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Parallel Computing Concepts

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@PoisedWhistle

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Questions and Answers

What is data parallelism in parallel computing?

Distributing different tasks across multiple processors

Performing different operations on the same data set

Distributing data across multiple processors for the same operation (correct)

Combining results from multiple processors performing different operations

Which of the following best describes task parallelism?

Each processor handling a different task or operation (correct)

Processing data sequentially before feeding it to processors

All processors performing the same function

Spreading the same data across different processors

In parallel computing, what is a primary benefit of data parallelism?

Decrease in overall computation due to task switching

Increased memory usage across processors

Reduction in the variety of tasks assigned

Enhanced processing speed for uniform operations (correct)

Which statement about parallel computing paradigms is incorrect?

<p>Both paradigms require identical hardware specifications.</p> Signup and view all the answers

How do data parallelism and task parallelism differ?

<p>Data parallelism requires the same operation to be executed across processors.</p> Signup and view all the answers

Study Notes

Parallel Computing

Involves simultaneous execution of multiple calculations.

Data Parallelism

Focuses on distributing subsets of data across numerous processors.
Each processor executes identical operations on its assigned data subset.
Useful for applications that require the same processing steps on large datasets, enhancing efficiency.

Task Parallelism

Involves distributing distinct tasks among various processors.
Each processor handles a unique operation, allowing for a diverse set of tasks to be completed simultaneously.
Ideal for workflows with multiple independent computations, optimizing resource use and overall execution time.

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Description

Explore the fundamentals of parallel computing, including data parallelism and task parallelism. Learn how these concepts distribute computations across multiple processors to enhance efficiency and optimize resource use in complex workflows.