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Fork Join (BeJUG 2012)
- 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?
- 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
- 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
- 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