Нет никакой реальной проблемы, я просто ищу обзор нового алгоритма сортировки, который я разработал. Этот алгоритм является новым по своему подходу, и у меня есть набор стратегий для запуска алгоритма с методом входа, чтобы определить, какую стратегию использовать. Дальше я вставлю код
using System;
using System.Collections.Generic;
using System.Linq;
using System.Numerics;
using System.Threading.Tasks;
using OpenCL.Net; // Add this using directive for OpenCL
public class GoldenRatioFractalSortSuite
{
private const double GoldenRatio = 1.61803398875;
public async Task SortAsync(int[] array)
{
int datasetSize = array.Length;
try
{
// Entrance Method: Choose the appropriate strategy based on dataset size
if (datasetSize < 100)
{
SynchronousSort(array);
}
else if (datasetSize < 1000)
{
await ParallelAsyncSort(array);
}
else if (datasetSize < 10000)
{
await SIMDEnhancedSort(array);
}
else
{
await GpuAcceleratedSort(array);
}
}
catch (AggregateException ex)
{
Console.WriteLine("An error occurred during sorting:");
foreach (var inner in ex.InnerExceptions)
{
Console.WriteLine(inner.Message);
}
}
catch (Exception ex)
{
Console.WriteLine($"An unexpected error occurred: {ex.Message}");
}
}
// Strategy 1: CPU Synchronous Sorting for small datasets
private void SynchronousSort(int[] array)
{
int numberOfGroups = CalculateNumberOfGroups(array.Length);
var groups = DistributeIntoGroups(array, numberOfGroups);
foreach (var group in groups)
{
group.Sort(); // Simple in-place sort for each group
}
MergeGroups(array, groups);
}
// Strategy 2: CPU Parallel and Async Sorting for medium datasets
private async Task ParallelAsyncSort(int[] array)
{
int numberOfGroups = CalculateNumberOfGroups(array.Length);
var groups = DistributeIntoGroups(array, numberOfGroups);
var sortTasks = groups.Select(group => Task.Run(() => group.Sort())).ToArray();
await Task.WhenAll(sortTasks);
MergeGroups(array, groups);
}
// Strategy 3: CPU + SIMD Enhanced Sorting for larger datasets
private async Task SIMDEnhancedSort(int[] array)
{
if (!Vector.IsHardwareAccelerated)
{
Console.WriteLine("SIMD is not supported on this hardware. Falling back to parallel sort.");
await ParallelAsyncSort(array); // Fallback to standard parallel sort
return;
}
int numberOfGroups = CalculateNumberOfGroups(array.Length);
var groups = await DistributeIntoGroupsSIMDAsync(array, numberOfGroups);
var sortTasks = groups.Select(group => Task.Run(() => group.Sort())).ToArray();
await Task.WhenAll(sortTasks);
MergeGroups(array, groups);
}
// Strategy 4: GPU-Accelerated Sorting using Golden Ratio Fractal Sorting Algorithm
private async Task GpuAcceleratedSort(int[] array)
{
if (!IsGpuAvailable())
{
Console.WriteLine("GPU is not available or not compatible. Falling back to SIMD-enhanced sort.");
await SIMDEnhancedSort(array); // Fallback to SIMD sort
return;
}
try
{
// Implement GPU-accelerated Golden Ratio Fractal Sorting using OpenCL
// Initialize OpenCL
ErrorCode error;
Platform[] platforms = Cl.GetPlatformIDs(out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to get OpenCL platforms.");
Device[] devices = Cl.GetDeviceIDs(platforms[0], DeviceType.Gpu, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to get OpenCL GPU devices.");
Context context = Cl.CreateContext(null, 1, devices, null, IntPtr.Zero, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL context.");
CommandQueue commandQueue = Cl.CreateCommandQueue(context, devices[0], CommandQueueProperties.None, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL command queue.");
// Prepare the kernel source code for distribution and sorting
string kernelSource = @"
__kernel void DistributeAndSort(
__global int* input,
__global int* groupIndices,
__global int* groupOffsets,
int numElements,
int numberOfGroups,
float goldenRatio)
{
int gid = get_global_id(0);
if (gid < numElements)
{
int item = input[gid];
int groupIndex = (int)(fmod((goldenRatio * item), numberOfGroups));
groupIndices[gid] = groupIndex;
}
}
__kernel void MergeGroups(
__global int* sortedGroups,
__global int* output,
__global int* groupOffsets,
int numElements)
{
int gid = get_global_id(0);
if (gid < numElements)
{
output[gid] = sortedGroups[gid];
}
}";
// Create and build program
Program program = Cl.CreateProgramWithSource(context, 1, new[] { kernelSource }, null, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL program.");
error = Cl.BuildProgram(program, 1, devices, null, null, IntPtr.Zero);
if (error != ErrorCode.Success)
{
// Get build log
string buildLog = Cl.GetProgramBuildInfo(program, devices[0], ProgramBuildInfo.Log, out error).ToString();
throw new Exception($"Failed to build OpenCL program. Build log:\n{buildLog}");
}
// Create kernels
Kernel distributeKernel = Cl.CreateKernel(program, "DistributeAndSort", out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL kernel for distribution.");
// Prepare data
int numElements = array.Length;
int numberOfGroups = CalculateNumberOfGroups(numElements);
float goldenRatio = (float)GoldenRatio;
// Create buffers
IMem inputBuffer = Cl.CreateBuffer(context, MemFlags.CopyHostPtr | MemFlags.ReadOnly, array, out error);
IMem groupIndicesBuffer = Cl.CreateBuffer(context, MemFlags.WriteOnly, numElements, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL buffers.");
// Set kernel arguments for distribution
error = Cl.SetKernelArg(distributeKernel, 0, inputBuffer);
error |= Cl.SetKernelArg(distributeKernel, 1, groupIndicesBuffer);
error |= Cl.SetKernelArg(distributeKernel, 2, IntPtr.Zero);
error |= Cl.SetKernelArg(distributeKernel, 3, numElements);
error |= Cl.SetKernelArg(distributeKernel, 4, numberOfGroups);
error |= Cl.SetKernelArg(distributeKernel, 5, goldenRatio);
if (error != ErrorCode.Success)
throw new Exception("Failed to set OpenCL kernel arguments for distribution.");
// Execute distribution kernel
IntPtr globalWorkSize = new IntPtr(numElements);
error = Cl.EnqueueNDRangeKernel(commandQueue, distributeKernel, 1, null, new[] { globalWorkSize }, null, 0, null, out _);
if (error != ErrorCode.Success)
throw new Exception("Failed to enqueue OpenCL distribution kernel.");
// Read group indices back to host
int[] groupIndices = new int[numElements];
error = Cl.EnqueueReadBuffer(commandQueue, groupIndicesBuffer, Bool.True, IntPtr.Zero, new IntPtr(sizeof(int) * numElements), groupIndices, 0, null, out _);
if (error != ErrorCode.Success)
throw new Exception("Failed to read group indices from OpenCL buffer.");
// Organize data into groups on the host
var groups = new List[numberOfGroups];
for (int i = 0; i < numberOfGroups; i++)
groups = new List();
for (int i = 0; i < numElements; i++)
{
int groupIndex = groupIndices;
groups[groupIndex].Add(array);
}
// Sort each group on the host (could also implement sorting on GPU if desired)
var sortTasks = groups.Select(group => Task.Run(() => group.Sort())).ToArray();
await Task.WhenAll(sortTasks);
// Merge groups back into a single array
int[] sortedData = new int[numElements];
int index = 0;
foreach (var group in groups)
{
foreach (var item in group)
{
sortedData[index++] = item;
}
}
// Copy sorted data back to GPU
IMem sortedDataBuffer = Cl.CreateBuffer(context, MemFlags.CopyHostPtr | MemFlags.ReadOnly, sortedData, out error);
IMem outputBuffer = Cl.CreateBuffer(context, MemFlags.WriteOnly, numElements, out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL buffers for output.");
// Create merge kernel
Kernel mergeKernel = Cl.CreateKernel(program, "MergeGroups", out error);
if (error != ErrorCode.Success)
throw new Exception("Failed to create OpenCL kernel for merging.");
// Set kernel arguments for merging
error = Cl.SetKernelArg(mergeKernel, 0, sortedDataBuffer);
error |= Cl.SetKernelArg(mergeKernel, 1, outputBuffer);
error |= Cl.SetKernelArg(mergeKernel, 2, IntPtr.Zero);
error |= Cl.SetKernelArg(mergeKernel, 3, numElements);
if (error != ErrorCode.Success)
throw new Exception("Failed to set OpenCL kernel arguments for merging.");
// Execute merge kernel
error = Cl.EnqueueNDRangeKernel(commandQueue, mergeKernel, 1, null, new[] { globalWorkSize }, null, 0, null, out _);
if (error != ErrorCode.Success)
throw new Exception("Failed to enqueue OpenCL merge kernel.");
Cl.Finish(commandQueue);
// Read the sorted data back to host memory
error = Cl.EnqueueReadBuffer(commandQueue, outputBuffer, Bool.True, IntPtr.Zero, new IntPtr(sizeof(int) * numElements), array, 0, null, out _);
if (error != ErrorCode.Success)
throw new Exception("Failed to read sorted data from OpenCL buffer.");
// Release OpenCL resources
Cl.ReleaseKernel(distributeKernel);
Cl.ReleaseKernel(mergeKernel);
Cl.ReleaseProgram(program);
Cl.ReleaseMemObject(inputBuffer);
Cl.ReleaseMemObject(groupIndicesBuffer);
Cl.ReleaseMemObject(sortedDataBuffer);
Cl.ReleaseMemObject(outputBuffer);
Cl.ReleaseCommandQueue(commandQueue);
Cl.ReleaseContext(context);
}
catch (Exception ex)
{
Console.WriteLine($"An error occurred during GPU-accelerated sorting: {ex.Message}");
await SIMDEnhancedSort(array); // Fallback to SIMD sort
}
}
// Helper to calculate the number of groups based on Golden Ratio
private int CalculateNumberOfGroups(int datasetSize)
{
if (datasetSize
{
var groups = new List[numberOfGroups];
for (int i = 0; i < numberOfGroups; i++) groups = new List();
int vectorSize = Vector.Count;
int i = 0;
// Process elements in batches of 'vectorSize' using SIMD
for (; i 0)
{
return true;
}
}
}
catch
{
// Ignore exceptions and assume GPU is not available
}
return false; // Return true if a compatible GPU is available
}
// Merging helper method to consolidate sorted groups into the main array
private void MergeGroups(int[] array, List[] groups)
{
int index = 0;
foreach (var group in groups)
{
foreach (var item in group)
{
array[index++] = item;
}
}
}
}
Подробнее здесь: https://stackoverflow.com/questions/791 ... -algorithm
