Slides from my session on GPars at Gr8Conf EU 2012. It was a manifesto speech for using actors, dataflow, CSP and data parallelism and avoiding all explicit locking and indeed explicit shared-memory multi-threading.

1. GPars
The concurrency and parallelism framework
for Groovy and Java applications
Russel Winder
@russel_winder russel@winder.org.uk
http://www.russel.org.uk

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
Independent processes triggered by arrival of
communicating via data on the channels.
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 2009
38

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
4 1x 2
39

40. One Possible Algorithm
Use quadrature to estimate the value of the integral –
which is the area under the curve.
4 n 1
Embarrassingly parallel. = ∑i=1
n i−0.5 2
1 
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:
http://www.russel.org.uk/Git/Pi_Quadrature.git
47

48. Or if you just want to browse:
http://www.russel.org.uk/gitweb
48

49. The Sleeping Barber Problem
49

50. The Sleeping Barber Problem
The barber's shop has a single If the barber is cutting, a new
cutting chair and a row of customer checks to see if
waiting seats. there is a free waiting seat.
The barber sleeps in the cutting – If there is join the
chair unless trimming a queue to be
customer. trimmed.
Customers arrive at the shop at – If there isn't leave
intervals. disgruntled.
If the barber is asleep, the
customer wakes the barber Problem originally due
sits in the cutting chair and to Edsger Dijkstra.
gets a trim. 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.
http://en.wikipedia.org/wiki/Sleeping_barber_problem
52

53. More code…
53

54. If you want the code, clone the Git repository:
http://www.russel.org.uk/Git/SleepingBarber.git
54

55. Or if you just want to browse:
http://www.russel.org.uk/gitweb
55

56. 56

57. 57

58. 58

59. 59

60. 60

61. 61

62. Squirrels deny parallelism.
62

63. Squirrels deny performance enhancement.
63

64. Don't be a squirrel.
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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