Using Groovy? Got lots of stuff to do at the same time? Then you need to take a look at GPars (“Jeepers!”), a library providing support for concurrency and parallelism in Groovy. GPars brings powerful concurrency models from other languages to Groovy and makes them easy to use with custom DSLs: - Actors (Erlang and Scala) - Dataflow (Io) - Fork/join (Java) - Agent (Clojure agents) In addition to this support, GPars integrates with standard Groovy frameworks like Grails and Griffon. Background, comparisons to other languages, and motivating examples will be given for the major GPars features.

1. Groovy Concurrency
Alex Miller
Revelytix

2. GPars
Jeepers!
• Groovy library - http://gpars.codehaus.org/
• Open-source
• Great team: Václav Pech, Dierk König, Alex
Tkachman, Russel Winder, Paul King
• Integration with Griffon, Grails, etc

3. Concurrency Patterns
• Asynchronous processing
• Computational processing
• Managing mutable state
• Actor messaging
• Agents
• Dataflow variables

4. Asynchronous
Processing
Improve perceived performance by pushing
I/O-bound work onto a different thread.

5. Serialized Processing
main
Retrieve
user1 Twitter
tweets
Retrieve
user2 Twitter
tweets
Process
tweets

6. Asynchronous Processing
main background 1 background 2
Request user1
tweets
Request user2 Retrieve
tweets user1
tweets Retrieve
user2
Process tweets tweets

7. GParsExecutorsPool
• Leverages java.util.concurrent.Executor
• void execute(Runnable command)
• Decouple task submission from task
execution
• GParsExecutorsPool.withPool() blocks add:
• async() - takes closure, returns Future
• callAsync() - same, but takes args

8. Future
• Future represents the result of an
asynchronous computation
• Poll for results, block till available, etc
• Cancel, check for cancellation

9. trends.each {
retrieveTweets(api, it.query)
}
GParsExecutorsPool.withPool {
def retrieveTweets = {
query -> recentTweets(api, query)
}
trends.each {
retrieveTweets.callAsync(it.query)
}
}

10. Computational
Processing
Use multiple cores to reduce the elapsed
time of some cpu-bound work.

11. Executor Pools
Same pools used for asynch are also good
for transaction processing. Leverage more
cores to process the work.
T1
Task 1
T1 T2 T3
Task 2
Task 1 Task 2 Task 3
Task 3
Task 4 Task 5 Task 6
Task 4
Task 5
Task 6

12. Work pools and
Queues
put take
queue

13. Fork/Join Pools
• DSL for new JSR 166y construct (JDK 7)
• DSL for ParallelArray over fork/join
• DSL for map/reduce and functional calls

14. Fork/Join Pools
put take
queue
put take
queue
put take
queue

15. Fork/Join vs Executors
• Executors (GParsExecutorsPool) tuned for:
• small number of threads (4-16)
• medium-grained I/O-bound or CPU-bound
tasks
• Fork/join (GParsPool) tuned for:
• larger number of threads (16-64)
• fine-grained computational tasks, possibly
with dependencies

16. Divide and Conquer
Task: Find max value in an array

17. Divide and Conquer

18. Divide and Conquer

19. ParallelArray
• Most business problems don’t really look
like divide-and-conquer
• Common approach for working in parallel
on a big data set is to use an array where
each thread operates on independent
chunks of the array.
• ParallelArray implements this over fork/join.

20. ParallelArray
Starting orders Order Order Order Order Order Order
w/due dates 2/15 5/1 1/15 3/15 2/1 4/1
Order Order Order
Filter only overdue 2/15 1/15 2/1
Map to days
15 45 30
overdue
Avg 30 days

21. ParallelArray usage
def isLate = { order -> order.dueDate > new Date() }
def daysOverdue = { order -> order.daysOverdue() }
GParsPool.withPool {
def data = createOrders()
.filter(isLate)
.map(daysOverdue)
println("# overdue = " + data.size())
println("avg overdue by = " + (data.sum() / data.size()))
}

22. Managing Mutable
State

23. a
The Jav
nc y
concur re
bible

24. JCIP
• It’s the mutable state, stupid.
• Make fields final unless they need to be mutable.
• Immutable objects are automatically thread-safe.
• Encapsulation makes it practical to manage the complexity.
• Guard each mutable variable with a lock.
• Guard all variables in an invariant with the same lock.
• Hold locks for the duration of compound actions.
• A program that accesses a mutable variable from multiple threads
without synchronization is a broken program.
• Don’t rely on clever reasoning about why you don’t need to synchronize.
• Include thread safety in the design process - or explicitly document that
your class is not thread-safe.
• Document your synchronization policy.

25. JCIP
• It’s the mutable state, stupid.
• Make fields final unless they need to be mutable.
• Immutable objects are automatically thread-safe.
• Encapsulation makes it practical to manage the complexity.
• Guard each mutable variable with a lock.
• Guard all variables in an invariant with the same lock.
• Hold locks for the duration of compound actions.
• A program that accesses a mutable variable from multiple threads
without synchronization is a broken program.
• Don’t rely on clever reasoning about why you don’t need to synchronize.
• Include thread safety in the design process - or explicitly document that
your class is not thread-safe.
• Document your synchronization policy.

26. The problem with shared state concurrency...

27. The problem with shared state concurrency...
is the shared state.

28. Actors
• No shared state
• Lightweight processes
• Asynchronous, non-blocking message-passing
• Buffer messages in a mailbox
• Receive messages with pattern matching
• Concurrency model popular in Erlang, Scala, etc

29. Actors

30. Actors
start

31. Actors
send
start

32. Actors
receive

33. Actor Example
class Player extends AbstractPooledActor {
String name
def random = new Random()
void act() {
loop {
react {
// player replies with a random move
reply
Move.values()[random.nextInt(Move.values().length)]
}
}
}
}

34. Agent
• Like Clojure Agents
• Imagine wrapping an actor
around mutable state...
• Messages are:
• functions to apply to (internal) state OR
• just a new value
• Can always safely retrieve value of agent

35. Simple example...
• Use generics to specify data type of wrapped data
• Pass initial value on construction
• Send function to modify state
• Read value by accessing val
def stuff = new Agent<List>([])
// thread 1
stuff.send( {it.add(“pizza”)} )
// thread 2
stuff.send( {it.add(“nachos”)} )
println stuff.val

36. class BankAccount extends Agent<Long> {
def BankAccount() { super(0) }
private def deposit(long deposit)
{ data += deposit }
private def withdraw(long deposit)
{ data -= deposit }
}
final BankAccount acct = new BankAccount()
final Thread atm2 = Thread.start {
acct << { withdraw 200 }
}
final Thread atm1 = Thread.start {
acct << { deposit 500 }
}
[atm1,atm2]*.join()
println "Final balance: ${ acct.val }"

37. Dataflow Variables
• A variable that computes its value when its’
inputs are available
• Value can be set only once
• Data flows safely between variables
• Ordering falls out automatically
• Deadlocks are deterministic

38. Dataflow Tasks
• Logical tasks
• Scheduled over a thread pool, like actors
• Communicate with dataflow variables

39. Dataflow example
final def x = new DataFlowVariable()
final def y = new DataFlowVariable()
final def z = new DataFlowVariable()
task {
z << x.val + y.val
println “Result: ${z.val}”
}
task {
x << 10
}
task {
y << 5
}

40. Dataflow support
• Bind handlers - can be registered on a
dataflow variable to execute on bind
• Dataflow streams - thread-safe unbound
blocking queue to pass data between tasks
• Dataflow operators - additional
abstraction, formalizing inputs/outputs

41. Sir Not-Appearing-In-
This-Talk
• Caching - memoize
• CSP
• GPU processing

42. Alex Miller
Twitter: @puredanger
Blog: http://tech.puredanger.com
Code:
http://github.com/puredanger/gpars-examples
Conference: Strange Loop
http://strangeloop2010.com