Introduction to JDK7's Fork/Join framework. Present on May 3 2012 at the Belgian Java User Group (BeJUG). For more information see: http://www.bejug.org/confluenceBeJUG/display/BeJUG/ForkJoin+and+Akka Demo code can be found at http://bit.ly/bejug-fj

1. ForkJoin
@Sander_Mak

2. About
Coding at
( )
Writing Blog @
branchandbound.net
Speaking

3. Agenda
ForkJoin Setting the scene
ForkJoin API & Patterns
Comparisons & Future
break
Akka Introduction
Actors
Async IO

4. Problem?

5. Problem?

6. Problem?

7. Problem?

8. Problem?
Architectural mismatch

9. Problem?
So let the compiler/runtime solve it!

10. Problem?
So let the compiler/runtime solve it!
n
<
2
n
if
(n
<
2)
{
return
n;
}
else
{
return
fib(n
-­‐
1)
+
fib(n
-­‐
2);
}

11. Problem?
So let the compiler/runtime solve it!
Unfortunately not in ,
but feasible in pure
functional languages like

12. First attempt
public
static
int
fib(final
int
n)
{
if
(n
<
2)
{
return
n;
}
else
{
final
int[]
t1
=
{0},
t2
=
{0};
Thread
thread1
=
new
Thread()
{
public
void
run()
{
t1[0]
=
fib(n
-­‐
1);
}
};
Thread
thread2
=
new
Thread()
{
public
void
run()
{
t2[0]
=
fib(n
-­‐
2);
}
};
thread1.start();
thread2.start();
thread1.join();
thread2.join();
return
t1[0]
+
t2[0];
}
} if
(n
<
2)
{
return
n;
}
else
{
return
fib(n
-­‐
1)
+
fib(n
-­‐
2);
}

13. Threads
Threads are mostly waiting
Improve with threadpooling
but not by much
starvation
What if we sum in a new thread?
synchronization
visibility issues (Java Memory Model)

14. ForkJoin
Fork:
Recursively decompose Result = 3
large task into subtasks
Fib(4)
Join:
Fib(3) Fib(2)
Await results of
recursive tasks
and combine Fib(2) Fib(1) Fib(1) Fib(0)
Fib(1) Fib(0)

15. ForkJoin
Introducing:
ForkJoinPool
java.util.concurrent.
java.util.concurrent.ForkJoinTask
ForkJoinTask
RecursiveAction RecursiveTask
ForkJoinPool:
void execute (ForkJoinTask<?>)
T invoke (ForkJoinTask<T>)

16. ForkJoin
compute(problem)
{
if
(problem.size
<
threshold)
directlySolve(problem)
else
{
do-­‐forked
{
leftResult
=
compute(left(problem))
rightResult
=
compute(right(problem))
}
join(leftResult,
rightResult)
return
combine(leftResult,
rightResult)
}
}
Keep overhead down:
sequential cutoff

17. ForkJoinPool
Implements ExecutorService
Autosize workers (overridable)
Work queue per worker
Work-stealing between queues

18. ForkJoinPool
Work stealing

19. ForkJoinPool
Work stealing
1

20. ForkJoinPool
Work stealing
1

21. ForkJoinPool
Work stealing
2
3
1

22. ForkJoinPool
Work stealing
st
ol
en
3 2

23. ForkJoinPool
Work stealing
3 2

24. ForkJoinPool
Work stealing
4 6
5 7
3 2

25. ForkJoinPool
Work stealing
4
sto
len
5 7 6

26. ForkJoinPool
Work stealing
4
8 10 12
sto
len
9 11 13
5 7 6

27. ForkJoinPool
Scalability

28. Patterns
#1 Problem structure
Acyclic CPU Bound
- I/O upfront
- No webcrawlers
please...

29. Patterns
#2 Sequential cutoff
Guidelines
compute(problem)
{
> 100 and < 10.000
if
(problem.size
<
threshold)
‘basic computational
directlySolve(problem)
else
{
steps’
do-­‐forked
{
...
Experiment and tune
}
join
...
}
Never lock/synchronize }

30. Patterns
#3 Fork once, fool me twice
Why? left.fork()
rightResult
=
right.compute()
leftResult
=
left.join()
Implementation return
leftResult
+
rightResult
specific
Avoids overhead
left.fork()
Especially on smallish leftResult
=
left.join()
rightResult
=
right.compute()
tasks return
leftResult
+
rightResult

31. Patterns
#4 Use convenience methods
invoke invokeAll
fjTask.invoke(); invokeAll(fjTask1,
fjTask2,
//
Equivalent
to
fjTaskN);
fjTask.fork();
fjTask.join(); //
Or:
//
But
always
tries
execution
//
in
current
thread
first! invokeAll(collectionOfTasks);

32. Demo
2.797.245 world cities (Maxmind.com)
Demo 1: simple search
Demo 2: lat/long bounded

33. Demo
Search range:
i.e. 0.4°

34. ForkJoin & Java EE
ForkJoinPool creates threads
Illegal in EJBs
CDI/Servlet is a gray area
JCA/WorkManager could work
@Asynchronous as alternative
But: Java EE7 may contain javax.util.concurrent

35. Comparison
ExecutorService
Thread pooling (bounded or
unbounded)
Single work queue (no workstealing)
Coarse-grained independent tasks
Blocking I/O ok

36. Comparison
MapReduce
Environment Single JVM Cluster
Model Recursive forking Often single map
Scales with Cores/CPUs Nodes
Worker Workstealing No inter-node
interaction communication

37. Criticism
Complex implementation
(uses sun.misc.Unsafe)
Scalability > 100 cores?
Questionable assumption:
1-1 mapping worker
thread to core

38. Criticism
Complex implementation
(uses sun.misc.Unsafe)
Scalability > 100 cores?
Questionable assumption:
1-1 mapping worker
thread to core
Too low-level

39. Future
InfoQ: “What is supported out of the box?”
“Almost nothing".
We chickened out; we are not going to release
the layers on top of this
That means that right now, people who are using
this framework are going to be the people who
actually get into this parallel recursive
decomposition and know how to use it.

40. Future
JDK 8 plans
Parallel collections
Depends on Project Lambda
CountedCompleter for I/O
Some already available in jsr166extra

41. Future
int
findCities(List<String>
cities,
String
query)
{
Pattern
p
=
Pattern.compile(query)
return
cities.parallel()
.filter(c
=>
p.matcher(c).matches());
}
int
findNearestCities(List<String>
lines,
int
lat,
int
lng)
{
return
lines.parallel()
.map(c
=>
toCity(c))
.filter(c
=>
c.isNear(lat,
lng))
.sort();
}

42. Questions?
Code @ bit.ly/bejug-fj