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Questions and Answers
What does data parallelism focus on in parallel computing environments?
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?
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?
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?
In the given example, how many times faster is the parallel execution compared to the sequential execution?
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What is a common data structure that data parallelism can be applied on?
What is a common data structure that data parallelism can be applied on?
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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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