gpars workshop

Copyright © 2013 Russel Winder 1

GPars Workshop

Russel Winder

email: [email protected]: [email protected]

twitter: @russel_winder

http://www.russel.org.uk

mailto:[email protected]

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


Aims, Goals and Objectives

● Gain practical experience of the various models of concurrent and parallel behaviour available in GPars; actors, dataflow, data parallelism, etc.

● Have some fun.


Subsidiary Aims, Goals and Objects

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


…people should tremblein fear at the prospect of using

Shared mutable memorymulti-threading.


Structure

Introduction.

Actors.

Dataflow.

Data Parallelism.

Analysis.

Closing.


Protocol

Short presentation.

(Short presentation Practical period)→ 3

Interaction.

Short presentation.

Questions or comments welcome at any time.


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Introduction


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 capable hardware is the norm.


Software technology in use is now lagginghardware technology by decades.


Operating systems manage coreswith kernel threads.

Operating systems are fundamentally shared mutable 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.


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.


…but how…


Use appropriate architectural models.


It's all about controllingconcurrency and parallelismwith tools that applicationsprogrammers find usable.


Shared mutable memory multi-threadingis an operating system technique.


Applications and tools programmersneed computational models with

integrated synchronization.


Use processes and message passing.


It's all easier if processesare single threaded.


ActorsIndependent processes communicating via asynchronous exchange of messages.

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

Data ParallelismTransform a sequence to another sequence where all individual actions happen at the same time.


Active ObjectsAn object that is actually an actor but looks like a full service object.

AgentsA wrapper for some shared mutable state.

Software Transactional MemoryWrappers for mutable values that uses transactions rather than locks.

Fork/JoinAn toolkit for tree structured concurrency and parallelism.


Actors


ActorsIndependent processes communicating via asynchronous exchange of messages


Need an example.


The Sleeping Barber Problem


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

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


Code!


Dataflow


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


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



Data Parallelism


Data ParallelismTransform a sequence to another sequence where all individual actions happen at the same time.


Need an example.


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

1 1

1x2dx


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.


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


Code!


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:




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, dataflow, and data parallelism(and CSP, agents, fork/join,…)

are 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

Data Parallelism


Use

Go on, you know you want to…


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The End


GPars Workshop

Russel Winder

email: [email protected]: [email protected]

twitter: @russel_winderwebsite: http://www.russel.org.uk

mailto:[email protected]

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