Granularity of Parallel Systems
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Questions and Answers

What does the granularity of a task measure?

  • The time required to perform the computation of a task
  • The amount of work performed by a task (correct)
  • The ratio of communication time to computation time
  • The number of instructions executed in a particular task

    • How is the granularity G of a task calculated?

  • G = Tcomp + Tcomm
  • G = Tcomp / Tcomm (correct)
  • G = Tcomp - Tcomm
  • G = Tcomm / Tcomp

    • What is fine-grained parallelism characterized by?

  • A small number of small tasks
  • A small number of large tasks
  • A large number of large tasks
  • A large number of small tasks (correct)

    • What is the benefit of fine-grained parallelism?

    <p>Facilitates load balancing</p> Signup and view all the answers

    What is an alternative way to specify granularity?

    <p>In terms of the execution time of a program</p> Signup and view all the answers

    What is the purpose of considering granularity in parallel systems?

    <p>To take into account the communication overhead between processors</p> Signup and view all the answers

    What is an example of a fine-grained system from outside the parallel computing domain?

    <p>The system of neurons in our brain</p> Signup and view all the answers

    What occurs in coarse-grained parallelism if tasks process bulk of the data unevenly?

    <p>Load imbalance</p> Signup and view all the answers

    What is the advantage of coarse-grained parallelism?

    <p>Low communication and synchronization overhead</p> Signup and view all the answers

    What is medium-grained parallelism relative to?

    <p>Fine-grained and coarse-grained parallelism</p> Signup and view all the answers

    What is the result of using fewer processors in parallel systems?

    <p>Improved performance</p> Signup and view all the answers

    What is the optimal performance achieved in parallel and distributed computing?

    <p>Between fine-grained and coarse-grained parallelism</p> Signup and view all the answers

    Study Notes

    Granularity of Parallel Systems

    • Granularity is a measure of the amount of work (or computation) performed by a task.
    • It can also be defined as the ratio of computation time to communication time, wherein:
      • Computation time is the time required to perform the computation of a task.
      • Communication time is the time required to exchange data between processors.

    Calculating Granularity

    • Granularity (G) can be calculated as: G = Tcomp / Tcomm
    • Granularity is usually measured in terms of the number of instructions executed in a particular task.
    • Alternatively, it can be specified in terms of the execution time of a program, combining the computation time and communication time.

    Types of Parallelism

    Fine-grained Parallelism

    • A program is broken down into a large number of small tasks.
    • These tasks are assigned individually to many processors.
    • The amount of work associated with a parallel task is low and the work is evenly distributed among the processors.
    • Example: The system of neurons in our brain.

    Coarse-grained Parallelism

    • A program is split into large tasks.
    • A large amount of computation takes place in processors.
    • This might result in load imbalance, where certain tasks process the bulk of the data while others might be idle.
    • Advantage: Low communication and synchronization overhead.
    • Example: Message-passing architecture.

    Medium-grained Parallelism

    • A compromise between fine-grained and coarse-grained parallelism.
    • Task size and communication time are greater than fine-grained parallelism and lower than coarse-grained parallelism.
    • Example: General-purpose parallel computers.

    Effects of Granularity in Parallel and Distributed Computing

    • Using fewer processors can improve performance of parallel systems.
    • Scaling down a parallel system means using fewer than the maximum possible number of processing elements to execute a parallel algorithm.
    • Optimal performance is achieved between the two extremes of fine-grained and coarse-grained parallelism.

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    Description

    Understand the concept of granularity in parallel systems, including its definition, computation time, and communication time. Learn how granularity affects parallel processing and task distribution.

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