8. Analysis of Multithreaded Algorithms

TL;DR

Task parallel algorithms, like matrix multiplication, can be analyzed based on work and span to optimize performance.

Transcript

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Key Insights

• 🗂️ Divide and conquer and parallel loops are common strategies in task parallel algorithms.
• 💦 Work and span analysis helps optimize performance.

Questions & Answers

Q: What is the purpose of the grain size in parallel algorithms?

The grain size determines the size of the chunks of work that are parallelized. It helps balance the work and reduce scheduling overhead.

Q: How can you maximize parallelism in parallel loops?

By minimizing the work overhead and reducing the span, you can increase parallelism. This can be achieved by choosing appropriate grain size and coarsening the work.

Summary & Key Takeaways

• Task parallel algorithms rely on dividing and conquering the problem through recursive calls.

• The work is the total number of operations performed, while the span is the longest path of dependencies in the algorithm.

• It is important to find a balance between work and span to maximize parallelism and minimize overhead.