antonov_dmitry_lab_5/benchmark.py

@@ -1,11 +1,12 @@
+import multiprocessing
+
 import numpy as np
 import time
-import concurrent.futures
 
 
 def multiply_matrices(matrix_a, matrix_b):
     if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
+        raise ValueError("матрицы имеют разную длину")
 
     result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
 
@@ -17,29 +18,29 @@ def multiply_matrices(matrix_a, matrix_b):
     return result
 
 
-def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
-
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        futures = []
-        for i in range(len(matrix_a)):
-            futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
-
-        for i, future in enumerate(concurrent.futures.as_completed(futures)):
-            result[i] = future.result()
-
-    return result
-
-
-def _multiply_row(matrix_a, matrix_b, i):
+def multiply_row(args):
+    matrix_a, matrix_b, i = args
     row_result = [0 for _ in range(len(matrix_b[0]))]
     for j in range(len(matrix_b[0])):
         for k in range(len(matrix_b)):
             row_result[j] += matrix_a[i][k] * matrix_b[k][j]
-    return row_result
+    return row_result, i
+
+
+def multiply_matrices_parallel(matrix_a, matrix_b, threads):
+    if len(matrix_a[0]) != len(matrix_b):
+        raise ValueError("матрицы имеют разную длину")
+
+    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
+
+    with multiprocessing.Pool(processes=threads) as pool:
+        args_list = [(matrix_a, matrix_b, i) for i in range(len(matrix_a))]
+        rows_results = pool.map(multiply_row, args_list)
+
+    for row_result, row_index in rows_results:
+        result[row_index] = row_result
+
+    return result
 
 
 def benchmark_sequential(size):
@@ -66,12 +67,12 @@ def benchmark_parallel(size, num_threads):
 
 if __name__ == "__main__":
     sizes = [300]
-    threads = [2, 8]
-
-    for size in sizes:
-        sequential_time = benchmark_sequential(size)
-        print(f"Время обычное: {sequential_time} с")
-        print(f"Размер матрицы: {size}x{size}")
+    threads = [2, 16, 32]
+    #
+    # for size in sizes:
+    #     sequential_time = benchmark_sequential(size)
+    #     print(f"Время обычное: {sequential_time} с")
+    #     print(f"Размер матрицы: {size}x{size}")
 
     for thread in threads:
         for size in sizes:

antonov_dmitry_lab_5/simple_app.py

@@ -1,3 +1,5 @@
+import multiprocessing
+
 from flask import Flask, render_template, request
 import numpy as np
 import concurrent.futures
@@ -7,7 +9,7 @@ app = Flask(__name__)
 
 def multiply_matrices(matrix_a, matrix_b):
     if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
+        raise ValueError("матрицы имеют разную длину")
 
     result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
 
@@ -18,30 +20,31 @@ def multiply_matrices(matrix_a, matrix_b):
 
     return result
 
-def multiply_matrices_parallel(matrix_a, matrix_b, num_threads):
-    if len(matrix_a[0]) != len(matrix_b):
-        raise ValueError("Incompatible matrix dimensions for multiplication")
 
-    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
-
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        futures = []
-        for i in range(len(matrix_a)):
-            futures.append(executor.submit(_multiply_row, matrix_a, matrix_b, i))
-
-        for i, future in enumerate(concurrent.futures.as_completed(futures)):
-            result[i] = future.result()
-
-    return result
-
-def _multiply_row(matrix_a, matrix_b, i):
+def multiply_row(args):
+    matrix_a, matrix_b, i = args
     row_result = [0 for _ in range(len(matrix_b[0]))]
     for j in range(len(matrix_b[0])):
         for k in range(len(matrix_b)):
             row_result[j] += matrix_a[i][k] * matrix_b[k][j]
-    return row_result
+    return row_result, i
 
 
+def multiply_matrices_parallel(matrix_a, matrix_b, threads):
+    if len(matrix_a[0]) != len(matrix_b):
+        raise ValueError("матрицы имеют разную длину")
+
+    result = [[0 for _ in range(len(matrix_b[0]))] for _ in range(len(matrix_a))]
+
+    with multiprocessing.Pool(processes=threads) as pool:
+        args_list = [(matrix_a, matrix_b, i) for i in range(len(matrix_a))]
+        rows_results = pool.map(multiply_row, args_list)
+
+    for row_result, row_index in rows_results:
+        result[row_index] = row_result
+
+    return result
+
 
 @app.route('/')
 def index():
@@ -59,7 +62,7 @@ def multiply():
     if operation_type == 'sequential':
         result = multiply_matrices(matrix_a, matrix_b)
     elif operation_type == 'parallel':
-        result = multiply_matrices_parallel(matrix_a, matrix_b)
+        result = multiply_matrices_parallel(matrix_a, matrix_b, 16)
     else:
         return "Invalid operation type"