Exploratory Decomposition Quiz

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Questions and Answers

What is the purpose of speculative decomposition?

To handle programs that may take different branches depending on preceding computations.

How does speculative decomposition relate to a switch statement in C?

It is similar to evaluating branches of a switch statement in parallel before the input for the switch is available.

What is the potential advantage of speculative decomposition in terms of speedup?

It can result in super- or sub-linear speedups due to changing the amount of work done by parallel formulation.

When is speculative decomposition used?

When a program may take different computationally significant branches depending on preceding computations. Signup and view all the answers

How does speculative decomposition enable parallelism in computation?

By allowing tasks to start computing the next stage while one task resolves the current computation. Signup and view all the answers

How is speculative decomposition beneficial in terms of task efficiency?

It enables tasks to work on multiple branches of computation in parallel. Signup and view all the answers

What usually results in dependencies in a task-dependency graph?

The fact that the output of one task is the input for another Signup and view all the answers

What is represented by the nodes in a task-interaction graph?

Tasks Signup and view all the answers

How can the nodes and edges of a task-interaction graph be assigned weights?

Proportional to the amount of computation a task performs and the amount of interaction that occurs along an edge Signup and view all the answers

What is the typical nature of edges in a task interaction graph?

Undirected Signup and view all the answers

How can directed edges be used in a task-interaction graph?

To indicate the direction of flow of data if it is unidirectional Signup and view all the answers

What is the relationship between the edge-sets of a task-interaction graph and a task-dependency graph?

The edge-set of a task-interaction graph is usually a superset of the edge-set of the task-dependency graph Signup and view all the answers

How can the problem of computing the frequency of a set of item sets in a transaction database be decomposed?

Based on a partitioning of input data Signup and view all the answers

What does each task do in the 4-way decomposition for computing itemset frequencies?

Each task computes a local set of frequencies Signup and view all the answers

How are the p partial results combined to yield the final result in the sum of a sequence of N numbers using p processes?

The p partial results are added up Signup and view all the answers

What are the outputs of Tasks 1 and 3 added together for in the 4-way decomposition for computing itemset frequencies?

The outputs of Tasks 1 and 3 are added together Signup and view all the answers

What does each task in the 4-way decomposition for computing itemset frequencies receive?

Each task receives a different combination of the transaction set and frequencies Signup and view all the answers

What is the purpose of partitioning the input data into subsets of nearly equal sizes in the sum of a sequence of N numbers using p processes?

To allow each task to compute the sum of numbers in one of the subsets Signup and view all the answers

What is the owner-computes rule?

The owner-computes rule states that each partition performs all the computations involving data that it owns. Signup and view all the answers

How can partitioning intermediate data lead to higher concurrency?

Partitioning intermediate data can lead to higher concurrency by allowing tasks to work on different parts of the data simultaneously. Signup and view all the answers

When is exploratory decomposition used?

Exploratory decomposition is used to decompose problems whose underlying computations correspond to a search of a space for solutions. Signup and view all the answers

Give an example of a problem where exploratory decomposition can be applied.

A 15 puzzle (a tile puzzle) is a simple application of exploratory decomposition. Signup and view all the answers

What restructuring may be required when using intermediate data partitioning in algorithms?

Some restructuring of the original algorithm may be required to use intermediate data partitioning to induce a decomposition. Signup and view all the answers

How does the owner-computes rule differ based on partitioning input data and partitioning output data?

When partitioning input data, the owner-computes rule means that a task performs all the computations that can be done using the data assigned to it. When partitioning output data, the rule means that a task computes all the data in the partition assigned to it. Signup and view all the answers

What is the main difference between speculative decomposition and traditional decomposition techniques?

Speculative decomposition focuses on executing the most promising branch in parallel, while traditional decomposition evaluates all branches and discards the unnecessary ones. Signup and view all the answers

Why is a purely recursive decomposition not efficient in quicksort?

Purely recursive decomposition limits concurrency in quicksort, as it may result in more tasks than the available processes. Signup and view all the answers

How does hybrid decomposition improve the efficiency of algorithms?

Hybrid decomposition combines different decomposition techniques for different algorithm stages, optimizing concurrency and minimizing wasted computation. Signup and view all the answers

Explain the concept of rolling back computation in speculative decomposition.

Rolling back computation in speculative decomposition involves reverting to the correct branch of the switch when the anticipated outcome does not match the actual result. Signup and view all the answers

Why is speculative decomposition particularly useful when one outcome of a switch is more likely than others?

Speculative decomposition is beneficial in this scenario as it focuses on the most probable branch, reducing unnecessary computation. Signup and view all the answers

How does the parallel run time differ from the serial run time in the context of speculative decomposition?

The parallel run time is shorter by the time taken to evaluate the condition for the next task, which is utilized for useful computation in parallel. Signup and view all the answers

Study Notes

Speculative Decomposition Overview

Speculative decomposition breaks down a computational problem to explore multiple paths concurrently, potentially increasing throughput.
Relates to a switch statement in C by allowing it to predict and execute multiple branches simultaneously, relying on the likelihood of certain outcomes.

Speedup Advantages

Offers speedup by minimizing idle times, as tasks that can be predicted to succeed are executed in advance.
If a speculative path fails, only that path needs to be rolled back, allowing other tasks to continue.

Parallelism and Computation

Enables parallelism by allowing multiple speculative tasks to run concurrently, utilizing available resources effectively.
Beneficial for workloads that involve uncertainty or where outcomes can significantly vary.

Task Efficiency

Results in improved task efficiency by balancing workload and reducing overall execution times.
Dependencies in task-dependency graphs typically arise from shared resources or data that one task requires from another.

Task-Interaction Graphs

Nodes represent individual tasks within a tasks-interaction graph, illustrating relationships between tasks.
Weights can be assigned based on task execution time or resource consumption, influencing scheduling and resource allocation.

Edges in Task-Interaction Graphs

Edges typically represent dependencies or communication requirements between tasks.
Directed edges illustrate the flow of information, specifying which task's output is required by another.

Graph Relationships

Edge-sets of a task-interaction graph reflect the dependencies found within a task-dependency graph, forming a comprehensive representation of task interactions.

Decomposition of Itemset Frequencies

The problem can be decomposed by partitioning data into smaller sets, enabling parallel computation of item frequency.
Each task in a 4-way decomposition processes a distinct segment of data, contributing to the overall frequency count.

Partial Results Combination

The p partial results gathered from sub-tasks are combined through summation to produce a final result in bulk computation tasks.

Inputs in 4-way Decomposition

Tasks receive distinct, equally sized data segments to ensure balanced processing and avoid bottlenecks.

Purpose of Data Partitioning

Partitioning input data into subsets of nearly equal sizes enhances load balancing, minimizing execution times due to more equitable resource utilization.

Owner-Computes Rule

This rule assigns responsibility for computing specific data to the task that owns the data, thereby reducing redundant computations and optimizing resource usage.

Intermediate Data Partitioning

Leads to higher concurrency by allowing multiple tasks to work on different data subsets simultaneously, enhancing performance.