Python : An interpreted high-level programming language that has a lot of support for "systems programming" and which integrates well with existing software in other languages. • Concurrency : Doing more than one thing at a time. Of particular interest to programmers writing code for running on big iron, but also of interest for users of multicore PCs. Usually a bad idea--except when it's not.
We're going to explore the state of concurrent programming idioms being used in Python • A look at tradeoffs and limitations • Hopefully provide some clarity • A tour of various parts of the standard library • Goal is to go beyond the user manual and tie everything together into a "bigger picture."
primary focus is on Python • This is not a tutorial on how to write concurrent programs or parallel algorithms • No mathematical proofs involving "dining philosophers" or anything like that • I will assume that you have had some prior exposure to topics such as threads, message passing, network programming, etc.
like Python programming, but this tutorial is not meant to be an advocacy talk • In fact, we're going to be covering some pretty ugly (e.g., "sucky") aspects of Python • You might not even want to use Python by the end of this presentation • That's fine... education is my main agenda.
of programs that can work on more than one thing at a time • Example : A network server that communicates with several hundred clients all connected at once • Example : A big number crunching job that spreads its work across multiple CPUs 8
typically implies "multitasking" run run run run run Task A: Task B: task switch • If only one CPU is available, the only way it can run multiple tasks is by rapidly switching between them
You may have parallelism (many CPUs) • Here, you often get simultaneous task execution run run run run run Task A: Task B: run CPU 1 CPU 2 • Note: If the total number of tasks exceeds the number of CPUs, then each CPU also multitasks
tasks execute by alternating between CPU processing and I/O handling 11 run run run run I/O system call • For I/O, tasks must wait (sleep) • Behind the scenes, the underlying system will carry out the I/O operation and wake the task when it's finished
A task is "CPU Bound" if it spends most of its time processing with little I/O 12 run run run I/O I/O • Examples: • Crunching big matrices • Image processing
A task is "I/O Bound" if it spends most of its time waiting for I/O 13 run run I/O • Examples: • Reading input from the user • Networking • File processing • Most "normal" programs are I/O bound run I/O run I/O I/O
Tasks may run in the same memory space run run run run run Task A: Task B: run CPU 1 CPU 2 object write read • Simultaneous access to objects • Often a source of unspeakable peril Process
might run in separate processes run run run run run Task A: Task B: run CPU 1 CPU 2 • Processes coordinate using IPC • Pipes, FIFOs, memory mapped regions, etc. Process Process IPC
Tasks may be running on distributed systems run run run run run Task A: Task B: run messages • For example, a cluster of workstations • Communication via sockets
is interpreted 18 • Frankly, it doesn't seem like a natural match for any sort of concurrent programming • Isn't concurrent programming all about high performance anyways??? "What the hardware giveth, the software taketh away."
All? • Python is a very high level language • And it comes with a large library • Useful data types (dictionaries, lists,etc.) • Network protocols • Text parsing (regexs, XML, HTML, etc.) • Files and the file system • Databases • Programmers like using this stuff... 19
• Python is often used as a high-level framework • The various components might be a mix of languages (Python, C, C++, etc.) • Concurrency may be a core part of the framework's overall architecture • Python has to deal with it even if a lot of the underlying processing is going on in C 20
are often able to get complex systems to "work" in much less time using a high-level language like Python than if they're spending all of their time hacking C code. 21 "The best performance improvement is the transition from the nonworking to the working state." - John Ousterhout "You can always optimize it later." - Unknown "Premature optimization is the root of all evil." - Donald Knuth
Many concurrent programs are "I/O bound" • They spend virtually all of their time sitting around waiting • Python can "wait" just as fast as C (maybe even faster--although I haven't measured it). • If there's not much processing, who cares if it's being done in an interpreter? (One exception : if you need an extremely rapid response time as in real-time systems) 22
• Python can be extended with C code • Look at ctypes, Cython, Swig, etc. • If you need really high-performance, you're not coding Python--you're using C extensions • This is what most of the big scientific computing hackers are doing • It's called "using the right tool for the job" 23
usually a really bad option if you're merely trying to make an inefficient Python script run faster • Because its interpreted, you can often make huge gains by focusing on better algorithms or offloading work into C extensions • For example, a C extension might make a script run 20x faster vs. the marginal improvement of parallelizing a slow script to run on a couple of CPU cores 24
most programmers think of when they hear about "concurrent programming" • An independent task running inside a program • Shares resources with the main program (memory, files, network connections, etc.) • Has its own independent flow of execution (stack, current instruction, etc.) 26
python program.py statement statement ... create thread(foo) def foo(): statement statement ... statement statement ... return or exit statement statement ... Key idea: Thread is like a little "task" that independently runs inside your program thread
threads are defined by a class import time import threading class CountdownThread(threading.Thread): def __init__(self,count): threading.Thread.__init__(self) self.count = count def run(self): while self.count > 0: print "Counting down", self.count self.count -= 1 time.sleep(5) return • You inherit from Thread and redefine run() 32
threads are defined by a class import time import threading class CountdownThread(threading.Thread): def __init__(self,count): threading.Thread.__init__(self) self.count = count def run(self): while self.count > 0: print "Counting down", self.count self.count -= 1 time.sleep(5) return • You inherit from Thread and redefine run() 33 This code executes in the thread
Alternative method of launching threads def countdown(count): while count > 0: print "Counting down", count count -= 1 time.sleep(5) t1 = threading.Thread(target=countdown,args=(10,)) t1.start() • Creates a Thread object, but its run() method just calls the given function 35
Once you start a thread, it runs independently • Use t.join() to wait for a thread to exit t.start() # Launch a thread ... # Do other work ... # Wait for thread to finish t.join() # Waits for thread t to exit • This only works from other threads • A thread can't join itself 36
a thread runs forever, make it "daemonic" t.daemon = True t.setDaemon(True) • If you don't do this, the interpreter will lock when the main thread exits---waiting for the thread to terminate (which never happens) • Normally you use this for background tasks 37
is really easy • You can create thousands of them if you want • Programming with threads is hard • Really hard 38 Q: Why did the multithreaded chicken cross the road? A: to To other side. get the -- Jason Whittington
• Threads share all of the data in your program • Thread scheduling is non-deterministic • Operations often take several steps and might be interrupted mid-stream (non-atomic) • Thus, access to any kind of shared data is also non-deterministic (which is a really good way to have your head explode) 39
Consider a shared object x = 0 • And two threads that modify it Thread-1 -------- ... x = x + 1 ... Thread-2 -------- ... x = x - 1 ... • It's possible that the resulting value will be unpredictably corrupted 40
Low level interpreter code Thread-1 -------- LOAD_GLOBAL 1 (x) LOAD_CONST 2 (1) BINARY_ADD STORE_GLOBAL 1 (x) Thread-2 -------- LOAD_GLOBAL 1 (x) LOAD_CONST 2 (1) BINARY_SUB STORE_GLOBAL 1 (x) thread switch 42 thread switch These operations get performed with a "stale" value of x. The computation in Thread-2 is lost.
Is this actually a real concern? x = 0 # A shared value def foo(): global x for i in xrange(100000000): x += 1 def bar(): global x for i in xrange(100000000): x -= 1 t1 = threading.Thread(target=foo) t2 = threading.Thread(target=bar) t1.start(); t2.start() t1.join(); t2.join() # Wait for completion print x # Expected result is 0 43 • Yes, the print produces a random nonsensical value each time (e.g., -83412 or 1627732)
corruption of shared data due to thread scheduling is often known as a "race condition." • It's often quite diabolical--a program may produce slightly different results each time it runs (even though you aren't using any random numbers) • Or it may just flake out mysteriously once every two weeks 44
and fixing a race condition will make you a better programmer (e.g., it "builds character") • However, you'll probably never get that month of your life back... • To fix : You have to synchronize threads 45
my experience, there is often a lot of confusion concerning the intended use of the various synchronization objects • Maybe because this is where most students "space out" in their operating system course (well, yes actually) • Anyways, let's take a little tour 48
Exclusion Lock m = threading.Lock() • Probably the most commonly used synchronization primitive • Primarily used to synchronize threads so that only one thread can make modifications to shared data at any given time 49
are two basic operations m.acquire() # Acquire the lock m.release() # Release the lock • Only one thread can successfully acquire the lock at any given time • If another thread tries to acquire the lock when its already in use, it gets blocked until the lock is released 50
• Commonly used to enclose critical sections x = 0 x_lock = threading.Lock() 51 Thread-1 -------- ... x_lock.acquire() x = x + 1 x_lock.release() ... Thread-2 -------- ... x_lock.acquire() x = x - 1 x_lock.release() ... Critical Section • Only one thread can execute in critical section at a time (lock gives exclusive access)
• It is your responsibility to identify and lock all "critical sections" 52 x = 0 x_lock = threading.Lock() Thread-1 -------- ... x_lock.acquire() x = x + 1 x_lock.release() ... Thread-2 -------- ... x = x - 1 ... If you use a lock in one place, but not another, then you're missing the whole point. All modifications to shared state must be enclosed by lock acquire()/release().
locks must always be released • However, it gets evil with exceptions and other non-linear forms of control-flow • Always try to follow this prototype: 54 x = 0 x_lock = threading.Lock() # Example critical section x_lock.acquire() try: statements using x finally: x_lock.release()
2.6/3.0 has an improved mechanism for dealing with locks and critical sections 55 x = 0 x_lock = threading.Lock() # Critical section with x_lock: statements using x ... • This automatically acquires the lock and releases it when control enters/exits the associated block of statements
Don't write code that acquires more than one mutex lock at a time 56 x = 0 y = 0 x_lock = threading.Lock() y_lock = threading.Lock() with x_lock: statements using x ... with y_lock: statements using x and y ... • This almost invariably ends up creating a program that mysteriously deadlocks (even more fun to debug than a race condition)
Lock m = threading.RLock() # Create a lock m.acquire() # Acquire the lock m.release() # Release the lock • Similar to a normal lock except that it can be reacquired multiple times by the same thread • However, each acquire() must have a release() • Common use : Code-based locking (where you're locking function/method execution as opposed to data access) 57
a kind of "monitor" object class Foo(object): lock = threading.RLock() def bar(self): with Foo.lock: ... def spam(self): with Foo.lock: ... self.bar() ... 58 • Only one thread is allowed to execute methods in the class at any given time • However, methods can call other methods that are holding the lock (in the same thread)
synchronization primitive m = threading.Semaphore(n) # Create a semaphore m.acquire() # Acquire m.release() # Release • acquire() - Waits if the count is 0, otherwise decrements the count and continues • release() - Increments the count and signals waiting threads (if any) • Unlike locks, acquire()/release() can be called in any order and by any thread 59
control. You can limit the number of threads performing certain operations. For example, performing database queries, making network connections, etc. • Signaling. Semaphores can be used to send "signals" between threads. For example, having one thread wake up another thread. 60
a semaphore to limit resources sema = threading.Semaphore(5) # Max: 5-threads def fetch_page(url): sema.acquire() try: u = urllib.urlopen(url) return u.read() finally: sema.release() 61 • In this example, only 5 threads can be executing the function at once (if there are more, they will have to wait)
a semaphore to signal done = threading.Semaphore(0) 62 ... statements statements statements done.release() done.acquire() statements statements statements ... Thread 1 Thread 2 • Here, acquire() and release() occur in different threads and in a different order • Often used with producer-consumer problems
e = threading.Event() e.isSet() # Return True if event set e.set() # Set event e.clear() # Clear event e.wait() # Wait for event • This can be used to have one or more threads wait for something to occur • Setting an event will unblock all waiting threads simultaneously (if any) • Common use : barriers, notification 63
Objects cv = threading.Condition([lock]) cv.acquire() # Acquire the underlying lock cv.release() # Release the underlying lock cv.wait() # Wait for condition cv.notify() # Signal that a condition holds cv.notifyAll() # Signal all threads waiting 66 • A combination of locking/signaling • Lock is used to protect code that establishes some sort of "condition" (e.g., data available) • Signal is used to notify other threads that a "condition" has changed state
Use : Producer/Consumer patterns items = [] items_cv = threading.Condition() 67 item = produce_item() with items_cv: items.append(item) with items_cv: ... x = items.pop(0) # Do something with x ... Producer Thread Consumer Thread • First, you use the locking part of a CV synchronize access to shared data (items)
Use : Producer/Consumer patterns items = [] items_cv = threading.Condition() 68 item = produce_item() with items_cv: items.append(item) items_cv.notify() with items_cv: while not items: items_cv.wait() x = items.pop(0) # Do something with x ... Producer Thread Consumer Thread • Next you add signaling and waiting • Here, the producer signals the consumer that it put data into the shared list
tricky bits involving wait() 69 with items_cv: while not items: items_cv.wait() x = items.pop(0) # Do something with x ... Consumer Thread • Before waiting, you have to acquire the lock • wait() releases the lock when waiting and reacquires when woken • Conditions are often transient and may not hold by the time wait() returns. So, you must always double-check (hence, the while loop)
all of the synchronization primitives is a lot trickier than it looks • There are a lot of nasty corner cases and horrible things that can go wrong • Bad performance, deadlock, livelock, starvation, bizarre CPU scheduling, etc... • All are valid reasons to not use threads 70
Threaded programs are often easier to manage if they can be organized into producer/ consumer components connected by queues 72 Thread 1 (Producer) Thread 2 (Consumer) Queue send(item) • Instead of "sharing" data, threads only coordinate by sending data to each other • Think Unix "pipes" if you will...
Python has a thread-safe queuing module • Basic operations from Queue import Queue q = Queue([maxsize]) # Create a queue q.put(item) # Put an item on the queue q.get() # Get an item from the queue q.empty() # Check if empty q.full() # Check if full 73 • Usage : You try to strictly adhere to get/put operations. If you do this, you don't need to use other synchronization primitives.
commonly used to set up various forms of producer/consumer problems for item in produce_items(): q.put(item) 74 while True: item = q.get() consume_item(item) from Queue import Queue q = Queue() Producer Thread Consumer Thread • Critical point : You don't need locks here
also have a signaling mechanism q.task_done() # Signal that work is done q.join() # Wait for all work to be done 75 • Many Python programmers don't know about this (since it's relatively new) • Used to determine when processing is done for item in produce_items(): q.put(item) # Wait for consumer q.join() while True: item = q.get() consume_item(item) q.task_done() Producer Thread Consumer Thread
are many ways to use queues • You can have as many consumers/producers as you want hooked up to the same queue 76 Queue producer producer producer consumer consumer • In practice, try to keep it simple
Thread programming quickly gets hairy • End up with a huge mess of shared data, locks, queues, and other synchronization primitives • Which is really unfortunate because Python threads have some major limitations • Namely, they have pathological performance! 78
Consider this CPU-bound function def count(n): while n > 0: n -= 1 79 • Sequential Execution: count(100000000) count(100000000) • Threaded execution t1 = Thread(target=count,args=(100000000,)) t1.start() t2 = Thread(target=count,args=(100000000,)) t2.start() • Now, you might expect two threads to run twice as fast on multiple CPU cores
comparison (Dual-Core 2Ghz Macbook, OS-X 10.5.6) 80 Sequential : 24.6s Threaded : 45.5s (1.8X slower!) • If you disable one of the CPU cores... Threaded : 38.0s • Insanely horrible performance. Better performance with fewer CPU cores? It makes no sense.
this point that programmers often decide to abandon threads altogether • Or write a blog rant that vaguely describes how Python threads "suck" because of their failed attempt at Python supercomputing • Well, yes there is definitely some "suck" going on, but let's dig a little deeper... 81
• Python threads are real system threads • POSIX threads (pthreads) • Windows threads • Fully managed by the host operating system • All scheduling/thread switching • Represent threaded execution of the Python interpreter process (written in C) 83
Here's the rub... • Only one Python thread can execute in the interpreter at once • There is a "global interpreter lock" that carefully controls thread execution • The GIL ensures that sure each thread gets exclusive access to the entire interpreter internals when it's running 84
a thread runs, it holds the GIL • However, the GIL is released on blocking I/O 85 I/O I/O I/O release acquire release acquire acquire release • So, any time a thread is forced to wait, other "ready" threads get their chance to run • Basically a kind of "cooperative" multitasking run run run run acquire
To deal with CPU-bound threads, the interpreter periodically performs a "check" • By default, every 100 interpreter "ticks" 86 CPU Bound Thread Run 100 ticks Run 100 ticks Run 100 ticks check check check
The check interval is a global counter that is completely independent of thread scheduling 87 Main Thread 100 ticks check check check 100 ticks 100 ticks Thread 2 Thread 3 Thread 4 100 ticks • A "check" is simply made every 100 "ticks"
What happens during the periodic check? • In the main thread only, signal handlers will execute if there are any pending signals • Release and reacquisition of the GIL • That last bullet describes how multiple CPU- bound threads get to run (by briefly releasing the GIL, other threads get a chance to run). 88
does not have a thread scheduler • There is no notion of thread priorities, preemption, round-robin scheduling, etc. • For example, the list of threads in the interpreter isn't used for anything related to thread execution • All thread scheduling is left to the host operating system (e.g., Linux, Windows, etc.) 91
GIL is not a simple mutex lock • The implementation (Unix) is either... • A POSIX unnamed semaphore • Or a pthreads condition variable • All interpreter locking is based on signaling • To acquire the GIL, check if it's free. If not, go to sleep and wait for a signal • To release the GIL, free it and signal 92
switching is far more subtle than most programmers realize (it's tied up in the OS) 93 Thread 1 100 ticks check check check 100 ticks Thread 2 ... Operating System signal signal SUSPENDED Thread Context Switch check • The lag between signaling and scheduling may be significant (depends on the OS) SUSPENDED signal signal check signal
we saw earlier, CPU-bound threads have horrible performance properties • Far worse than simple sequential execution • 24.6 seconds (sequential) • 45.5 seconds (2 threads) • A big question : Why? • What is the source of that overhead? 94
thread signaling is the source of that • After every 100 ticks, the interpreter • Locks a mutex • Signals on a condition variable/semaphore where another thread is always waiting • Because another thread is waiting, extra pthreads processing and system calls get triggered to deliver the signal 95
Sequential Execution (OS-X, 1 CPU) • 736 Unix system calls • 117 Mach System Calls • Two threads (OS-X, 1 CPU) • 1149 Unix system calls • ~ 3.3 Million Mach System Calls • Yow! Look at that last figure. 96
The penalty gets far worse on multiple cores • Two threads (OS-X, 1 CPU) • 1149 Unix system calls • ~3.3 Million Mach System Calls • Two threads (OS-X, 2 CPUs) • 1149 Unix system calls • ~9.5 Million Mach System calls 97
With multiple cores, CPU-bound threads get scheduled simultaneously (on different processors) and then have a GIL battle 98 Thread 1 (CPU 1) Thread 2 (CPU 2) Release GIL signal Acquire GIL Wake Acquire GIL (fails) Release GIL Acquire GIL signal Wake Acquire GIL (fails) run run run • The waiting thread (T2) may make 100s of failed GIL acquisitions before any success
Code • As mentioned, Python can talk to C/C++ • C/C++ extensions can release the interpreter lock and run independently • Caveat : Once released, C code shouldn't do any processing related to the Python interpreter or Python objects • The C code itself must be thread-safe 99
Extensions • Having C extensions release the GIL is how you get into true "parallel computing" 100 Thread 1: Thread 2 Python instructions Python instructions C extension code GIL release GIL acquire Python instructions GIL release GIL acquire
GIL • The ctypes module already releases the GIL when calling out to C code • In hand-written C extensions, you have to insert some special macros 101 PyObject *pyfunc(PyObject *self, PyObject *args) { ... Py_BEGIN_ALLOW_THREADS // Threaded C code ... Py_END_ALLOW_THREADS ... }
Extensions • The trouble with C extensions is that you have to make sure they do enough work • A dumb example (mindless spinning) 102 void churn(int n) { while (n > 0) { n--; } } • How big do you have to make n to actually see any kind of speedup on multiple cores?
there? • Simplifies the implementation of the Python interpreter (okay, sort of a lame excuse) • Better suited for reference counting (Python's memory management scheme) • Simplifies the use of C/C++ extensions. Extension functions do not need to worry about thread synchronization • And for now, it's here to stay... (although people continue to try and eliminate it) 105
Threads are still useful for I/O-bound apps • For example : A network server that needs to maintain several thousand long-lived TCP connections, but is not doing tons of heavy CPU processing • Here, you're really only limited by the host operating system's ability to manage and schedule a lot of threads • Most systems don't have much of a problem-- even with thousands of threads 108
everything is I/O-bound, you will get a very quick response time to any I/O activity • Python isn't doing the scheduling • So, Python is going to have a similar response behavior as a C program with a lot of I/O bound threads • Caveat: You have to stay I/O bound! 109
threads are a useful tool, but you have to know how and when to use them • I/O bound processing only • Limit CPU-bound processing to C extensions (that release the GIL) • Threads are not the only way... 110
An alternative to threads is to run multiple independent copies of the Python interpreter • In separate processes • Possibly on different machines • Get the different interpreters to cooperate by having them send messages to each other 112
Python send() recv() pipe/socket • On the surface, it's simple • Each instance of Python is independent • Programs just send and receive messages • Two main issues • What is a message? • What is the transport mechanism?
module for serializing objects 115 • Serializing an object onto a "file" import pickle ... pickle.dump(someobj,f) • Unserializing an object from a file someobj = pickle.load(f) • Here, a file might be a file, a pipe, a wrapper around a socket, etc.
can also turn objects into byte strings import pickle # Convert to a string s = pickle.dumps(someobj) ... # Load from a string someobj = pickle.loads(s) • You might use this embed a Python object into a message payload 116
There is an alternative implementation of pickle called cPickle (written in C) • Use it whenever possible--it is much faster 117 import cPickle as pickle ... pickle.dump(someobj,f) • There is some history involved. There are a few things that cPickle can't do, but they are somewhat obscure (so don't worry about it)
pickle is almost too easy • Almost any Python object works • Builtins (lists, dicts, tuples, etc.) • Instances of user-defined classes • Recursive data structures • Exceptions • Files and network connections • Running generators, etc. 118
a subprocess and hooking up the child process via a pipe • Use the subprocess module 120 import subprocess p = subprocess.Popen(['python','child.py'], stdin=subprocess.PIPE, stdout=subprocess.PIPE) p.stdin.write(data) # Send data to subprocess p.stdout.read(size) # Read data from subprocess Python p.stdin p.stdout Python sys.stdin sys.stdout Pipe
Most programmers would use the subprocess module to run separate programs and collect their output (e.g., system commands) • However, if you put a pickling layer around the files, it becomes much more interesting • Becomes a communication channel where you can send just about any Python object 121
Using the child worker 124 >>> ch.send("Hello World") Hello World >>> ch.send(42) 42 >>> ch.send([1,2,3,4]) [1, 2, 3, 4] >>> ch.send({'host':'python.org','port':80}) {'host': 'python.org', 'port': 80} >>> This output is being produced by the child • You can send almost any Python object (numbers, lists, dictionaries, instances, etc.)
is a fairly general concept • However, it's also kind of nebulous in Python • No agreed upon programming interface • Vast number of implementation options • Intersects with distributed objects, RPC, cross-language messaging, etc. 126
new library module added in Python 2.6 • Originally known as pyprocessing (a third- party extension module) • This is a module for writing concurrent Python programs based on communicating processes • A module that is especially useful for concurrent CPU-bound processing 128
the cool part... • You already know how to use multiprocessing • At a very high-level, it simply mirrors the thread programming interface • Instead of "Thread" objects, you now work with "Process" objects. 129
tasks using a Process class import time import multiprocessing class CountdownProcess(multiprocessing.Process): def __init__(self,count): multiprocessing. Process.__init__(self) self.count = count def run(self): while self.count > 0: print "Counting down", self.count self.count -= 1 time.sleep(5) return • You inherit from Process and redefine run() 130
launch, same idea as with threads if __name__ == '__main__': p1 = CountdownProcess(10) # Create the process object p1.start() # Launch the process p2 = CountdownProcess(20) # Create another process p2.start() # Launch • Processes execute until run() stops • A critical detail : Always launch in main as shown (required for Windows) 131
Alternative method of launching processes def countdown(count): while count > 0: print "Counting down", count count -= 1 time.sleep(5) if __name__ == '__main__': p1 = multiprocessing.Process(target=countdown, args=(10,)) p1.start() • Creates a Process object, but its run() method just calls the given function 132
Consider this CPU-bound function def count(n): while n > 0: n -= 1 133 • Sequential Execution: count(100000000) count(100000000) • Multiprocessing Execution p1 = Process(target=count,args=(100000000,)) p1.start() p2 = Process(target=count,args=(100000000,)) p2.start() 24.6s 12.5s • Yes, it seems to work
Joining a process (waits for termination) p = Process(target=somefunc) p.start() ... p.join() • Making a daemonic process 134 p = Process(target=somefunc) p.daemon = True p.start() • Terminating a process p = Process(target=somefunc) ... p.terminate() p = Process(target=somefunc) • These mirror similar thread functions
multiprocessing, there are no shared data structures • Every process is completely isolated • Since there are no shared structures, forget about all of that locking business • Everything is focused on messaging 135 p = Process(target=somefunc)
for sending/receiving objects 136 p = Process(target=somefunc) (c1, c2) = multiprocessing.Pipe() • Returns a pair of connection objects (one for each end-point of the pipe) • Here are methods for communication c.send(obj) # Send an object c.recv() # Receive an object c.send_bytes(buffer) # Send a buffer of bytes c.recv_bytes([max]) # Receive a buffer of bytes c.poll([timeout]) # Check for data
Pipe() function largely mimics the behavior of Unix pipes • However, it operates at a higher level • It's not a low-level byte stream • You send discrete messages which are either Python objects (pickled) or buffers 137 p = Process(target=somefunc)
= Process(target=somefunc) def consumer(p1, p2): p1.close() # Close producer's end (not used) while True: try: item = p2.recv() except EOFError: break print item # Do other useful work here • A simple data consumer • A simple data producer def producer(sequence, output_p): for item in sequence: output_p.send(item)
= Process(target=somefunc) if __name__ == '__main__': p1, p2 = multiprocessing.Pipe() cons = multiprocessing.Process( target=consumer, args=(p1,p2)) cons.start() # Close the input end in the producer p2.close() # Go produce some data sequence = xrange(100) # Replace with useful data producer(sequence, p1) # Close the pipe p1.close()
also provides a queue • The programming interface is the same 140 p = Process(target=somefunc) from multiprocessing import Queue q = Queue() q.put(item) # Put an item on the queue item = q.get() # Get an item from the queue • There is also a joinable Queue from multiprocessing import JoinableQueue q = JoinableQueue() q.task_done() # Signal task completion q.join() # Wait for completion
are implemented on top of pipes • A subtle feature of queues is that they have a "feeder thread" behind the scenes • Putting an item on a queue returns immediately (allowing the producer to keep working) • The feeder thread works on its own to transmit data to consumers 141 p = Process(target=somefunc)
consumer process 142 p = Process(target=somefunc) def consumer(input_q): while True: # Get an item from the queue item = input_q.get() # Process item print item # Signal completion input_q.task_done() • A producer process def producer(sequence,output_q): for item in sequence: # Put the item on the queue output_q.put(item)
the two processes 143 p = Process(target=somefunc) if __name__ == '__main__': from multiprocessing import Process, JoinableQueue q = JoinableQueue() # Launch the consumer process cons_p = Process(target=consumer,args=(q,)) cons_p.daemon = True cons_p.start() # Run the producer function on some data sequence = range(100) # Replace with useful data producer(sequence,q) # Wait for the consumer to finish q.join()
have written threaded programs that strictly stick to the queuing model, they can probably be ported to multiprocessing • The following restrictions apply • Only objects compatible with pickle can be queued • Tasks can not rely on any shared data other than a reference to the queue 144 p = Process(target=somefunc)
has many other features • Process Pools • Shared objects and arrays • Synchronization primitives • Managed objects • Connections • Will briefly look at one of them 145 p = Process(target=somefunc)
a process pool 146 p = Process(target=somefunc) p = multiprocessing.Pool([numprocesses]) • Pools provide a high-level interface for executing functions in worker processes • Let's look at an example...
a function that does some work • Example : Compute a SHA-512 digest of a file 147 p = Process(target=somefunc) import hashlib def compute_digest(filename): digest = hashlib.sha512() f = open(filename,'rb') while True: chunk = f.read(8192) if not chunk: break digest.update(chunk) f.close() return digest.digest() • This is just a normal function (no magic)
is some code that uses our function • Make a dict mapping filenames to digests 148 p = Process(target=somefunc) import os TOPDIR = "/Users/beazley/Software/Python-3.0" digest_map = {} for path, dirs, files in os.walk(TOPDIR): for name in files: fullname = os.path.join(path,name) digest_map[fullname] = compute_digest(fullname) • Running this takes about 10s on my machine
a pool, you can farm out work • Here's a small sample 149 p = Process(target=somefunc) p = multiprocessing.Pool(2) # 2 processes result = p.apply_async(compute_digest,('README.txt',)) ... ... various other processing ... digest = result.get() # Get the result • This executes a function in a worker process and retrieves the result at a later time • The worker churns in the background allowing the main program to do other things
a dictionary mapping names to digests 150 p = Process(target=somefunc) import multiprocessing import os TOPDIR = "/Users/beazley/Software/Python-3.0" p = multiprocessing.Pool(2) # Make a process pool digest_map = {} for path, dirs, files in os.walk(TOPDIR): for name in files: fullname = os.path.join(path,name) digest_map[fullname] = p.apply_async( compute_digest, (fullname,) ) # Go through the final dictionary and collect results for filename, result in digest_map.items(): digest_map[filename] = result.get() • This runs in about 5.6 seconds
kinds of applications, programmers have turned to alternative approaches that don't rely on threads or processes • Primarily this centers around asynchronous I/O and I/O multiplexing • You try to make a single Python process run as fast as possible without any thread/process overhead (e.g., context switching, stack space, and so forth) 152 p = Process(target=somefunc)
seems to be two schools of thought... • Event-driven programming • Turn all I/O handling into events • Do everything through event handlers • asyncore, Twisted, etc. • Coroutines • Cooperative multitasking all in Python • Tasklets, green threads, etc. 153 p = Process(target=somefunc)
asyncore library module • Implements a wrapper around sockets that turn all blocking I/O operations into events 154 p = Process(target=somefunc) s = socket(...) s.accept() s.connect(addr) s.recv(maxbytes) s.send(msg) ... from asyncore import dispatcher class MyApp(dispatcher): def handle_accept(self): ... def handle_connect(self): ... def handle_read(self): ... def handle_write(self): ... # Create a socket and wrap it s = MyApp(socket())
To run, asyncore provides a central event loop based on I/O multiplexing (select/poll) 155 p = Process(target=somefunc) import asyncore asyncore.loop() # Run the event loop Event Loop socket socket socket socket dispatcher select()/poll() handle_*()
asyncore is one of the ugliest, most annoying, mind-boggling modules in the entire Python library • Combines all of the "fun" of network programming with the "elegance" of GUI programming (sic) • However, if you use this module, you can technically create programs that have "concurrency" without any threads/processes 156 p = Process(target=somefunc)
concurrency approach is possible using Python generator functions (coroutines) • This is a little subtle, but I'll give you the gist • First, a quick refresher on generators 157 p = Process(target=somefunc)
functions are commonly used to feed values to for-loops (iteration) 158 p = Process(target=somefunc) def countdown(n): while n > 0: yield n n -= 1 for x in countdown(10): print x • Under the covers, the countdown function executes on successive next() calls >>> c = countdown(10) >>> c.next() 10 >>> c.next() 9 >>>
a generator function hits the yield statement, it suspends execution 159 p = Process(target=somefunc) def countdown(n): while n > 0: yield n n -= 1 • Here's the idea : Instead of yielding a value, a generator can yield control • You can write a little scheduler that cycles between generators, running each one until it explicitly yields
you set up a set of "tasks" 160 p = Process(target=somefunc) def countdown_task(n): while n > 0: print n yield n -= 1 # A list of tasks to run from collections import deque tasks = deque([ countdown_task(5), countdown_task(10), countdown_task(15) ]) • Each task is a generator function
run a task scheduler 161 p = Process(target=somefunc) def scheduler(tasks): while tasks: task = tasks.popleft() try: next(task) # Run to the next yield tasks.append(task) # Reschedule except StopIteration: pass # Run it scheduler(tasks) • This loop is what drives the application
It is also possible to tie coroutines to I/O • You take an event loop (like asyncore), but instead of firing callback functions, you schedule coroutines in response to I/O activity 163 p = Process(target=somefunc) Scheduler loop socket socket socket socket coroutine select()/poll() next() • Unfortunately, this requires its own tutorial...
of coroutines is somewhat exotic • Mainly due to poor documentation and the "newness" of the feature itself • There are also some grungy aspects of programming with generators 164 p = Process(target=somefunc)
gave a tutorial that goes into more detail • "A Curious Course on Coroutines and Concurrency" at PyCON'09 • http://www.dabeaz.com/coroutines 165 p = Process(target=somefunc)
Covered various options for Python concurrency • Threads • Multiprocessing • Event handling • Coroutines/generators • Hopefully have expanded awareness of how Python works under the covers as well as some of the pitfalls and tradeoffs
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