Which scenario could benefit the most from parallel processing?

Which scenario could benefit the most from parallel processing?

• A. Your custom transformer conducts a geometry validation process on hundreds of road features split into thousands of groups by municipality.
• B. Your custom transformer conducts a logical test on 3D bridge data split into many groups by bridge. The outcome of each test affects the next test.
• C. Your custom transformer conducts a small attribute edit to a small number (less than 10) customer features split into thousands of groups by company.
• D. Your custom transformer conducts complex spatial analysis on thousands of geometry features split into three groups by county.

Answer: (D)

Parallel processing shines when you can split the work into independent, heavy tasks that can run at the same time without waiting on each other. In this scenario, thousands of geometry features are divided into a small number of groups by county. The spatial analysis on each feature is typically CPU-intensive, and the result for one county doesn’t depend on another. That means you can run the analysis for each county in parallel on separate cores or workers and then combine the results at the end. The workload is substantial, and the lack of inter-group dependencies makes parallel execution highly effective, so you get a meaningful speedup. The other scenarios either involve lightweight work (where the overhead of parallelization erodes benefits), sequential dependencies (where one result must precede the next), or many tiny tasks with little per-task computation (where parallelization adds little value).

Parallel processing shines when you can split the work into independent, heavy tasks that can run at the same time without waiting on each other. In this scenario, thousands of geometry features are divided into a small number of groups by county. The spatial analysis on each feature is typically CPU-intensive, and the result for one county doesn’t depend on another. That means you can run the analysis for each county in parallel on separate cores or workers and then combine the results at the end. The workload is substantial, and the lack of inter-group dependencies makes parallel execution highly effective, so you get a meaningful speedup.

The other scenarios either involve lightweight work (where the overhead of parallelization erodes benefits), sequential dependencies (where one result must precede the next), or many tiny tasks with little per-task computation (where parallelization adds little value).