Add Python skeleton and solutions

datatypes/README.md

@@ -4,7 +4,9 @@ Write a program that sends the highlighted elements of a 2D array
 using user defined datatypes from one MPI task to another. Note the
 different assignments for C and Fortran, and remember that C stores
 arrays in a row-major order and Fortran in a column-major order. You can
-start from skeleton codes in [C](./c) or [Fortran](./fortran)
+start from skeleton codes in [C](./c), [Fortran](./fortran) or
+[Python](./python). For Python use the C-patterns in a) and b) (`Note`: no
+Python solutions are provided for c) and d) )
 
 a) 
 

datatypes/python/skeleton.py

+from __future__ import print_function
+from mpi4py import MPI
+import numpy as np
+
+comm = MPI.COMM_WORLD
+size = comm.Get_size()
+rank = comm.Get_rank()
+
+if size != 2:
+    raise RuntimeError("Please run with two MPI tasks")
+
+data = np.zeros((8,8), int)
+if rank == 0:
+    for i in range(8):
+        data[i,:] = np.arange(1, 9) + (i+1) * 10
+
+if rank == 0:
+    print("Original data:")
+    print(data)
+
+# TODO Create the custom datatype
+# Note: Python integers are 64 bits
+
+# TODO: communicate with the datatype
+# Note: mpi4py requires that the input and output buffers are contiguous 
+# in memory. Thus, in order to send/receive from second column we need
+# to create a contiguous view starting from the second element in memory
+# which can be accomplished by flattening the array into 1d with ravel
+
+
+
+if rank == 1:
+    print("Received data:")
+    print(data)

datatypes/python/solution/columntype.py

+from __future__ import print_function
+from mpi4py import MPI
+import numpy as np
+
+comm = MPI.COMM_WORLD
+size = comm.Get_size()
+rank = comm.Get_rank()
+
+if size != 2:
+    raise RuntimeError("Please run with two MPI tasks")
+
+data = np.zeros((8,8), int)
+if rank == 0:
+    for i in range(8):
+        data[i,:] = np.arange(1, 9) + (i+1) * 10
+
+if rank == 0:
+    print("Original data:")
+    print(data)
+
+# Create the custom datatype
+# Note: Python integers are 64 bits
+columntype = MPI.INT64_T.Create_vector(8, 1, 8)
+columntype.Commit()
+
+# mpi4py requires that the input and output buffers are contiguous 
+# in memory. Thus, in order to send/receive from second column we need
+# to create a contiguous view starting from the second element in memory
+# which can be accomplished by flattening the array into 1d with ravel
+if rank == 0:
+    comm.Send((data.ravel()[1:], 1, columntype), dest=1)
+elif rank == 1:
+    comm.Recv((data.ravel()[1:], 1, columntype), source=0)
+
+if rank == 1:
+    print("Received data:")
+    print(data)

datatypes/python/solution/indexed_type.py

+from __future__ import print_function
+from mpi4py import MPI
+import numpy as np
+
+comm = MPI.COMM_WORLD
+size = comm.Get_size()
+rank = comm.Get_rank()
+
+if size != 2:
+    raise RuntimeError("Please run with two MPI tasks")
+
+data = np.zeros((8,8), int)
+if rank == 0:
+    for i in range(8):
+        data[i,:] = np.arange(1, 9) + (i+1) * 10
+
+if rank == 0:
+    print("Original data:")
+    print(data)
+
+# Create the custom datatype
+# Note: Python integers are 64 bits
+counts = np.arange(4, dtype=int) + 1
+displacements = np.arange(4, dtype=int) * (1 + 2 * data.itemsize)
+indexedtype = MPI.INT64_T.Create_indexed(counts, displacements)
+indexedtype.Commit()
+
+if rank == 0:
+    comm.Send((data, 1, indexedtype), dest=1)
+elif rank == 1:
+    comm.Recv((data, 1, indexedtype), source=0)
+
+if rank == 1:
+    print("Received data:")
+    print(data)