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Communicating Sequential Processes (CSP) provides a formal concurrency model consisting of synchronously communicating independent processes. The model offers deterministic behavior plus it allows developers to combine the processes into composable and reusable components.

This is currently work-in-progress. We'll have some more news for you soon.

Processes, in GPars called Tasks, are concurrently run independent activities, which communicate by sending data through (typically synchronous) channels.

Code Block
titleA concurrent implementation of the Sieve of Eratosthenes
final int requestedPrimeNumberCount = 1000
final DataflowQueue initialChannel = new DataflowQueue()
 * Generating candidate numbers
group.task {
    (2..10000).each {
        initialChannel << it
    initialChannel << -1  //poisson

 * Chain a new filter for a particular prime number to the end of the Sieve
 * @param inChannel The current end channel to consume
 * @param prime The prime number to divide future prime candidates with
 * @return A new channel ending the whole chain
def filter(inChannel, int prime) {
    def outChannel = new DataflowQueue()

    group.task {
        while (true) {
            def number = inChannel.val
            if (number % prime != 0) {
                outChannel << number
            if (number == -1) break  //handle poisson and stop
    return outChannel

 * Consume Sieve output and add additional filters for all found primes
def currentOutput = initialChannel
requestedPrimeNumberCount.times {
    int prime = currentOutput.val
    println "Found: $prime"
    currentOutput = filter(currentOutput, prime)

GPars Tasks represent active computations. Indirect addressing through channels gives you an enormous flexibility in how and when you wire tasks together. The concept of Promises allows tasks to easily signal events or values to other parts of your program in a thread-safe manner. CSP programms are highly deterministic, which is a very useful quality of concurrent programms.

Tasks can be easily combined with other GPars concepts - with Agents to ease shared-state management or with Dataflow Operators to process streamed data.

For further details, please refer to the Groovy CSP section of the User Guide.