GParsThe concurrency and parallelism framework

for Groovy and Java applications

Russel Winder

@russel_winder russel@winder.org.uk

http://www.russel.org.uk

Aims and Goals

Convince people that they:– Can deal with parallelism and concurrency without fear.

– Already understand GPars.

– Can use GPars tonight.

– …

…should tremble in fear atthe prospect of using

shared-memory multi-threading

with Java…

…and even with Groovy.

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 activeall at the same time.

Concurrency is a tool for structuringexecution where a single processoris used by multiple computations.

Parallelism is about making acomputation complete faster 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.

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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.

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ActorsIndependent processes communicating via asynchronous exchange of messages

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

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

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We need examples.

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What is the Value of ?

Easy, it's known exactly, it's (obviously).

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It's simples

Александр Орлов 2009

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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

1 1

1x2dx

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One Possible Algorithm

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

=4n∑i=1

n 1

1i−0.5n

2

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

Embarrassingly parallel.

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Commutative and Associative

Because addition is commutative andassociative, expression can be

decomposed into sums of partial sums.

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a + b + c + d + e + f

=

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

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Scatter – Gather

map reduce

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Code…

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But isn't this just data parallelism?

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More code…

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If you want the code, clone the Git repository:

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

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Or if you just want to browse:

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

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The Sleeping Barber Problem

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The Sleeping Barber ProblemThe 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 sits in 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 Edsger Dijkstra.

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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.

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Wikipedia article presents the classic operatingsystems approach using locks and semaphores.

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

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More code…

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If you want the code, clone the Git repository:

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

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Or if you just want to browse:

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

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Squirrels deny parallelism.

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Squirrels deny performance enhancement.

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Don't be a squirrel.

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Do not use explicit locking algorithms.

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Use computational architectures that promoteparallelism and hence performance improvement:

Actors

Dataflow

CSP

Data Parallelism

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Use GPars.

Go on, you know you want to…