It's All About Processes Communicating

It's All AboutProcesses Communicating

Russel Winder

email: russel@winder.org.ukxmpp: russel@winder.org.uk

twitter: russel_winder

Aims, Goals and Objects

● Show that shared memory multi-threading should return to being an operating systems development technique and not continue to be pushed as an applications programming technique.

● Show that…

…people should tremble in fear

at the prospect of using

Shared-memorymultithreading.

Structure

A beginning.

A middle.

An end.

Protocol

● Questions or short comments during the session are entirely in order.

● Let me know you have an interjection by raising your hand, and when I come to an appropriate pause, I'll pass you the token.

Questions, answers, comments, etc. appearingto get too long as interjections may get stackedto be unstacked at a break.

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A Beginning

It is no longer contentious thatThe Multicore Revolution

is well underway.

Quad core laptops and phones.

Eight and twelve core workstations.

Servers with “zillions” of cores.

Parallel hardware is the norm.

Software technology is now lagginghardware technology by decades.

Operating systems manage coreswith kernel threads.

Operating systems are fundamentally shared memory multi-threaded systems.

Operating systems rightly use all the lock, semaphore, monitor, etc. technologies.

Computationally intensive systems orsubsystems definitely have to be parallel.

Other systems likely use concurrencybut not parallelism.

Concurrency

Execution as co-routines:

Sequences of code give up the executionto pass it to another coroutine.

More Concurrency

Concurrency is a technique founded in auniprocessor view of the world.

Time-division multiplexing.

Parallelism

Having multiple executions activeat the same time.

Concurrency is a tool for structuring execution where asingle processor is used by multiple computations.

Parallelism is about making a computation completefaster than using a single processor.

Squirrel behaviour emulatessynchronized software behaviour.

Thanks to Paul King…

Who uses synchronized?

Thanks to Paul King…

You did it wrong.

Small addition by me…

Who uses lock objects?

Small addition by me…

You definitely did it wrong.

Locks deny parallelism.

The whole purpose of a lock is toprevent parallelism.

Parallelism is performance improvement.

Performance improvement requires parallelism.

Locks deny performance improvement.

Locks are needed only ifthere is mutable shared state.

Avoid mutable shared state.

Use processes and message passing.

It's all easier if processesare single threaded.

…but how…

Use appropriate architectural models.

A Middle

It's all about controllingconcurrency and parallelism

with tools that programmers find usable.

Shared memory multi-threading isan operating system technique.

Applications and tools programmersneed computational models with

integrated synchronization.

ActorsIndependent processes communicating via asynchronous exchange of messages

CSPSequential processes connected by channels using synchronous message exchange (rendezvous).

DataflowOperators connected by channels with activity triggered by arrival of data on the channels.

ActorsIndependent processes communicating via asynchronous exchange of messages

DataflowOperators connected by channels with activity triggered by arrival of data on the channels.

CSPSequential processes connected by channels using synchronous message exchange (rendezvous).

Need examples.

What is the Value of ?

Easy, it's known exactly.

It's .

Obviously.

It's simples Александр Орлов 2009

Approximating

● What is it's value represented as a floating point number?● We can only obtain an approximation.● A plethora of possible algorithms to choose from, a

popular one is to employ the following integral equation.

4=∫0

One Possible Algorithm

● Use quadrature to estimate the value of the integral – which is the area under the curve.

=4n∑i=1

1i−0.5n

With n = 3 not much to do, but potentially lots of error. Use n = 107 or n = 109?

Embarrassingly parallel.

Because addition is commutative andassociative, expression can be

decomposed into sums of partial sums.

a + b + c + d + e + f

( a + b ) + ( c + d ) + ( e + f )

Scatter – Gather

map reduce

If you want the code, clone the Git repository:

http://www.russel.org.uk/Git/Pi_Quadrature.git

Or if you just want to browse:

http://www.russel.org.uk/gitweb

Need another example.

The Sleeping Barber Problem

● The barber's shop has a single cutting chair and a row of waiting seats.

● The barber sleeps in the cutting chair unless trimming a customer.

● Customers arrive at the shop at intervals.

● If the barber is asleep, the customer wakes the barber takes the cutting chair and gets a trim.

● If the barber is cutting, a new customer checks to see if there is a free waiting seat.

● If there is join the queue to be trimmed.

● If there isn't leave disgruntled.

Problem originally dueto Edsgar Dijkstra.

A new customer enters the shop,check to see if they can go straightto the cutting chair, if not can theytake a waiting chair, if not leave.

The waiting chairs

The cutting chair.

The barber's shop.

Wikipedia article presents the classic operating systems approach using locks and semaphores.

http://en.wikipedia.org/wiki/Sleeping_barber_problem

More code!

If you want the code, clone the Git repository:

http://www.russel.org.uk/Git/SleepingBarber.git

Or if you just want to browse:

http://www.russel.org.uk/gitweb

An End

Multicore and multiprocessor are nowthe norm, not the exception.

Parallelism only matters ifcomputational performance matters.

Unstructured synchronizationof concurrent systems

is not a feasible approach.

Actors, CSP and Datafloware the future of

applications structure.

Passing messages betweenprocesses is the way forward.

Shared memory concurrencyis a dead end for applications.

Squirrels deny parallelism.

Squirrels deny performance enhancement.

Don't be a squirrel.

Do not use explicit locking algorithms.

Use computational architectures that promoteparallelism and hence performance

improvement:

ActorsDataflow

CSPData Parallelism

Go on, you know you want to…

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The End

It's All AboutProcesses Communicating

Russel Winder

email: russel@winder.org.ukxmpp: russel@winder.org.uk

twitter: russel_winder

It's All About Processes Communicating

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