A Survey of Concurrency Constructs

A Survey of
Concurrency
Constructs
Ted Leung
Sun Microsystems
ted.leung@sun.com
@twleung

Today’s model
 Threads
 Program counter
 Own stack
 Shared Memory
 Locks

Some of the problems
 Locks
 manually lock and unlock
 lock ordering is a big problem
 locks are not compositional
 How do we decide what is concurrent?
 Need to pre-design, but now we have to retrofit
concurrency via new requirements

Design Goals/Space
 Mutual Exclusion
 Serialization / Ordering
 Inherent / Implicit vs Explicit
 Fine / Medium / Coarse grained
 Composability

A good solution
 Is substantially less error prone
 Makes it much easier to identify concurrency
 Runs on today’s (and future) parallel hardware
 Works if you keep adding cores/threads

Theoretical Models
 Actors
 CSP
 CCS
 petri-nets
 pi-calculus
 join-calculus
 Functional Programming

Implementation matters
 Threads are not free
 Message sending is not free
 Context/thread switching is not free
 Lock acquire/release is not free

The models
 Transactional Memory
 Persistent data structures
 Actors
 Dataflow
 Tuple spaces

Transactional Memory
 Original paper on STM 1995
 Idea goes as far back as 1986
 Tom Knight (Hardware Transactional Memory)
 First appearance in a programming language
 Concurrent Haskell 2005

The Model
 Use transactions on items in memory
 Enclose code in begin/end blocks
 Variations
 specify manual abort/retry
 specify an alternate path (way of controlling manual
abort)

Example

(defn deposit [account amount]
(dosync
(let [owner (account :owner)
balance-ref (account :balance-ref)]
(do
(alter balance-ref + amount)
(println “depositing” amount (account :owner)))))))

STM Design Space
 STM Algorithms / Strategies
 Granularity
 word vs block
 Locks vs Optimistic concurrency
 Conflict detection
 eager vs lazy
 Contention management

STM Problems
 Non transactional access to STM cells
 Non abortable operations
 I/O
 STM Overhead
 read/write barrier elimination
 Where to place transaction boundaries?
 Still need condition variables
 ordering problems are important
 1/3 of non-deadlock problems in one study

Implementations
 Haskell/GHC
 Use logs and aborts txns
 Clojure STM - via Refs
 based on ML Refs to confine changes, but ML Refs
have no automatic (i.e. STM) concurrency semantics
 only for Refs to aggregates
 Implementation uses MVCC
 Persistent data structures enable MVCC allowing
decoupling of readers/writers (readers don’t wait)

Persistent Data Structures
 Original formulation circa 1981
 Formalization 1986 Sarnoff
 Popularized by Clojure

The model
 Upon “update”, previous versions are still available
 preserve functionalness
 both versions meet O(x) characteristics
 In Clojure, combined with STM
 Motivated by copy on write
 hash-map, vector, sorted map

Available data structures
 Lists, Vectors, Maps
 hash list based on VLists
 VDList - deques based on VLists
 red-black trees

Available data structures
 Real Time Queues and Deques
 deques, output-restricted deques
 binary random access lists
 binomial heaps
 skew binary random access lists
 skew binomial heaps
 catenable lists
 heaps with efficient merging
 catenable deques

Problems
 Not really a full model
 Oriented towards functional programming

Actors
 Invented by Carl Hewitt at MIT (1973)
 Formal Model
 Programming languages
 Hardware
 Led to continuations, Scheme
 Recently revived by Erlang
 Erlang’s model is not derived explicitly from Actors

Example

object account extends Actor {

private var balance = 0

def act() {
loop {
react {
case Withdraw(amount) =>
balance -= amount
sender ! Balance(balance)
case Deposit(amount) =>
balance += amount
case BalanceRequest =>
case TerminateRequest =>
}
}

}

Problems with actors
 DOS of the actor mail queue
 Multiple actor coordination
 reinvent transactions?
 Actors can still deadlock and starve
 Programmer defines granularity
 by choosing what is an actor

Actor Implementations
 Scala
 Scala Actors
 Lift Actors
 Erlang
 CLR
 F# / Axum

Java
 kilim
 http://www.malhar.net/sriram/kilim/
 Actor Foundry
 http://osl.cs.uiuc.edu/af/
 actorom
 http://code.google.com/p/actorom/
 Actors Guild
 http://actorsguildframework.org/

Measuring performance
 actor creation?
 message passing?
 memory usage?

Erlang vs JVM
 Erlang
 per process GC heap
 tail call
 distributed
 JVM
 per JVM heap
 no tail call (fixed in JSR-292?)
 not distributed
 2 kinds of actors (Scala)

Actor variants
 Kamaelia
 messages are sent to named boxes
 coordination language connects outboxes to inboxes
 box size is explicitly controllable

Actor variants
 Clojure Agents
 Designed for loosely coupled stuff
 Code/actions sent to agents
 Code is queued when it hits the agent
 Agent framework guarantees serialization
 State of agent is always available for read (unlike
actors which could be busy processing when you
send a read message)
 not in favor of transparent distribution
 Clojure agents can operate in an ‘open world’ - actors
answer a specific set of messages

Last thoughts on Actors
 Actors are an assembly language
 OTP type stuff and beyond
 Akka - Jonas Boner
 http://github.com/jboner/akka

Dataflow
 Bill Ackerman’s PhD Thesis at MIT (1984)
 Declarative Concurrency in functional languages
 Research in the 1980’s and 90’s
 Inherent concurrency
 Turns out to be very difficult to implement
 Interest in declarative concurrency is slowly returning

The model
 Dataflow Variables
 create variable
 bind value
 read value or block
 Threads
 Dataflow Streams
 List whose tail is an unbound dataflow variable
 Deterministic computation!

Example: Variables 1
object Test5 extends Application {
import DataFlow._

val x, y, z = new DataFlowVariable[Int]

val main = thread {
println("Thread 'main'")
x << 1
println("'x' set to: " + x())
println("Waiting for 'y' to be set...")
if (x() > y()) {
z << x
println("'z' set to 'x': " + z())
} else {
z << y
println("'z' set to 'y': " + z())
}

x.shutdown
y.shutdown
z.shutdown
v.shutdown
}

Example: Variables 2

val setY = thread {
println("Thread 'setY', sleeping...")
Thread.sleep(5000)
y << 2
println("'y' set to: " + y())
}

// shut down the threads
main ! 'exit
setY ! 'exit

System.exit(0)
}

Example: Streams
import DataFlow._

def ints(n: Int, max: Int, stream: DataFlowStream[Int]): Unit = if (n != max) {
println("Generating int: " + n)
stream <<< n
ints(n + 1, max, stream)
}
def sum(s: Int, in: DataFlowStream[Int], out: DataFlowStream[Int]): Unit = {
println("Calculating: " + s)
out <<< s
sum(in() + s, in, out)
}
def printSum(stream: DataFlowStream[Int]): Unit = {
println("Result: " + stream())
printSum(stream)
}

val producer = new DataFlowStream[Int]
val consumer = new DataFlowStream[Int]

thread { ints(0, 1000, producer) }
thread { sum(0, producer, consumer) }
thread { printSum(consumer) }
}

Example: Streams (Oz)

fun {Ints N Max}
if N == Max then nil
else
{Delay 1000}
N|{Ints N+1 Max}
end
end

fun {Sum S Stream}
case Stream of nil then S
[] H|T then S|{Sum H+S T} end
end

local X Y in
thread X = {Ints 0 1000} end
thread Y = {Sum 0 X} end
{Browse Y}
end

Implementations
 Mozart Oz
 http://www.mozart-oz.org/
 Jonas Boner’s Scala library (now part of Akka)
 http://github.com/jboner/scala-dataflow
 dataflow variables and streams
 Ruby library
 http://github.com/larrytheliquid/dataflow
 dataflow variables and streams
 Groovy
 http://code.google.com/p/gparallelizer/

Variations
 Futures
 Originated in Multilisp
 Eager/speculative evaluation
 Implementation quality matters
 I-Structures
 Id, pH (Parallel Haskell)
 Single assignment arrays
 cannot be rebound => no streams

Problems
 Can’t handle non-determinism
 like a server
 Need ports
 this leads to actor like things

Tuple Spaces
 Originated in Linda (1984)
 Popularized by Jini

The Model
 Three operations
 write() (out)
 take() (in)
 read()

The Model
 Space uncoupling
 Time uncoupling
 Readers are decoupled from Writers
 Content addressable by pattern matching
 Can emulate
 Actor like continuations
 CSP
 Message Passing
 Semaphores

Example

public class Account implements Entry {
public Integer accountNo;
public Integer value;
public Account() { ... }
public Account(int accountNo, int value) {
this.accountNo = newInteger(accountNo);
this.value = newInteger(value);
}
}

try {
Account newAccount = new Account(accountNo, value);
space.write(newAccount, null, Lease.FOREVER);
}

space.read(accountNo);

Implementations
 Jini/JavaSpaces
 http://incubator.apache.org/river/RIVER/index.html
 BlitzSpaces
 http://www.dancres.org/blitz/blitz_js.html
 PyLinda
 http://code.google.com/p/pylinda/
 Rinda
 built in to Ruby

Problems
 Low level
 High latency to the space - the space is contention
point / hot spot
 Scalability
 More for distribution than concurrency

Projects
 Scala
 Erlang
 Clojure
 Kamaelia
 Haskell
 Axum/F#
 Mozart/Oz
 Akka

Work to be done
 More in depth comparisons on 4+ core platforms
 Higher level frameworks
 Application architectures/patterns
 Web
 Middleware

Final thoughts
 Shared State is troublesome
 immutability or
 no sharing
 It’s too early

References
 Actors: A Model of Concurrent Computation in
Distributed Systems - Gul Agha - MIT Press 1986
 Concepts, Techniques, and Models of Computer
Programming - Peter Van Roy and Seif Haridi - MIT
Press 2004

A Survey of Concurrency Constructs

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