Is Readonly Data Shared or Copied in Multiprocessing Environments?

Sharing Readonly Data in Multiprocessing: Copying or Sharing?

In multiprocessing environments, data sharing is crucial for performance optimization. A common question arises: are readonly data shared or copied across different processes?

The code snippet provided highlights a concern regarding a large global array (glbl_array) passed to multiple worker processes within a multiprocessing pool. The question arises if the array is shared or copied, potentially leading to significant memory overhead.

Using Numpy and Shared Memory for Data Sharing

To ensure shared access to readonly data, one approach mentioned in the answer is utilizing shared memory from multiprocessing along with Numpy. Here's how:

<code class="python">import multiprocessing
import ctypes
import numpy as np

shared_array_base = multiprocessing.Array(ctypes.c_double, 10*10)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(10, 10)</code>

This code creates a shared memory object (shared_array_base) using the multiprocessing package and converts it into a Numpy array (shared_array). Subsequent use of shared_array in worker processes will operate on the shared memory, avoiding unnecessary data copying.

Copy-on-Write Semantics in Linux

Additionally, it's worth noting that Linux utilizes copy-on-write semantics on fork(). This implies that even without using explicit shared memory techniques, the readonly data will only be copied when it is modified. Therefore, as long as the array remains unchanged, it will be shared without incurring any copying overhead.

Conclusion

Whether readonly data is shared or copied in multiprocessing depends on the specific implementation. Using Numpy with shared memory provides a reliable method for ensuring data sharing, while Linux's copy-on-write semantics may also contribute to avoiding unnecessary copying. By carefully considering these factors, programmers can optimize their multiprocessing applications for efficient data sharing.

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