Parallelism and Algorithm Efficiency

The video tutorial covers parallel and distributed association rule algorithms, focusing on data and task parallelism. It explains the count distribution algorithm for data parallelism and the data distribution algorithm for task parallelism. The tutorial also discusses metrics for combining data and compares various algorithms, highlighting their efficiency and application scenarios.

What is the primary focus of parallel and distributed association rule algorithms?

• To increase the complexity of data processing

• To enhance the speed and efficiency of rule generation

• To reduce the number of processors required

• To eliminate the need for data partitioning

In task parallelism, how are candidates managed?

• Candidates are processed by a single processor

• Candidates are processed sequentially

• Candidates are partitioned and counted separately at each processor

• Candidates are ignored

What is a key advantage of data parallelism?

• Reduced communication costs

• Reduced memory requirements

• Increased communication costs

• Simplified algorithm design

Which type of parallelism scales linearly with the number of processors?

• None of the above

• Sequential processing

• Data parallelism

• Task parallelism

What is the first step in the count distribution algorithm?

• Merging local counts

• Partitioning the database

• Broadcasting global counts

• Counting candidates in parallel

What is another name for the task parallelism algorithm?

• Data distribution algorithm

• Count distribution algorithm

• Memory optimization algorithm

• Sequential algorithm

Which of the following is a metric for combining data sets?

• Algorithm complexity

• Processor speed

• User interface design

• Data source presence

Which algorithm requires m+1 scans in a priori?

• DDA

• CDA

• Partitioning

• Sampling