Update prefixSum.py

prefixSum.py

@@ -4,39 +4,57 @@ import numpy as np
 
 # Perform parallel prefix sum on the given local array.
 def prefix_mpi(local_array, rank, size):
-    
     comm = MPI.COMM_WORLD
 
-    # [Step 1]: Compute local prefix sums
+    # Start timers for computation and communication
+    computation_time = 0.0
+    communication_time = 0.0
+
+    # [Step 1]: Compute local prefix sums (computation)
+    comp_start = MPI.Wtime()
     for i in range(1, len(local_array)):
         local_array[i] += local_array[i - 1]
+    comp_end = MPI.Wtime()
+    computation_time += comp_end - comp_start
 
-    # Valuable Information printed
     print(f"[ worker #{rank} ] local prefix sum: {local_array} ")
 
-    # [Step 2]: Gather the last element of each local array to compute offsets
+    # [Step 2]: Gather the last element of each local array to compute offsets (communication)
     local_sum = np.array([local_array[-1]], dtype=int)
     all_local_sums = None
-
     if rank == 0:
         all_local_sums = np.zeros(size, dtype=int)
 
-    # Gather Blocks Results
+    comm_start = MPI.Wtime()
     comm.Gather(local_sum, all_local_sums, root=0)
+    comm_end = MPI.Wtime()
+    communication_time += comm_end - comm_start
 
     if rank == 0:
+        # Compute offsets (computation)
+        comp_start = MPI.Wtime()
         offsets = np.zeros(size, dtype=int)
         for i in range(1, size):
             offsets[i] = offsets[i - 1] + all_local_sums[i - 1]
+        comp_end = MPI.Wtime()
+        computation_time += comp_end - comp_start
     else:
         offsets = None
 
-    # Broadcast offsets to all processes
+    # Broadcast offsets to all processes (communication)
     local_offset = np.zeros(1, dtype=int)
+    comm_start = MPI.Wtime()
     comm.Scatter(offsets, local_offset, root=0)
+    comm_end = MPI.Wtime()
+    communication_time += comm_end - comm_start
 
-    # [Step 3]: Add offsets to local prefix sums
+    # [Step 3]: Add offsets to local prefix sums (computation)
+    comp_start = MPI.Wtime()
     local_array += local_offset[0]
+    comp_end = MPI.Wtime()
+    computation_time += comp_end - comp_start
+
+    return computation_time, communication_time
 
 
 def main():
@@ -48,18 +66,12 @@ def main():
     total_size = 8  # Total number of elements
     block_size = (total_size + size - 1) // size  # Block size for each process
 
-    # Root Process 
-    #   -Its generate the array 
-    #   ther scatter it 
-    #   then Gather Blocks Prefix Sums 
-    #   and compute result finaly
     if rank == 0:
-        
         # Generate an array of random integers
         global_array = np.random.randint(0, 100, total_size, dtype=int)
         print("[ master ++ ] Input array:", global_array)
 
-        # Pad the array if its not divisible by the number of processes
+        # Pad the array if it's not divisible by the number of processes
         padded_size = block_size * size
         if total_size != padded_size:
             global_array = np.pad(global_array, (0, padded_size - total_size), constant_values=0)
@@ -72,11 +84,11 @@ def main():
     # Distribute chunks of the array to all processes
     comm.Scatter(global_array, local_array, root=0)
 
-    # Start measuring time
+    # Start measuring total execution time
     start_time = MPI.Wtime()
 
-    # Perform parallel prefix sum
-    prefix_mpi(local_array, rank, size)
+    # Perform parallel prefix sum and measure computation and communication times
+    computation_time, communication_time = prefix_mpi(local_array, rank, size)
 
     # Gather results back to the root process
     if rank == 0:
@@ -86,25 +98,25 @@ def main():
 
     comm.Gather(local_array, result, root=0)
 
-    # Stop measuring time
+    # Stop measuring total execution time
     end_time = MPI.Wtime()
 
-    # Validate the result on the root process
     if rank == 0:
         # Remove padding before validation
         result = result[:total_size]
 
-        print(f"[ master ++ ] prefix sum: {result} ")
+        print(f"[ master ++ ] prefix sum: {result}")
 
         # Sequential prefix sum
         sequential_result = np.zeros_like(result, dtype=int)
-        sequential_result[0]=global_array[0]
+        sequential_result[0] = global_array[0]
         for i in range(1, total_size):
             sequential_result[i] = sequential_result[i - 1] + global_array[i]
 
-        print(f"[ ++++++ ++ ] Sequential result: {sequential_result} ")
-        # Output the execution time
-        print(f"[ ++++++ ++ ] Execution time: {end_time - start_time :.6f} seconds")
+        print(f"[ ++++++ ++ ] Sequential result: {sequential_result}")
+        print(f"[ ++++++ ++ ] Total execution time: {end_time - start_time:.6f} seconds")
+        print(f"[ ++++++ ++ ] Total computation time: {computation_time:.6f} seconds")
+        print(f"[ ++++++ ++ ] Total communication time: {communication_time:.6f} seconds")
 
         # Validate the results
         if np.array_equal(result, sequential_result):