocks from queues (light data structures), but from any of those. So it doesn't matter how many blocks are there, because they are only pieces of code and pointers stored somewhere in the main memory.
You simply can't implement all this low level features from python. And only implementing the high level "GCD way of programming", you are going to make veeeery slow programs, or maybe even impossible to execute in a personal computer.
So first, for instance Cython could support GCD, and the OS that you want to use too. Linux has an implementation called libdispatch, available for Devian. But it only implements the compiler portion, so the program starts as many threads as cores on the system minus one. So I think it is still not a good option. Someone should add Linux OS support for GCD, maybe as a kernel module.
Nothing to say about Windows. I really don't know.
So the first natural step, should be to add and test support of CGD in Cython, for Mac OS. From there, you could do a native Python library that internally uses de Cython GCD library, to offer blocks and queues to normal python programmers.
Anoder option could be the CPython project to embrace this, and the Python project to add blocks and queues as a native feature of python. That would be amazing XD
Python does not have an equivalent module, though twisted uses many of the same basic concepts (async APIs, callback-based). The Python multiprocessing module actually uses sub-processes rather than threads and is not particularly equivalent either. The best approach would probably be one similar to that taken by MacRuby, which is to create wrappers for the GCD APIs and use those. Unlike Python, of course, MacRuby was also designed not to have a GIL (Global Interpreter Lock) and this will reduce the effectiveness of multi-threading in Python as various interpret threads hit the GIL at different times. Not much to do about that other than redesign the language, I'm afraid.
What are some good guidelines to follow when deciding to use threads or multiprocessing when speaking in terms of efficiency and code clarity?
Many of the differences between threading and multiprocessing are not really Python-specific, and some differences are specific to a certain Python implementation.
For CPython, I would use the multiprocessing module in either fo the following cases:
I need to make use of multiple cores simultaneously for performance reasons. The global interpreter lock (GIL) would prevent any speedup when using threads. (Sometimes you can get away with threads in this case anyway, for example when the main work is done in C code called via ctypes or when using Cython and explicitly releasing the GIL where approriate. Of course the latter requires extra care.) Note that this case is actually rather rare. Most applications are not limited by processor time, and if they really are, you usually don't use Python.
I want to turn my application into a real distributed application later. This is a lot easier to do for a multiprocessing application.
There is very little shared state needed between the the tasks to be performed.
In almost all other circumstances, I would use threads. (This includes making GUI applications responsive.)
For code clarity, one of the biggest things is to learn to know and love the Queue object for talking between threads (or processes, if using multiprocessing... multiprocessing has its own Queue object). Queues make things a lot easier and I think enable a lot cleaner code.
I had a look for some decent Queue examples, and this one has some great examples of how to use them and how useful they are (with the exact same logic applying for the multiprocessing Queue):
http://effbot.org/librarybook/queue.htm
For efficiency, the details and outcome may not noticeably affect most people, but for python <= 3.1 the implementation for CPython has some interesting (and potentially brutal), efficiency issues on multicore machines that you may want to know about. These issues involve the GIL. David Beazley did a video presentation on it a while back and it is definitely worth watching. More info here, including a followup talking about significant improvements on this front in python 3.2.
Basically, my cheap summary of the GIL-related multicore issue is that if you are expecting to get full multi-processor use out of CPython <= 2.7 by using multiple threads, don't be surprised if performance is not great, or even worse than single core. But if your threads are doing a bunch of i/o (file read/write, DB access, socket read/write, etc), you may not even notice the problem.
The multiprocessing module avoids this potential GIL problem entirely by creating a python interpreter (and GIL) per processor.
Does python threading expose issues of memory visibility and statement reordering as Java does? Since I can't find any reference to a "Python Memory Model" or anything like that, despite the fact that lots of people are writing multithreaded Python code, I'm guessing that these gotchas don't exist here. No volatile keyword, for instance. But it doesn't seem to be stated explicitly anywhere that, for instance, a change in a variable in one thread is immediately visible to all other threads.
Maybe this stuff is all very obvious to Python programmers, but as a fearful Java programmer, I require a little extra reassurance :)
There is no formal model for Python's threading (hey, after all, there wasn't one for Java's for years... hopefully, one will also eventually be written for Python).
In practice, no Python implementation performs any advanced optimization such as statement reordering or temporarily treating shared variables as thread-local ones -- and you can count on these semantics constraints even though they are not formally assured.
CPython in particular, as #Rawheiser's mention, uses a global interpreter lock; other implementations (PyPy, IronPython, Jython, ...) do not (so they can use multiple cores effectively with a threading model, while CPython requires multi-processing for the same purpose), so you should not count on that if you want to write code that's portable throughout Python implementations. (So, you shouldn't count on the "atomicity" of operations that only happen to be atomic in CPython because of the GIL, such as dictionary accesses -- in other Python implementations, multiple threads might be modifying a dict at once and cause errors unless you protect the dict with a lock or the like).