Why Did Parallelizing a Go Moving Average Calculation with Goroutines Result in Performance Degradation?

Background

This problem involves optimizing a Go function for computing the moving average of a slice. The function is inherently embarrassingly parallel, meaning that it can be easily split into independent tasks that can be executed concurrently.

Optimization Attempts and Results

The developer attempted to parallelize the function using goroutines in two ways:

• Moving_avg_concurrent2: The slice was split into smaller pieces, and each piece was processed by a separate goroutine.
• Moving_avg_concurrent3: The master/worker paradigm was adopted, where a master goroutine spawned multiple worker goroutines to compute the moving average for different windows of the input slice.

Benchmarks showed that both concurrent approaches performed worse than the original serial function moving_avg_serial4.

Why does moving_avg_concurrent2 not scale?

The reason why moving_avg_concurrent2 does not scale is that the overhead of creating and managing goroutines outweighs the benefits of parallelism. In this case, the overhead includes the time spent on creating and scheduling the goroutines, as well as the time spent on communication and synchronization between the goroutines.

Why is moving_avg_concurrent3 that much slower than moving_avg_serial4?

The master/worker paradigm introduces additional overhead compared to the direct goroutine approach. In moving_avg_concurrent3, there is a need to create a channel for communication between the master and worker goroutines, and to manage the sending and receiving of work units. This overhead further degrades the performance of the function.

Is it possible that goroutine overhead generates so much overhead?

Yes, it is possible that goroutine overhead can significantly impact the performance of a program. Goroutines are lightweight threads, but they still have some overhead associated with their creation, scheduling, and synchronization. In the case of moving_avg_concurrent3, the overhead of managing the channel and the master/worker communication adds to the runtime of the function.

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