7.1 Creating Task Parallelism

7.1 Creating Task Parallelism

The essence of task parallelism is that the task to be accomplished can be executed in parallel. Since we assume that the tasks are not completely independent (otherwise they are just a collection of ordinary sequential jobs), some sort of coordinating mechanism must exist. We will call this process the manager, and the processes that carry out the subtasks the workers. (The manager could even be the human user, who manages the worker processes "by hand," but we will assume that the manager is a single program that the user causes to be started.) Manager/worker algorithms and execution mechanisms have many variations, which we survey in the next section; but as we use the term, "task parallelism" always involves the following steps.

  1. Divide the task into independent or nearly independent subtasks. By "independent" we mean that while communication of some sort occurs between the manager and the workers, there is no direct communication between any two workers.

  2. Start the workers. We assume that each worker is represented by an operating system process. In Section 7.2 we will describe Unix processes and how to start them. (Use of threads for workers is atypical for a Beowulf cluster and will not be described.)

  3. Communicate subtask specifications from the manager to the workers.

  4. Communicate results from the workers to the manager.

  5. Ensure that all results have been collected and that the workers have been shut down.

7.1.1 Variations on Task Parallelism

The scheme just described had many variations; we will discuss a few of them here, and then in the following section we will illustrate some of these with concrete examples. The variations involve the scheduling algorithm by which the manager assigns subtasks to the workers, the ways in which the worker processes are started and managed, and the communication mechanism between manager and workers.

Variations in How Work Is Assigned

For an efficient manager/worker parallel program, the workers should be kept working as much of the total time as possible. If the total work to be done can be easily divided into arbitrarily sized subtasks, then the scheduling job is easy: if there are n workers, then divide the work up into n pieces, each of which will take the same amount of time, and give one piece to each worker. This is called static scheduling.

Although sometimes such scheduling can be done, breaking up the total amount of work into subtasks typically results in subtasks of widely differing sizes, more subtasks than there are workers, or both. In all of these cases, the manager must organize the assignment of work to workers more carefully in order to keep the workers working. If some workers are idle when there is still more work to do, a load balancing problem occurs. Fortunately the general manager/worker algorithms can be used to overcome this problem when there are substantially more subtasks than there are workers. The idea is for the manager to make an initial assignment of subtasks to workers and then wait for subtask completion by any worker. At that point the worker can be assigned another subtask. In this way the master does not need to know ahead of time how much time each subtask will take; it just keeps all the workers as busy as possible.

Figure 7.1 shows a high-level framework for the manager and worker in a manager/worker system. In this example, n processes (workers) are started and then each process is sent the data for each task to perform. New processes are started once rather than for each task, because starting a new process is often a time-consuming operation.

start figure

     manager:                             worker:     for (i=0; i<n; i++) {                receive msg from manager         start new process                while (not exit msg) {         send work                            do work     }                                        send results     while (not done) {                       receive next message         wait for msg from any worker     }         receive results                  exit         if (work left) {             send new work to worker         }         else {             send exit msg to worker         }     } 

end figure

Figure 7.1: Schematic of a general manager-worker system

We note a few points about this algorithm.

  • A load balancing problem will inevitably occur near the end of the job, as some workers become idle but there is no more work to be assigned, because all the work that is not done is being worked on by other workers.

  • To make this period of load imbalance as small as possible, it is a good idea to make the subtasks small. If the manager can estimate the sizes of the subtasks, then it should assign the larger tasks first and the smaller ones near the end.

  • If the subtasks are too small, then the overhead of communication between manager and worker will take up too much of the total time.

Therefore one needs to give some thought to just exactly how to divide up the work. A technique that is sometimes used is to further subdivide the work units during the computation. In some algorithms, the workers subdivide their own tasks and return the new subsubtasks to the manager for redistribution to the other workers. An example is the Mandelbrot program described in Chapter 5 of [48].

Variations in Implementation Mechanisms

Processes can be started in a variety of ways, including shell commands, Unix system calls, remote shell commands of different kinds, parallel process management systems, and the use of daemons of various kinds. Even Web browsers can be used to launch remote tasks. We will discuss process startup in Section 7.2, after we have established a deeper understanding of operating system processes.

Similarly, the communication between manager and worker can be carried out in many ways. One way is to use the file system as a communication device. This is particularly convenient if all of the workers have access to the same file system. (See Chapter 19 for a discussion of shared file systems.) This mechanism is often used when the manager is programmed as a shell script.

A more flexible and powerful approach to communication among processes uses sockets. Sockets and how to use them in several programming languages are covered in Section 7.2.5. The use of higher-level communication libraries (MPI and PVM) is covered in later chapters.

Beowulf Cluster Computing With Linux 2003
Beowulf Cluster Computing With Linux 2003
Year: 2005
Pages: 198

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net