Scheduling for parallel processing

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Contents:

  • Scheduling for Parallel Processing
  • Loop scheduling
  • Table of contents
  • Bibliographic Information

    • I understand my question title is rather broad, I am new to parallel programming and openmp. I collected data by running a program for different cases and plotted the data, this is what I got Performance vs Number of threads Performance can be assumed to be proportional to MegaFLOPS.

    Introduction to Parallel Programming

    I was surprised to see that static scheduling generally did better than dynamic scheduling for this problem? Can anyone explain the possible reasons for this behavior? Your results are not that revelant to notice a strong difference between the dynamic and static approach scheduling.

    13th International Workshop, JSSPP 2007, Seattle, WA, USA, June 17, 2007. Revised Papers

    I find measuring speedup more appropriate in your context where you want to see the behaviour of your parallel scalability. You can also use different metrics such as weak and strong scaling. You hardly reach a speedup of 2 using both scheduling with the coarse grained approach.

    Scheduling for Parallel Processing

    This is not enough to conclude anything. Moreover, you cannot analyze your results from your fine grained implementation since you have no parallel gain from it this can be explained by the poor workload you have for each thread. Get good parallel scalability first. Generally I choose the static or dynamic scheduling depending on the type of computations I am working on :. Static scheduling where computation workload is regular the same for each thread such as basic image convolution, naive matrix computation.

    For instance, using static scheduling for gaussian filter should be the best option. Dynamic scheduling where the computation workload is irregular such as Mandelbrot set. The way dynamic works is a little more complex chunks are not precomputed as in static scheduling hence some overhead might appear. In your case, your nbody simulation implies quite regular works. So static scheduling should be more appropriate. Having good parallel scalability is sometimes empirical and depends of your context. Likewise, tasks that you define for a job in the client session exist in the MATLAB Job Scheduler data location, and you access them through task objects.

    When you create and run a job, it progresses through a number of stages.

    • Scheduling for Parallel Processing | Maciej Drozdowski | Springer?
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    • Job Scheduling Strategies for Parallel Processing?

    Each of these stages is briefly described in this section. The figure below illustrates the stages in the life cycle of a job. Some of the functions you use for managing a job are createJob , submit , and fetchOutputs. You create a job on the scheduler with the createJob function in your client session of Parallel Computing Toolbox software. The job's first state is pending. This is when you define the job by adding tasks to it.

    The scheduler executes jobs in the queue in the sequence in which they are submitted, all jobs moving up the queue as the jobs before them are finished. You can change the sequence of the jobs in the queue with the promote and demote functions.

    Loop scheduling

    When a job reaches the top of the queue, the scheduler distributes the job's tasks to worker sessions for evaluation. If more workers are available than are required for a job's tasks, the scheduler begins executing the next job. In this way, there can be more than one job running at a time.

    At this time, you can retrieve the results from all the tasks in the job with the function fetchOutputs. When using a third-party scheduler, a job might fail if the scheduler encounters an error when attempting to execute its commands or access necessary files.

    Table of contents

    This state is available only as long as the job object remains in the client. Therefore, you can retrieve information from a job later or in another client session, so long as the MATLAB Job Scheduler has not been restarted with the -clean option. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. The goal of scheduling is to determine an assignment of tasks to processing elements in order to optimize certain performance indexes.

    Performance and efficiency are two characteristics used to evaluate a scheduling system. We should evaluate a scheduling system based on the quality of the produced task assignment schedule and the efficiency of the scheduling algorithm scheduler. The produced schedule is judged based on the performance criterion to be optimized, while the scheduling algorithm is evaluated based on its time complexity.

    For example, if we try to optimize the completion time of a program, the less the completion time, the better the schedule will be. Also, if two scheduling algorithms produce task assignments that have the same quality, the less complex algorithm is clearly the better.

    Bibliographic Information

    Cache coherence provides two potential benefits to the equivalence-class scheme. Zomaya Search for more papers by this author. CASCH is useful for both novice and expert programmers of parallel machines, and can serve as a teaching and learning aid for understanding scheduling and mapping algorithms. They tend to find and distribute fine-grained parallelism across cores, even if the input code has coarser-grained parallelism available that can be distributed at a lower communication cost. This re-evaluation was supported by a significant body of research, which included the proposal of new scheduling approaches as well as detailed simulations based on real-life complex workload traces. Share Give access Share full text access.

    The scheduling problem in known to be computationally intractable in many cases. Fast optimal algorithms can only be obtained when some restrictions are imposed on the models representing the program and the distributed system. Solving the general problem in a reasonable amount of time requires the use of heuristic algorithms. These heuristics do not guarantee optimal solutions to the problem, but they attempt to find near optimal solutions. This chapter addresses the scheduling problem in many of its variations. We survey a number of solutions to this important problem.

    We cover program and system models, optimal algorithms, heuristic algorithms, scheduling versus allocation techniques, and homogeneous versus heterogeneous environments. Advanced Computer Architecture and Parallel Processing.