Timing array computations can be useful to understand the performance of your code and identify any potential bottlenecks. There are a few ways to time array computations in Python, depending on the specific libraries and tools you are using.
One approach is to use the time module from the Python standard library to measure the elapsed time between the start and end of a computation. For example, you can use the following code to time the computation of the sum of a NumPy array:
import numpy as npimport time# Create a large arraya = np.random.rand(1000000)# Time the sum computationstart_time = time.time()sum_a = np.sum(a)end_time = time.time()# Print the elapsed timeprint("Elapsed time: {:.4f} seconds".format(end_time - start_time)) |
This will print the elapsed time in seconds for the sum computation.
Similarly, you can time the computation of a Dask array using the time module. For example:
import dask.array as daimport time# Create a large Dask arraya = da.random.random((1000000,), chunks=(100000,))# Time the sum computationstart_time = time.time()sum_a = a.sum().compute()end_time = time.time()# Print the elapsed timeprint("Elapsed time: {:.4f} seconds".format(end_time - start_time)) |
This will print the elapsed time in seconds for the sum computation of a Dask array.
Another approach is to use profiling tools such as cProfile or line_profiler to get more detailed information about the performance of your code, such as the time spent on individual lines or functions. These tools can help you identify bottlenecks and optimize your code for better performance.