Python call shared object

Python Multiprocessing Shared Object

1. Use Python Shared Memory Objects in Multiprocessing
2. Conclusion

In Python, shared memory multiprocessing is made up of connecting multiple processors, but these processors must have direct access to the system’s main memory. This will allow all connected processors to access the other processor data they have used or created.

Use Python Shared Memory Objects in Multiprocessing

Using multiprocessing in Python, a new process can run independently and have its own memory space. By seeing the examples below, let’s understand shared object multiprocessing using Python in detail.

import multiprocessing  #an empty array globally declared answer = []  def square_numbers(mynumbers):  #for squaring array elements, a function has been used   global answer  #appending square numbers to a global array  for n in mynumbers:  answer.append(n * n)  #print a global array for generating an answer  print("Answer using first process: <>".format(answer))  if __name__ == "__main__":  #input array  mynumbers = [5,10,15]   #new process has been created  p = multiprocessing.Process(target=square_numbers, args=(mynumbers,))  #process begins here  p.start()  #wait unless a process is completed  p.join()   #print a global array for generating an answer  print("Answer using main program: <>".format(answer)) 

Answer using first process: [25, 100, 225] Answer using main program: [] 

We’ve printed global array answers in two places using the above example.

The process p is called the square_numbers function so that the array elements would be changed for process p in the memory space.

The main program is run after process p completes, and we will get the empty array as an answer in the memory space.

Multiprocessing in Python provides value objects and an array for sharing data between multiple processes.

import multiprocessing  def square_data(mydata, answer, square_sum):  #a function has been made for squaring of given data   #appending squares of mydata to the given array  for ix, n in enumerate(mydata):  answer[ix] = n * n   #sum the square values  square_sum.value = sum(answer)   #print array of squared values for process p  print("Answer in process p: <>".format(answer[:]))   # print the sum of squared values for process p  print("Sum of squares values in process p: <>".format(square_sum.value))  if __name__ == "__main__":  #here, we input the data  mydata = [1,2,3]   #an array has been created for the int data type for three integers  answer = multiprocessing.Array('i', 3)   #value has been created for int data type  square_sum = multiprocessing.Value('i')   #new process has been created  p = multiprocessing.Process(target=square_data, args=(mydata, answer, square_sum))   #process begins from here  p.start()   #wait unless the process is completed  p.join()   # print an array of squared values for the main program  print("Answer in main program: <>".format(answer[:]))   # print the sum of squared values for the main program  print("Sum of square values in main program: <>".format(square_sum.value)) 

Answer in process p: [1, 4, 9] Sum of squares in process p: 14 Answer in main program: [1, 4, 9] Sum of squares in main program: 14 

In the above example, we’ve created an array and passed three integers to it. We’ve printed an array of squared values and then a sum of square values for process p .

After this, we again printed an array of squared values and the sum of square values for the main program.

Conclusion

There could be several ways to explain shared memory multiprocessing using Python. So, in this article, we’ve explained the multiprocessing shared memory concept that how an object could be placed in shared memory space and run independently.

Apart from this, we have also learned that Python allows processes to share data between various processes.

Zeeshan is a detail oriented software engineer that helps companies and individuals make their lives and easier with software solutions.

Related Article — Python Multiprocessing

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35.6. dl — Call C functions in shared objects¶

Deprecated since version 2.6: The dl module has been removed in Python 3.0. Use the ctypes module instead.

The dl module defines an interface to the dlopen() function, which is the most common interface on Unix platforms for handling dynamically linked libraries. It allows the program to call arbitrary functions in such a library.

The dl module bypasses the Python type system and error handling. If used incorrectly it may cause segmentation faults, crashes or other incorrect behaviour.

This module will not work unless sizeof(int) == sizeof(long) == sizeof(char *) If this is not the case, SystemError will be raised on import.

The dl module defines the following function:

Open a shared object file, and return a handle. Mode signifies late binding ( RTLD_LAZY ) or immediate binding ( RTLD_NOW ). Default is RTLD_LAZY . Note that some systems do not support RTLD_NOW .

Return value is a dlobject .

The dl module defines the following constants:

Useful as an argument to open() . Note that on systems which do not support immediate binding, this constant will not appear in the module. For maximum portability, use hasattr() to determine if the system supports immediate binding.

The dl module defines the following exception:

Exception raised when an error has occurred inside the dynamic loading and linking routines.

>>> import dl, time >>> a=dl.open('/lib/libc.so.6') >>> a.call('time'), time.time() (929723914, 929723914.498) 

This example was tried on a Debian GNU/Linux system, and is a good example of the fact that using this module is usually a bad alternative.

35.6.1. Dl Objects¶

Dl objects, as returned by open() above, have the following methods:

Free all resources, except the memory.

Return the pointer for the function named name, as a number, if it exists in the referenced shared object, otherwise None . This is useful in code like:

>>> if a.sym('time'): . a.call('time') . else: . time.time() 

(Note that this function will return a non-zero number, as zero is the NULL pointer)

Call the function named name in the referenced shared object. The arguments must be either Python integers, which will be passed as is, Python strings, to which a pointer will be passed, or None , which will be passed as NULL. Note that strings should only be passed to functions as const char* , as Python will not like its string mutated.

There must be at most 10 arguments, and arguments not given will be treated as None . The function’s return value must be a C long , which is a Python integer.

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