Why Don't Goroutines Improve Performance in this Moving Average Calculation?

Embarassedly Parallel Tasks and Go Performance

Background:

The provided code addresses an optimization task to enhance the performance of a computation involving the moving average of a data window, with a goal of achieving significant speedups using goroutines.

Question:

Why do the provided goroutine-based implementations (moving_avg_concurrent2 and moving_avg_concurrent3) not demonstrate the expected performance improvements?

Answer:

Fact #1: This Task is Not Embarassedly Parallel

The moving average calculation is inherently a sequential process. Although it operates on multiple data points, the computation depends on the previous values in the window, making it impossible to fully parallelize the operation.

Fact #2: Go's Distributed Processing Limitations

Go's distributed processing capabilities only become effective when the bulk of the processing is in parallel. In this case, the moving average calculation is primarily sequential, limiting the benefits of distribution.

Additional Considerations:

• Initialization and Synchronization Overheads: Creating and synchronizing goroutines introduces additional overhead, which can outweigh the benefits of parallel processing, especially for computations with short execution times.
• Data Partitioning and Communication: Dividing the input data into chunks for parallel processing requires additional partitioning and communication steps, which also add overhead.
• Insufficient Parallel Work: The moving average calculation has a relatively small amount of non-sequential work, making it difficult to achieve significant speedups through parallelization.

Conclusion:

While Goroutines and parallel processing can be effective for certain types of computations, they are not a silver bullet for performance improvements. In this case, the inherent sequential nature of the moving average calculation limits the benefits of parallel processing.

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