How to Serialize SWIG Objects for Parallel Processing

Introduction to Pickling SWIG Objects for Parallelization

Today, we delve into the art of serializing (or pickling) SWIG objects in Python, focusing on parallelization. This technique proves invaluable when dealing with C/C++ extensions in Python and seeking to harness the power of multi-threading or multi-processing.

What will you learn?

In this comprehensive guide, you will master the process of serializing SWIG objects using pickle. By grasping this concept, you can seamlessly integrate these objects into parallel computing scenarios within your Python applications.

Understanding Serialization with SWIG and Python

Serialization stands as a pivotal concept in parallel processing, enabling the transformation of complex objects into a format that is easily stored, transmitted, and reconstructed. However, pickling custom C/C++ objects exposed through SWIG (Simplified Wrapper and Interface Generator) presents unique challenges due to their inherent nature.

To overcome these challenges, we must implement custom pickling methods for our SWIG objects. This involves defining how these objects are converted into a byte stream during serialization and reconstructed during deserialization. This process entails leveraging Python’s pickle module alongside tailored strategies designed to handle SWIG-exposed objects effectively.


import pickle

# Assuming MyClass is a SWIG-wrapped object
class MyPickleableSWIGObject:
    def __init__(self, swig_obj):
        self.swig_obj = swig_obj

    def __getstate__(self):
        # Custom serialization logic here
        return self.serialize_something()

    def __setstate__(self, state):
        # Custom deserialization logic here
        self.swig_obj = self.deserialize_something(state)

    def serialize_something(self):
        # Logic to serialize the swig_obj attributes or state

    def deserialize_something(self, state):
        # Logic to reconstruct the swig_obj from its serialized state

# Copyright PHD


  • Understanding Pickle with Custom Objects: The __getstate__ and __setstate__ methods are instrumental in controlling what gets serialized and how an object’s state is restored.

  • Serialization/Deserialization Logic: Replace serialize_something() and deserialize_something() with logic tailored to handle your specific SWIG object’s internal details effectively.

    1. Can all types of C/C++ extensions be pickled?

      • Not all types can be pickled; only those where you have control over their serialization/deserialization process or those designed with serialization compatibility in mind can be reliably pickled.
    2. Is there any performance impact when pickling large Swig objects?

      • Yes, serialization could introduce overheads especially if not optimized properly or when dealing with very large datasets.
    3. How does this approach work with multiprocessing?

      • This method enables Swig objects wrapped inside custom classes like MyPickleableSWIGObject to be safely sent across processes as they are now serializable using typical python mechanisms utilized by multiprocessing modules.
    4. Are there alternative serialization tools apart from pickle?

      • Yes! Tools like dill or Pyro offer more sophisticated serialization options which may handle certain edge cases better at times.
    5. Does adding custom serialization break existing functionality?

      • Typically no; however, caution should always be exercised as modifying how an object is serialized/deserialized could lead to unexpected behavior if not thoroughly tested across different environments.

Serializing Swig Objects introduces new possibilities for distributing computational tasks among processors/threads seamlessly within the broader Python ecosystem. By carefully implementing these techniques and exploring alternate serialization mechanisms beyond the standard library, scalability options can be enhanced significantly. This journey promises a deeper understanding and shared knowledge base within the community, benefiting all involved parties moving forward.

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