it takes a long time when python return big data.
generally, when a function return something, it only take less than 1e-5 second,
but when the result is big, like np.random.rand(2048,3,224,224), the time cost will increase to 0.1-0.2 second

This is expected. Your timing measures the time for garbage collection of the large arrays in addition to the time for the result to be returned.

In the line `result = myfunc()`, the name `result` gets rebound to the value of `myfunc()`. That means that `result` is unbound from whatever it was previously bound to, and the old value then gets garbage collected.

You can test this by adding a "del result" line as the last line inside the "for" loop block.

thanks for your explaining, by the way, why it costs lots of time when del  
 a large array?

I am currently processing large data, and the time spent by del is unacceptable. Is there any way to process del in parallel?

> why it costs lots of time when del a large array?

That's probably a question for the NumPy folks, or possibly for Stack Overflow or some other question-and-answer resource. It'll depend on how NumPy arrays are de-allocated.

> Is there any way to process del in parallel?

Seems unlikely, given GIL constraints.