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Actors and Message Passing

Some programming languages favor a different way of implementing synchronization using so-called actors. Actors are threads that have only private memory and communicate through message passing. See Figure 20.1 for an illustration. Given that there is no shared memory in the actor model (other than the message queues, which have built-in synchronization), there is no need for critical sections. Instead, some sequential thread owns a particular piece of data and other threads access it by sending request messages to the thread and optionally waiting for response messages. Each thread handles one message at a time, serializing all access to the data it owns. As message queues are FIFO (First-In-First-Out), starvation is prevented.

Figure 20.1: Depiction of three actors. The producer does not receive messages.

The actor synchronization model is popular in a variety of programming languages, including Erlang and Scala. Actor support is also available through popular libraries such as Akka, which is available for various programming languages. In Python, Java, and C/C++, actors can be easily emulated using threads and synchronized queues (aka blocking queues) for messaging. Each thread would have one such queue for receiving messages. Dequeuing from an empty synchronized queue blocks the thread until another thread enqueues a message on the queue.

The synch library supports a synchronized message queue, similar to the Queue object in Python. Its interface is as follows:

  • Queue() returns an empty queue;

  • put(q, item) adds item to the queue pointed to by q;

  • get(q) waits for and returns an item on the queue pointed to by q.

For those familiar with counting semaphores: note that a Queue behaves much like a zero-initialized counting semaphore. put is much like V, except that it is accompanied by data. get is much like P, except that it also returns data. Thus, synchronized queues can be considered a generalization of counting semaphores.

import synch

const NCLIENTS = 3

server_queue = synch.Queue()

def server():
    var counter = 0
    while True:
        let q = synch.get(?server_queue):   # await request
            synch.put(q, counter)           # send response
            counter += 1

spawn eternal server()

sequential done
done = [False,] * NCLIENTS

def client(client_queue):
    synch.put(?server_queue, client_queue)      # send request
    let response = synch.get(client_queue):     # await response
        done[response] = True
    await all(done)

alice_queue = synch.Queue()
spawn client(?alice_queue)
bob_queue = synch.Queue()
spawn client(?bob_queue)
charlie_queue = synch.Queue()
spawn client(?charlie_queue)
Figure 20.2 (code/counter.hny): An illustration of the actor approach

Figure 20.2 illustrates the actor approach. There are three client threads that each want to be assigned a unique identifier from the set \(\{ 0, 1, 2 \}\). Normally one would use a shared 0-initialized counter and a lock. Each client would acquire the lock, get the value of the counter and increment it, and release the lock. Instead, in the actor approach the counter is managed by a separate server thread. The server never terminates, so it is spawned with the keyword eternal to suppress non-terminating state warnings. Each client sends a request to the server, consisting in this case of simply the queue to which the server must send the response. The server maintains a local, zero-initialized counter variable. Upon receiving a request, it returns a response with the value of the counter and increments the counter. No lock is required.

This illustration is an example of the client/server model. Here a single actor implements some service, and clients send request messages and receive response messages. The model is particularly popular in distributed systems, where each actor runs on a separate machine and the queues are message channels. For example, the server can be a web server, and its clients are web browsers.


20.1 Actors and message queues are good for building pipelines. Develop a pipeline that computes Mersenne primes (primes that are one less than a power of two). Write four actors:

  1. an actor that generates a sequence of integers 1 through N;

  2. an actor that receives integers and forwards only those that are prime;

  3. an actor that receives integers and forwards only those that are one less than a power of two;

  4. an actor that receives integers but otherwise ignores them.

Configure two versions of the pipeline, one that first checks if a number is prime and then if it is one less than a power of two, the other in the opposite order. Which do you think is better?