yakovleva_yulia_lab_5/.gitignore

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+# globs
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yakovleva_yulia_lab_5/MultiplicationLargeMatrices/MultiplicationLargeMatrices.sln

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+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio Version 17
+VisualStudioVersion = 17.9.34728.123
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MultiplicationLargeMatrices", "MultiplicationLargeMatrices\MultiplicationLargeMatrices.csproj", "{8AE2E184-71C6-4F80-A9F3-AC4C4C14E4AF}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{8AE2E184-71C6-4F80-A9F3-AC4C4C14E4AF}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{8AE2E184-71C6-4F80-A9F3-AC4C4C14E4AF}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{8AE2E184-71C6-4F80-A9F3-AC4C4C14E4AF}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{8AE2E184-71C6-4F80-A9F3-AC4C4C14E4AF}.Release|Any CPU.Build.0 = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+	GlobalSection(ExtensibilityGlobals) = postSolution
+		SolutionGuid = {41970885-F067-487E-97B2-658775C2625A}
+	EndGlobalSection
+EndGlobal

yakovleva_yulia_lab_5/MultiplicationLargeMatrices/MultiplicationLargeMatrices/MatrixMultiplication.cs

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+using System;
+using System.Threading.Tasks;
+
+public class MatrixMultiplication
+{
+    // Обычный метод умножения матриц
+    public static int[,] MultiplySequential(int[,] matrixA, int[,] matrixB)
+    {
+        int size = matrixA.GetLength(0);
+        int[,] result = new int[size, size];
+
+        for (int i = 0; i < size; i++)
+        {
+            for (int j = 0; j < size; j++)
+            {
+                int sum = 0;
+                for (int k = 0; k < size; k++)
+                {
+                    sum += matrixA[i, k] * matrixB[k, j];
+                }
+                result[i, j] = sum;
+            }
+        }
+
+        return result;
+    }
+
+    // Параллельный метод умножения матриц с заданием количества потоков
+    public static int[,] MultiplyParallel(int[,] matrixA, int[,] matrixB, int threadCount)
+    {
+        int size = matrixA.GetLength(0);
+        int[,] result = new int[size, size];
+        int chunkSize = size / threadCount;
+
+        Parallel.For(0, threadCount, threadIndex =>
+        {
+            int startRow = threadIndex * chunkSize;
+            int endRow = (threadIndex == threadCount - 1) ? size : startRow + chunkSize;
+
+            for (int i = startRow; i < endRow; i++)
+            {
+                for (int j = 0; j < size; j++)
+                {
+                    int sum = 0;
+                    for (int k = 0; k < size; k++)
+                    {
+                        sum += matrixA[i, k] * matrixB[k, j];
+                    }
+                    result[i, j] = sum;
+                }
+            }
+        });
+
+        return result;
+    }
+}

yakovleva_yulia_lab_5/MultiplicationLargeMatrices/MultiplicationLargeMatrices/MatrixMultiplicationBenchmark.cs

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+using System;
+using BenchmarkDotNet.Attributes;
+using BenchmarkDotNet.Running;
+
+public class MatrixMultiplicationBenchmark
+{
+    private int[,] matrixA100, matrixB100;
+    private int[,] matrixA300, matrixB300;
+    private int[,] matrixA500, matrixB500;
+
+    // Количество потоков
+    private int threadCount = 5;
+
+    [GlobalSetup]
+    public void Setup()
+    {
+        matrixA100 = GenerateMatrix(100);
+        matrixB100 = GenerateMatrix(100);
+
+        matrixA300 = GenerateMatrix(300);
+        matrixB300 = GenerateMatrix(300);
+
+        matrixA500 = GenerateMatrix(500);
+        matrixB500 = GenerateMatrix(500);
+    }
+
+    private int[,] GenerateMatrix(int size)
+    {
+        var random = new Random();
+        var matrix = new int[size, size];
+        for (int i = 0; i < size; i++)
+            for (int j = 0; j < size; j++)
+                matrix[i, j] = random.Next(1, 10);
+        return matrix;
+    }
+
+    [Benchmark]
+    public void MultiplySequential100x100() => MatrixMultiplication.MultiplySequential(matrixA100, matrixB100);
+
+    [Benchmark]
+    public void MultiplyParallel100x100() => MatrixMultiplication.MultiplyParallel(matrixA100, matrixB100, threadCount);
+
+    [Benchmark]
+    public void MultiplySequential300x300() => MatrixMultiplication.MultiplySequential(matrixA300, matrixB300);
+
+    [Benchmark]
+    public void MultiplyParallel300x300() => MatrixMultiplication.MultiplyParallel(matrixA300, matrixB300, threadCount);
+
+    [Benchmark]
+    public void MultiplySequential500x500() => MatrixMultiplication.MultiplySequential(matrixA500, matrixB500);
+
+    [Benchmark]
+    public void MultiplyParallel500x500() => MatrixMultiplication.MultiplyParallel(matrixA500, matrixB500, threadCount);
+}

yakovleva_yulia_lab_5/MultiplicationLargeMatrices/MultiplicationLargeMatrices/MultiplicationLargeMatrices.csproj

@@ -0,0 +1,14 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net8.0</TargetFramework>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="BenchmarkDotNet" Version="0.14.0" />
+  </ItemGroup>
+
+</Project>

yakovleva_yulia_lab_5/MultiplicationLargeMatrices/MultiplicationLargeMatrices/Program.cs

@@ -0,0 +1,9 @@
+using BenchmarkDotNet.Running;
+
+public class Program
+{
+    public static void Main(string[] args)
+    {
+        BenchmarkRunner.Run<MatrixMultiplicationBenchmark>();
+    }
+}

yakovleva_yulia_lab_5/README.md

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+# Лабораторная работа №5 - Алгоритмы перемножения матриц
+
+## Описание
+Были реализованы 2 алгоритма для перемножения больших матриц: обычный и параллельный. Для тестирования производительности обоих алгоритмов 
+был установлен пакет BenchmarkDotNet.
+
+## Прохождение tutorials:
+
+# Параметры
+Mean: Среднее время выполнения одного теста.
+
+StdErr (Standard Error): Погрешность среднего времени выполнения.
+
+N: Количество измерений — 98. Чем больше N, тем точнее статистические результаты.
+
+StdDev (Standard Deviation): Стандартное отклонение.
+
+Min и Max: Минимальное и максимальное зафиксированные значения.
+
+ConfidenceInterval: Доверительный интервал для среднего значения. Это диапазон, в который с высокой вероятностью попадёт среднее значение, если провести ещё одну серию тестов.
+
+Матрицы 100х100
+![результат работы](./images/image1.jpg)  
+
+Матрицы 300х300
+![результат работы](./images/image2.jpg)
+
+Матрицы 500х500
+![результат работы](./images/image3.jpg)
+
+Общий результат
+![результат работы](./images/image4.jpg)
+
+## Вывод
+По общему результату мы можем сделать вывод, что Параллельный алгоритм быстрее обычного за счет того, что он распределяет вычисления по нескольким потокам, что позволяет выполнять разные части задачи одновременно.
+
+## Отчет
+Работоспособность: [Видео](https://vk.com/video372869030_456239958?list=ln-gxUH2mdzTSJOvos5g3)