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Measure CPU time in nanoseconds with Python’s time.process_time_ns() for performance profiling and optimization.

# Python time.process_time_ns usage

In Python, the `time.process_time_ns()` function is used to measure the CPU time of the current process in nanoseconds. This function is particularly useful for performance profiling and benchmarking purposes.

To use `time.process_time_ns()`, you first need to import the `time` module in your Python script. Here is an example code snippet demonstrating how to use `time.process_time_ns()`:

import time

# Start measuring CPU time
start_time = time.process_time_ns()

# Perform some operations or computations here

# End measuring CPU time
end_time = time.process_time_ns()

# Calculate the elapsed CPU time in nanoseconds
elapsed_time = end_time – start_time

print(f”Elapsed time: {elapsed_time} nanoseconds”)

In the code snippet above, we import the `time` module and then use the `time.process_time_ns()` function to measure the CPU time before and after performing some operations. We then calculate the elapsed CPU time in nanoseconds by subtracting the start time from the end time.

It’s important to note that `time.process_time_ns()` measures the CPU time used by the current process and is different from wall-clock time. CPU time only accounts for the time the CPU spends executing the process and excludes time spent waiting for external resources or other processes.

When using `time.process_time_ns()` for benchmarking or profiling, it’s essential to consider factors such as system load, other processes running on the system, and the granularity of the measurements.

Overall, `time.process_time_ns()` is a valuable tool for measuring the CPU time of a Python process at a nanosecond resolution, allowing developers to optimize performance-critical parts of their code and track resource usage efficiently.