nfaggian · July 30, 2021 17:12 · Mar 25, 2014 · Mar 25, 2014
diff --git a/pool.py b/pool.py
@@ -1,3 +1,5 @@
+from __future__ import print_function
+
 import multiprocessing
 import ctypes
 import numpy as np

diff --git a/pool.py b/pool.py
@@ -0,0 +1,52 @@
+import multiprocessing
+import ctypes
+import numpy as np
+
+def shared_array(shape):
+    """
+    Form a shared memory numpy array.
+    
+    http://stackoverflow.com/questions/5549190/is-shared-readonly-data-copied-to-different-processes-for-python-multiprocessing 
+    """
+
+    shared_array_base = multiprocessing.Array(ctypes.c_double, shape[0]*shape[1])
+    shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
+    shared_array = shared_array.reshape(*shape)
+    return shared_array
+
+
+# Form a shared array and a lock, to protect access to shared memory.
+array = shared_array((1000, 1000))
+lock = multiprocessing.Lock()
+
+
+def parallel_function(i, def_param=(lock, array)):
+    """
+    Function that operates on shared memory.
+    """
+
+    # Make sure your not modifying data when someone else is.
+    lock.acquire()    
+
+    array[i, :] = i
+
+    # Always release the lock!
+    lock.release()
+
+if __name__ == '__main__':
+    """
+    The processing pool needs to be instantiated in the main 
+    thread of execution. 
+    """
+
+    pool = multiprocessing.Pool(processes=4)
+
+    # Call the parallel function with different inputs.
+    args = [(0), 
+            (1), 
+            (2)]
+
+    # Use map - blocks until all processes are done.
+    pool.map(parallel_function, args )
+
+    print(array)