yakovleva_yulia_lab_5 is ready

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JulYakJul 2024-10-25 18:12:36 +04:00
parent 131dc39f6c
commit a5f0403627
12 changed files with 989 additions and 0 deletions

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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
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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;
}
}

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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);
}

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<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>

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using BenchmarkDotNet.Running;
public class Program
{
public static void Main(string[] args)
{
BenchmarkRunner.Run<MatrixMultiplicationBenchmark>();
}
}

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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)

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