What is data locality in parallel algorithms?
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What is data locality in parallel algorithms?
Data locality refers to the practice of keeping data close to where it is processed in parallel algorithms, reducing latency.
Why is data locality important for performance?
It minimizes data transfer times between memory and processors, leading to faster execution and improved overall performance.
Can you give an example of how to achieve data locality?
Sure! One way is to partition data into smaller chunks that fit into cache memory, allowing threads to access data without frequent memory fetches.
What are the challenges in maintaining data locality?
Challenges include dynamic data structures, load balancing among processors, and the overhead of data movement in distributed systems.
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