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Microsoft Visual Studio Solution File, Format Version 12.00
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VisualStudioVersion = 17.8.34322.80
MinimumVisualStudioVersion = 10.0.40219.1
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "ConsoleApp1", "ConsoleApp1\ConsoleApp1.csproj", "{8EE37F82-A2D1-49A3-B4D0-7BF7D4A43CB0}"
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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="0.13.11" />
</ItemGroup>
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using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace ConsoleApp1
{
public class MatrixHelper
{
static readonly Random rand = new Random();
public int[,] RandomGenerateMatrix(int dimensionsCount)
{
int[,] result = new int[dimensionsCount, dimensionsCount];
for (int i = 0; i < dimensionsCount; i++)
{
for (int j = 0; j < dimensionsCount; j++)
{
result[i, j] = rand.Next(10000);
}
}
return result;
}
public int[][] MultiplicationMatrix(int[,] firstMatrix, int[,] secondMatrix, int threadCount = 1)
{
var resultMatrix = new ConcurrentDictionary<int, int>[firstMatrix.GetLength(0)]
.Select(x => new ConcurrentDictionary<int, int>())
.ToArray();
for (int i = 0; i < firstMatrix.GetLength(0); i++)
{
Parallel.For(0, secondMatrix.GetLength(1), new ParallelOptions()
{
MaxDegreeOfParallelism = threadCount
},
(j) =>
{
for (int k = 0; k < secondMatrix.GetLength(0); k++)
{
resultMatrix[i].AddOrUpdate(
j,
firstMatrix[i, k] * secondMatrix[k, j],
(key, value) => value + firstMatrix[i, k] * secondMatrix[k, j]);
}
});
}
return resultMatrix
.Select(x => x.Values.ToArray())
.ToArray();
}
}
}

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// See https://aka.ms/new-console-template for more information
using BenchmarkDotNet.Attributes;
using BenchmarkDotNet.Running;
using ConsoleApp1;
BenchmarkRunner.Run<MatrixTest>();
[MemoryDiagnoser]
public class MatrixTest
{
int[,] firstMatrix10;
int[,] secondMatrix10;
int[,] firstMatrix100;
int[,] secondMatrix100;
int[,] firstMatrix1000;
int[,] secondMatrix1000;
MatrixHelper matrixHelper;
public MatrixTest()
{
matrixHelper = new MatrixHelper();
firstMatrix10 = matrixHelper.RandomGenerateMatrix(10);
secondMatrix10 = matrixHelper.RandomGenerateMatrix(10);
firstMatrix100 = matrixHelper.RandomGenerateMatrix(100);
secondMatrix100 = matrixHelper.RandomGenerateMatrix(100);
firstMatrix1000 = matrixHelper.RandomGenerateMatrix(1000);
secondMatrix1000 = matrixHelper.RandomGenerateMatrix(1000);
}
[Benchmark]
public int[][] MultiplicationMatrix100Tread1()
{
return matrixHelper.MultiplicationMatrix(firstMatrix100, secondMatrix100, 1);
}
[Benchmark]
public int[][] MultiplicationMatrix100Tread5()
{
return matrixHelper.MultiplicationMatrix(firstMatrix100, secondMatrix100, 5);
}
[Benchmark]
public int[][] MultiplicationMatrix100Tread15()
{
return matrixHelper.MultiplicationMatrix(firstMatrix100, secondMatrix100, 15);
}
[Benchmark]
public int[][] MultiplicationMatrix1000Tread1()
{
return matrixHelper.MultiplicationMatrix(firstMatrix1000, secondMatrix1000, 1);
}
[Benchmark]
public int[][] MultiplicationMatrix1000Tread5()
{
return matrixHelper.MultiplicationMatrix(firstMatrix1000, secondMatrix1000, 5);
}
[Benchmark]
public int[][] MultiplicationMatrix1000Tread15()
{
return matrixHelper.MultiplicationMatrix(firstMatrix1000, secondMatrix1000, 15);
}
[Benchmark]
public int[][] MultiplicationMatrix10Tread1()
{
return matrixHelper.MultiplicationMatrix(firstMatrix10, secondMatrix10, 1);
}
[Benchmark]
public int[][] MultiplicationMatrix10Tread5()
{
return matrixHelper.MultiplicationMatrix(firstMatrix10, secondMatrix10, 5);
}
[Benchmark]
public int[][] MultiplicationMatrix10Tread15()
{
return matrixHelper.MultiplicationMatrix(firstMatrix10, secondMatrix10, 15);
}
}

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# Отчет по лабораторной работе №5
Выполнил студент гр. ИСЭбд-41 Миронов Е.О.
## Создание приложения
Выбрал язык C#, Консольное приложение.
Проверяю правильность работы алгоритма
```c#
public int[][] MultiplicationMatrix(int[,] firstMatrix, int[,] secondMatrix, int threadCount = 1)
{
var resultMatrix = new ConcurrentDictionary<int, int>[firstMatrix.GetLength(0)]
.Select(x => new ConcurrentDictionary<int, int>())
.ToArray();
for (int i = 0; i < firstMatrix.GetLength(0); i++)
{
Parallel.For(0, secondMatrix.GetLength(1), new ParallelOptions()
{
MaxDegreeOfParallelism = threadCount
},
(j) =>
{
for (int k = 0; k < secondMatrix.GetLength(0); k++)
{
resultMatrix[i].AddOrUpdate(
j,
firstMatrix[i, k] * secondMatrix[k, j],
(key, value) => value + firstMatrix[i, k] * secondMatrix[k, j]);
}
});
}
return resultMatrix
.Select(x => x.Values.ToArray())
.ToArray();
}
```
![](pic/1.png)
## Бенчмарки
Делаю 9 пробных запусков
Матрицы 10х10, 100х100, 1000х1000
Количество потоков 1,5,15
![](pic/2.png)
Предполагал что 15 потоков хватит, чтобы показать что бывает когда кол-во потоков в приложении больше кол-ва потоков в процессоре.
Однако этого не произошло, поэтому запускаю следующий тест на 55 потоков
![](pic/3.png)
Наблюдаем увелечение времени обработки. Это связано с с затратами на переключение контекста, синхронизацию потоков и т.п.
Вывод: Параллельный алгоритм работает быстрее чем однопоточный в случае если ресурсы процесора это позволяют.
В случае указания слишком большого числа потоков параллелльный алгоритм будет работать чуть медленее из-за накладных расходов на переключение контекста синхронизации и т.д.

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