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What is data parallelism in parallel computing?
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?
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?
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?
Which statement about parallel computing paradigms is incorrect?
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How do data parallelism and task parallelism differ?
How do data parallelism and task parallelism differ?
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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.
