Podcast
Questions and Answers
What is the most challenging step in Ian Foster's methodology for parallel algorithm design?
What is the most challenging step in Ian Foster's methodology for parallel algorithm design?
- Agglomeration
- Partition (correct)
- Mapping
- Communication
What is the main purpose of partitioning in parallel algorithm design?
What is the main purpose of partitioning in parallel algorithm design?
- To decompose the problem into concurrent tasks (correct)
- To increase degree of concurrency
- To minimize communication between tasks
- To reduce task granularity
What is a task dependency graph?
What is a task dependency graph?
- A graph illustrating task concurrency
- A directed graph illustrating task dependencies (correct)
- A tree representing task granularity
- A matrix representing task dependencies
What is the degree of concurrency?
What is the degree of concurrency?
How does the granularity of decomposition affect the degree of concurrency?
How does the granularity of decomposition affect the degree of concurrency?
What is the maximum degree of concurrency?
What is the maximum degree of concurrency?
What is the purpose of agglomeration in parallel algorithm design?
What is the purpose of agglomeration in parallel algorithm design?
What is the primary focus of parallel algorithm design?
What is the primary focus of parallel algorithm design?
What is the purpose of mapping in parallel algorithm design?
What is the purpose of mapping in parallel algorithm design?
What is the main challenge in mapping tasks to processing units?
What is the main challenge in mapping tasks to processing units?
What is the key strategy for solving problems using Recursive Decomposition?
What is the key strategy for solving problems using Recursive Decomposition?
Which of the following is a characteristic of Recursive Decomposition?
Which of the following is a characteristic of Recursive Decomposition?
What is the key benefit of Recursive Decomposition in Quicksort?
What is the key benefit of Recursive Decomposition in Quicksort?
What type of operation can be used in Recursive Decomposition for finding Min/Max/Sum?
What type of operation can be used in Recursive Decomposition for finding Min/Max/Sum?
What is the key mindset required for Recursive Decomposition?
What is the key mindset required for Recursive Decomposition?
What is the primary benefit of using Recursive Decomposition?
What is the primary benefit of using Recursive Decomposition?
Which of the following is NOT a partitioning technique?
Which of the following is NOT a partitioning technique?
What is the purpose of Recursive Decomposition in problem-solving?
What is the purpose of Recursive Decomposition in problem-solving?
Which of the following is a characteristic of Quicksort?
Which of the following is a characteristic of Quicksort?
What is the key difference between Recursive Decomposition and other partitioning techniques?
What is the key difference between Recursive Decomposition and other partitioning techniques?
What is the key advantage of using Recursive Decomposition compared to other techniques?
What is the key advantage of using Recursive Decomposition compared to other techniques?
What does a directed path in the task dependency graph represent?
What does a directed path in the task dependency graph represent?
What determines the shortest time in which a program can be executed in parallel?
What determines the shortest time in which a program can be executed in parallel?
What is the critical path length?
What is the critical path length?
If each task takes 10 time units, what is the minimum parallel execution time for the graph in (a)?
If each task takes 10 time units, what is the minimum parallel execution time for the graph in (a)?
Why can't the parallel time be made arbitrarily small by making the decomposition finer in granularity?
Why can't the parallel time be made arbitrarily small by making the decomposition finer in granularity?
What is a limitation of decomposing a computation into finer granularity?
What is a limitation of decomposing a computation into finer granularity?
What is an example of a computation that has a limit on how fine the granularity can be?
What is an example of a computation that has a limit on how fine the granularity can be?
What is the tradeoff that determines performance bounds?
What is the tradeoff that determines performance bounds?
What is the effect of communication overhead on parallel performance?
What is the effect of communication overhead on parallel performance?
What is the relationship between the critical path length and the parallel execution time?
What is the relationship between the critical path length and the parallel execution time?
Study Notes
Parallel Algorithm Design
- Ian Foster's Methodology (PCAM) for parallel algorithm design involves four steps: Partition, Communication, Agglomeration, and Mapping.
- Partition: decompose the problem, identify concurrent tasks, and identify dependencies between tasks.
- Communication: often dictated by partitioning, and involves exchange of data between tasks.
- Agglomeration (clustering/blocking): grouping tasks together, but often there's limited flexibility in this step.
- Mapping: assigning tasks to processors, a difficult problem that involves load balancing.
Preliminaries
- Task Dependency Graph: a directed graph representing dependencies between tasks.
- Degree of Concurrency: the number of tasks that can be executed in parallel.
- Maximum Degree of Concurrency: the maximum number of tasks that can be executed in parallel at any point during execution.
- Average Degree of Concurrency: the average number of tasks that can be processed in parallel over the execution of the program.
Task Dependency Graph
- A sequence of tasks that must be processed one after the other is represented by a directed path in the task dependency graph.
- The longest path in the graph determines the shortest time in which the program can be executed in parallel.
- The length of the longest path is called the critical path length.
Limits on Parallel Performance
- There is an inherent bound on how fine the granularity of a computation can be.
- Excessive decomposition can lead to communication overhead and diminishing returns.
- The tradeoff between granularity and overhead determines performance bounds.
Partitioning Techniques
- There is no single recipe that works for all problems.
- Common techniques include:
- Recursive Decomposition: suited to problems solved using a divide-and-conquer strategy.
- Data Decomposition: dividing the data into smaller pieces and processing each piece concurrently.
- Exploratory Decomposition: exploring different decomposition strategies to find the best one.
- Speculative Decomposition: decomposing the problem into smaller tasks and solving them concurrently, without knowing which one will be successful.
Recursive Decomposition
- Suited to problems solved using a divide-and-conquer strategy.
- Decompose based on sub-problems, which can be solved in parallel.
- Need to think recursively, not sequentially.
Examples
- Quicksort: a problem that can be solved using recursive decomposition.
- Finding the Min/Max/Sum: an example of an associative and commutative operation that can be parallelized using recursive decomposition.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
Explore Ian Foster's methodology for parallel algorithm design, focusing on partitioning and mapping. Learn how to decompose problems and identify concurrent tasks.
