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Data Parallelism in Parallel Computing

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Questions and Answers

What does data parallelism focus on in parallel computing environments?

Distributing memory across different nodes

Distributing tasks across different nodes

Distributing processing power across different nodes

Distributing data across different nodes (correct)

What is the contrast to data parallelism?

Process parallelism

Memory parallelism

Sequential parallelism

Task parallelism (correct)

How does data parallelism affect the time taken by a process?

It makes the time taken by the process unpredictable

It does not affect the time taken by the process

It reduces the time taken by the process (correct)

It increases the time taken by the process

In the given example, how many times faster is the parallel execution compared to the sequential execution?

What is a common data structure that data parallelism can be applied on?

<p>Arrays and matrices</p> Signup and view all the answers

Study Notes

Data Parallelism

In parallel computing environments, data parallelism focuses on dividing the data of a problem into smaller parts.
These parts are then processed simultaneously by multiple processors.
The contrast to data parallelism is task parallelism, where the tasks of a problem are divided and executed concurrently.
Data parallelism can significantly reduce the time taken by a process.
Example: If a task takes 10 seconds to complete sequentially, and you have 10 processors available, data parallelism can potentially reduce the time to 1 second.
Common data structures: Arrays are a common data structure where data parallelism can be effectively applied.

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Description

Test your knowledge on data parallelism and its role in parallel computing environments. This quiz will cover topics such as distributing data across nodes, working on elements in parallel, and the difference between data parallelism and task parallelism.