Beating the (sh** out of the) GIL - Multithreading vs. Multiprocessing

Threading Theory Multiprocessing Others Conclusion Finalise

Beating the (sh** out of the) GILMultithreading vs. Multiprocessing

Hair dryer 1920s,Dark Roasted Blend:http://www.darkroastedblend.com/2007/01/retro-technology-update.html

Guy K. Kloss | Multithreading vs. Multiprocessing 1/36

Beating the (sh** out of the) GILMultithreading vs. Multiprocessing

Guy K. Kloss

Computer ScienceMassey University, Albany

New Zealand Python User Group MeetingAuckland, 12 June 2008

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Source: http://blog.snaplogic.org/?cat=29Guy K. Kloss | Multithreading vs. Multiprocessing 6/36

What People Think Now

Threading and shared memory are common(thanks to Windows and Java)Python supports threads (Yay!)Python also supports easy forking (Yay!)The GIL . . . is a problem for pure Python,non I/O bound applicationsLots of people “understand” threads . . .. . . and fail at them (to do them properly)

Blog post by Mark Ramm, 14 May 2008A multi threaded system is particularly important for peoplewho use Windows, which makes multi–process computingmuch more memory intensive than it needs to be. As mygrandma always said Windows can’t fork worth a damn. ;)[. . . ]So, really it’s kinda like shared–memory optimizedmicro–processes running inside larger OS level processes, andthat makes multi–threaded applications a lot morereasonable to wrap your brain around. Once you start downthe path of lock managment the non-deterministic characterof the system can quickly overwhelm your brain.

Simple Threading Example

from threading import Threadfrom stuff import expensiveFunction

class MyClass(Thread):def __init__(self, argument):

self.argument = argumentThread.__init__(self) # I n i t i a l i s e the thread

def run(self):self.value = expensiveFunction(self.argument)

callObjects = []for i in range(config.segments):

callObjects.append(MyClass(i))

for item in callObjects:item.start()

# Do something e lse .time.sleep(15.0)

for item in callObjects:item.join()print item.value

Our Example with Threading

Our fractal examplenow with threading.

Just a humble hair–dryer from the30s: “One of the first machines usedfor permanent wave hairstyling backin the 1920’s and 1930’s.”Dark Roasted Blend:http://www.darkroastedblend.com/2007/05/

mystery-devices-issue-2.html

The GIL

Global Interpreter LockWhat is it for?

Cooperative multitaskingInterpreter knows when it’s “good to switch”Often more efficient than preemptive multi–taskingCan be released from native (C) code extensions(done for I/O intensive operations)

Is it good?Easy codingEasy modules/extensionsLarge base of available modules alredySpeed improvement by factor 2(for single–threaded applications)Keeps code safe

The GIL

Global Interpreter LockWhat is it for?

Cooperative multitaskingInterpreter knows when it’s “good to switch”Often more efficient than preemptive multi–taskingCan be released from native (C) code extensions(done for I/O intensive operations)

Is it good?Easy codingEasy modules/extensionsLarge base of available modules alredySpeed improvement by factor 2(for single–threaded applications)Keeps code safe

The GILAlternatives

Other implementations(C) Python uses itJython doesn’tIronPython doesn’tThey use their own/internal threading mechanisms

Is it a design flaw?Maybe . . . but . . .Fierce/intense discussions to change the code baseSolutions that pose other benefits:

Processes create fewer inherent dead lock situationsProcesses scale also to multi–host scenarios

The GILAlternatives

Other implementations(C) Python uses itJython doesn’tIronPython doesn’tThey use their own/internal threading mechanisms

Is it a design flaw?Maybe . . . but . . .Fierce/intense discussions to change the code baseSolutions that pose other benefits:

Processes create fewer inherent dead lock situationsProcesses scale also to multi–host scenarios

Doug Hellmann in Python Magazine 10/2007:Techniques using low–level, operating system–specific,libraries for process management are as passe as usingcompiled languages for CGI programming. I don’t have timefor this low–level stuff any more, and neither do you. Let’slook at some modern alternatives.

GIL–less Python

There was an attempt/patch “way back then ...”There’s a new project now by Adam OlsenPython 3000 with “free theading” [1]Using Monitors to isolate stateDesign focus: usability(for common cases, maintainable code)Optional at compile time using --with-freethread

Sacrificed single–threaded performance(60–65% but equivalent to threaded CPython)Automatic deadlock detection(detection/breaking, giving exceptions/stack trace)Runs on Linux and OS/X

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Parallelisation in General

CPU vs. I/O bottle necksThreading: Good for I/O constrainsThis talk aims at CPU constrains

Threads vs. ProcessesThreads: Within a process on one hostProcesses: Independent on the OSProcesses are:

Heavier in memory/overheadHave their own name space and memoryInvolve less problems with competing accessto resources and their management

But:On UN*X/Linux: Process overhead is very low(C)Python is inefficient in handling threadsStackless Python is much more efficient on threading

Abstraction Level vs. Control

Abstraction levels for parallel computing models [7]

Parallelism Communication Synchronisation4 implicit3 explicit implicit2 explicit implicit1 explicit

Explicit: The programmer specifies it in the parallel programImplicit: A compiler/runtime system derives it from other information

Low level: Close to hardwareMust specify parallelism. . . communication. . . and synchronisation

→ Best means for performance tuning→ Premature optimisation?High level: Highest machine independence

More/all handled by computing modelUp to automatic parallelisation approaches

Both extremes have not been very successful to dateMost developments now:

Level 3 for specific purposesLevel 1 for general programming(esp. in the scientific community)With Python consistent level 2 possible

Common for Parallel Computing

Message Passing Interface (MPI)for distributed memoryOpenMPshared memory multi–threadingThe two do not have to be categorised like this

Art by “Teknika Molodezhi,” Russia 1966

Dark Roasted Blend: http://www.darkroastedblend.com/2008/01/retro-future-mind-boggling.htmlGuy K. Kloss | Multithreading vs. Multiprocessing 20/36

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Processing around the GIL

Smart multi–processingSmart task farming

(py)Processing module

By R. Oudkerk [2]Written in C (really fast!)Allowes multiple cores and multiple hosts/clustersData synchronisation through managersEasy “upgrade path”

Drop in replacement (mostly) for the threading moduleTransparent to userForks processes, but uses Thread API

Supports queues, pipes, locks,managers (for sharing state), worker poolsVERY fast, see PEP-371 [3]

Jesse Noller for pyprocessing into core Pythonbenchmarks available, awesome results!PEP is officially accepted: Thanks Guido!

(py)Processing module(continued)

Some detailsProducer/consumer style system– workers pull jobsHides most details of communication– usable default settingsCommunication is tweakable(to improve performance or meet certain requirements)

(py)Processing module

Let’s see it!

Parallel Python module

By Vitalii Vanovschi [4]Pure PythonFull “Batteries included” paradigm model:

Spawns automatically across detected cores,and can spawn to clustersUses some thread module methods under the hoodMore of a “task farming” approach(requires potentially rethinking/restructuring)Automatically deploys code and data,no difficult/multiple installsFault tolerance, secure inter–node communication,runs everywhere

Very active communigy,good documentation, good support

Parallel Python module

Let’s see it!

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Honourable Mentions

pprocess [5]IPython for parallel computing [6]Bulk Synchronous Parallel (BSP) Model [7]sequence of super steps(computation, communication, barrier synch)Reactor based architectures, through Twisted [8]“Don’t call us, we call you”MPI (pyMPI, Pypar, MPI for Python, pypvm)requires constant number of processors duringcompation’s durationPyro (distributed object system)Linda (PyLinda)Scientific Python (master/slave computing model)data distribution through call parameters/replication

Outline

1 Threading

2 Theory

3 Multiprocessing

4 Others

5 Conclusion

Things to Note

Which approach is best?Can’t say!Many of the approaches are complimentaryNeeds to be evaluated what to use when

All, however, save you a lot of time over the alternativeof writing everything yourself with low–level libraries.What an age to be alive!Problems can arise when objects cannot be pickled

Conclusion

Resolving the GIL is not necessarily the best solutionMore inefficient (single threaded) runtimeProblems with shared memory access

Various approaches to beat the GILSolutions are complimentary in many waysmany scale beyond a local machine/memory system

Questions?

G.Kloss@massey.ac.nzSlides and code available here:http://www.kloss-familie.de/moin/TalksPresentations

References I

[1] A. Olsen,Python 3000 with Free Threading project,[Online]http://code.google.com/p/python-safethread/

[2] R. Oudkerk,Processing Package,[Online] http://pypi.python.org/pypi/processing/

[3] J. Noller,PEP-371,[Online] http://www.python.org/dev/peps/pep-0371/

References II

[4] V. Vanovschi,Parallel Python,[Online] http://parallelpython.com/

[5] P. Boddie,pprocess,[Online] http://pypi.python.org/pypi/processing/

[6] Project Website,IPython,[Online] http://ipython.scipy.org/doc/ipython1/html/parallel_intro.html

[7] K. Hinsen,Parallel Scripting with PythonComputing in Science & Engineering, Nov/Dec 2007

References III

[8] B. Eckel,Concurrency with Python, Twisted, and Flex,[Online] http://www.artima.com/weblogs/viewpost.jsp?thread=230001

Beating the (sh** out of the) GIL - Multithreading vs. Multiprocessing

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