GPars. Russel Winder. The concurrency and parallelism framework for Groovy and Java applications.

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1 GPars The concurrency and parallelism framework for Groovy and Java applications Russel

2 Aims and Goals Convince people that they: Can deal with parallelism and concurrency without fear. Already understand GPars. Can use GPars tonight.

3 should tremble in fear at the prospect of using shared-memory multi-threading with Java

4 and even with Groovy.

5 Concurrency Execution as co-routines: Sequences of code give up the execution to pass it to another coroutine.

6 More Concurrency Concurrency is a technique founded in a uniprocessor view of the world. Time-division multiplexing.

7 Parallelism Having multiple executions active all at the same time.

8 Concurrency is a tool for structuring execution where a single processor is used by multiple computations. Parallelism is about making a computation complete faster than using a single processor.

16 Squirrel behaviour emulates synchronized software behaviour.

17 Thanks to Paul King Who uses synchronized?

18 Thanks to Paul King You did it wrong.

19 Small addition by me Who uses lock objects?

20 Small addition by me You definitely did it wrong.

21 Locks deny parallelism.

22 The whole purpose of a lock is to prevent parallelism.

23 Parallelism is performance improvement. Performance improvement requires parallelism.

24 Locks deny performance improvement.

25 Locks are needed only if there is mutable shared state.

26 Avoid mutable shared state.

27 Use processes and message passing.

28 It's all easier if processes are single threaded.

29 but how

30 Use appropriate architectural models.

31 Dataflow Actors Operators connected by channels with activity triggered by arrival of data on the channels. Independent processes communicating via asynchronous exchange of messages CSP Sequential processes connected by channels using synchronous message exchange (rendezvous). 31

32 Actors Independent processes communicating via asynchronous exchange of messages 32

33 Dataflow Operators connected by channels with activity triggered by arrival of data on the channels. 33

34 CSP Sequential processes connected by channels using synchronous message exchange (rendezvous). 34

35 We need examples. 35

36 36

37 What is the Value of? Easy, it's known exactly, it's (obviously). 37

38 It's simples Александр Орлов

39 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. 1 1 = 0 dx x 39

40 One Possible Algorithm Use quadrature to estimate the value of the integral which is the area under the curve. Embarrassingly parallel. 4 n = i=1 n 1 i n With n = 3 not much to do, but potentially lots of error. Use n = 107 or n = 109? 40

41 Commutative and Associative Because addition is commutative and associative, expression can be decomposed into sums of partial sums. 41

42 a+b+c+d+e+f = (a+b)+(c+d)+(e+f) 42

43 Scatter Gather map reduce 43

44 Code 44

45 But isn't this just data parallelism? 45

46 More code 46

47 If you want the code, clone the Git repository: 47

48 Or if you just want to browse: 48

49 The Sleeping Barber Problem 49

50 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 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 due to Edsger Dijkstra. 50

51 The cutting chair. The waiting chairs. The barber's shop. A new customer enters the shop, check to see if they can go straight to the cutting chair, if not can they take a waiting chair, if not leave. 51

52 Wikipedia article presents the classic operating systems approach using locks and semaphores. 52

53 More code 53

54 If you want the code, clone the Git repository: 54

55 Or if you just want to browse: 55

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62 Squirrels deny parallelism. 62

63 Squirrels deny performance enhancement. 63

64 Don't be a squirrel. 64

65 Do not use explicit locking algorithms. 65

66 Use computational architectures that promote parallelism and hence performance improvement: Actors Dataflow CSP Data Parallelism 66

67 Use GPars. Go on, you know you want to 67

It's All About Processes Communicating

It's All About Processes Communicating Russel Winder email: russel@winder.org.uk xmpp: russel@winder.org.uk twitter: russel_winder 1 Aims, Goals and Objects Show that shared memory multi-threading should