To answer this question, we need to understand the different partitioning methods used in parallel processing.
A partitioning method determines how data is divided and distributed among multiple parallel stages. The goal is to distribute the workload evenly across the parallel stages to achieve efficient parallel processing.
Let's go through each option to understand why it is correct or incorrect:
Option A) Range - This option is incorrect. Range partitioning involves dividing the data based on a specified range of values. While it can be effective in certain scenarios, it may not always result in the lowest CPU cost for parallel stage to parallel stage processing.
Option B) Modulus - This option is incorrect. Modulus partitioning involves dividing the data based on the remainder of a division operation. Similar to range partitioning, it may not always result in the lowest CPU cost for parallel stage to parallel stage processing.
Option C) Entire - This option is incorrect. Entire partitioning involves sending the entire data set to each parallel stage. This method can be inefficient as it duplicates the data across multiple stages, leading to higher CPU costs.
Option D) Same - This option is correct. The "Same" partitioning method involves sending the same data to each parallel stage. This method ensures that each stage processes the same data without duplicating it. Since the data is not divided or duplicated, it typically results in the lowest CPU cost for parallel stage to parallel stage processing.
Therefore, the correct answer is D) Same. This option is correct because it offers the lowest CPU cost partitioning method for parallel stage to parallel stage processing.