gyulaid
/
gpgpu
1
0
Fork 0
Code Issues 1 Pull Requests Projects Releases Wiki Activity
Browse Source

Implemented Simple MVM 

tg-25 #in-progress
master
Daniel Gyulai 3 years ago
parent
fe982be9ea
commit
ae5ae5e612
11 changed files with 354 additions and 13328 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 203
      Linear/Common.h
  2. 145
      Linear/Jacobi.cpp
  3. 11
      Linear/Large.cpp
  4. 179
      Linear/Linear.cpp
  5. 14
      Linear/Linear.vcxproj
  6. 25
      Linear/Linear.vcxproj.filters
  7. 62
      Linear/LinearTests.h
  8. 11
      Linear/Reduce.cpp
  9. 51
      Linear/Simple.cpp
  10. 12936
      Linear/cl.hpp
  11. 37
      kernels/linear.cl

203
Linear/Common.h
View File

@ -1,203 +0,0 @@
#pragma once
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <sstream>
#include "cl.hpp"
#pragma warning( disable : 4996 )
void printTimeStats(cl_event event)
{
cl_int err = CL_SUCCESS;
if(event == NULL)
{
std::cerr << "No event object returned!" << std::endl;
}
else
{
clWaitForEvents(1, &event);
}
cl_ulong execStart, execEnd;
err = clGetEventProfilingInfo(event, CL_PROFILING_COMMAND_START,
sizeof(cl_ulong), &execStart, NULL);
if(err != CL_SUCCESS)
{
std::cerr << "Error during profile query: CL_PROFILING_COMMAND_START [" << err << "]." << std::endl;
}
err = clGetEventProfilingInfo(event, CL_PROFILING_COMMAND_END,
sizeof(cl_ulong), &execEnd, NULL);
if(err != CL_SUCCESS)
{
std::cerr << "Error during profile query: CL_PROFILING_COMMAND_END [" << err << "]." << std::endl;
}
std::cout << "[start] " << execStart << " [end] " << execEnd << " [time] " << (execEnd - execStart) / 1e+06 << "ms." << std::endl;
}
void WriteTGA_RGB(const char* filename, unsigned char* data, unsigned int width, unsigned int height)
{
FILE *f = fopen(filename, "wb");
if (!f) {
fprintf(stderr, "Unable to create output TGA image `%s'\n", filename);
exit(EXIT_FAILURE);
}
fputc(0x00, f); /* ID Length, 0 => No ID */
fputc(0x00, f); /* Color Map Type, 0 => No color map included */
fputc(0x02, f); /* Image Type, 2 => Uncompressed, True-color Image */
fputc(0x00, f); /* Next five bytes are about the color map entries */
fputc(0x00, f); /* 2 bytes Index, 2 bytes length, 1 byte size */
fputc(0x00, f);
fputc(0x00, f);
fputc(0x00, f);
fputc(0x00, f); /* X-origin of Image */
fputc(0x00, f);
fputc(0x00, f); /* Y-origin of Image */
fputc(0x00, f);
fputc(width & 0xff, f); /* Image Width */
fputc((width >> 8) & 0xff, f);
fputc(height & 0xff, f); /* Image Height */
fputc((height >> 8) & 0xff, f);
fputc(0x18, f); /* Pixel Depth, 0x18 => 24 Bits */
fputc(0x20, f); /* Image Descriptor */
for (int y = height - 1; y >= 0; y--) {
for (size_t x = 0; x < width; x++) {
const size_t i = (y * width + x) * 3;
fputc(data[i + 2], f); /* write blue */
fputc(data[i + 1], f); /* write green */
fputc(data[i], f); /* write red */
}
}
}
std::string FileToString(const std::string& path) {
std::ifstream file(path, std::ios::in | std::ios::binary);
if (file)
{
std::ostringstream contents;
contents << file.rdbuf();
file.close();
return(contents.str());
}
return std::string();
}
const char *getErrorString(cl_int error)
{
switch (error) {
// run-time and JIT compiler errors
case 0: return "CL_SUCCESS";
case -1: return "CL_DEVICE_NOT_FOUND";
case -2: return "CL_DEVICE_NOT_AVAILABLE";
case -3: return "CL_COMPILER_NOT_AVAILABLE";
case -4: return "CL_MEM_OBJECT_ALLOCATION_FAILURE";
case -5: return "CL_OUT_OF_RESOURCES";
case -6: return "CL_OUT_OF_HOST_MEMORY";
case -7: return "CL_PROFILING_INFO_NOT_AVAILABLE";
case -8: return "CL_MEM_COPY_OVERLAP";
case -9: return "CL_IMAGE_FORMAT_MISMATCH";
case -10: return "CL_IMAGE_FORMAT_NOT_SUPPORTED";
case -11: return "CL_BUILD_PROGRAM_FAILURE";
case -12: return "CL_MAP_FAILURE";
case -13: return "CL_MISALIGNED_SUB_BUFFER_OFFSET";
case -14: return "CL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LIST";
case -15: return "CL_COMPILE_PROGRAM_FAILURE";
case -16: return "CL_LINKER_NOT_AVAILABLE";
case -17: return "CL_LINK_PROGRAM_FAILURE";
case -18: return "CL_DEVICE_PARTITION_FAILED";
case -19: return "CL_KERNEL_ARG_INFO_NOT_AVAILABLE";
// compile-time errors
case -30: return "CL_INVALID_VALUE";
case -31: return "CL_INVALID_DEVICE_TYPE";
case -32: return "CL_INVALID_PLATFORM";
case -33: return "CL_INVALID_DEVICE";
case -34: return "CL_INVALID_CONTEXT";
case -35: return "CL_INVALID_QUEUE_PROPERTIES";
case -36: return "CL_INVALID_COMMAND_QUEUE";
case -37: return "CL_INVALID_HOST_PTR";
case -38: return "CL_INVALID_MEM_OBJECT";
case -39: return "CL_INVALID_IMAGE_FORMAT_DESCRIPTOR";
case -40: return "CL_INVALID_IMAGE_SIZE";
case -41: return "CL_INVALID_SAMPLER";
case -42: return "CL_INVALID_BINARY";
case -43: return "CL_INVALID_BUILD_OPTIONS";
case -44: return "CL_INVALID_PROGRAM";
case -45: return "CL_INVALID_PROGRAM_EXECUTABLE";
case -46: return "CL_INVALID_KERNEL_NAME";
case -47: return "CL_INVALID_KERNEL_DEFINITION";
case -48: return "CL_INVALID_KERNEL";
case -49: return "CL_INVALID_ARG_INDEX";
case -50: return "CL_INVALID_ARG_VALUE";
case -51: return "CL_INVALID_ARG_SIZE";
case -52: return "CL_INVALID_KERNEL_ARGS";
case -53: return "CL_INVALID_WORK_DIMENSION";
case -54: return "CL_INVALID_WORK_GROUP_SIZE";
case -55: return "CL_INVALID_WORK_ITEM_SIZE";
case -56: return "CL_INVALID_GLOBAL_OFFSET";
case -57: return "CL_INVALID_EVENT_WAIT_LIST";
case -58: return "CL_INVALID_EVENT";
case -59: return "CL_INVALID_OPERATION";
case -60: return "CL_INVALID_GL_OBJECT";
case -61: return "CL_INVALID_BUFFER_SIZE";
case -62: return "CL_INVALID_MIP_LEVEL";
case -63: return "CL_INVALID_GLOBAL_WORK_SIZE";
case -64: return "CL_INVALID_PROPERTY";
case -65: return "CL_INVALID_IMAGE_DESCRIPTOR";
case -66: return "CL_INVALID_COMPILER_OPTIONS";
case -67: return "CL_INVALID_LINKER_OPTIONS";
case -68: return "CL_INVALID_DEVICE_PARTITION_COUNT";
// extension errors
case -1000: return "CL_INVALID_GL_SHAREGROUP_REFERENCE_KHR";
case -1001: return "CL_PLATFORM_NOT_FOUND_KHR";
case -1002: return "CL_INVALID_D3D10_DEVICE_KHR";
case -1003: return "CL_INVALID_D3D10_RESOURCE_KHR";
case -1004: return "CL_D3D10_RESOURCE_ALREADY_ACQUIRED_KHR";
case -1005: return "CL_D3D10_RESOURCE_NOT_ACQUIRED_KHR";
default: return "Unknown OpenCL error";
}
}
bool CheckCLError(cl_int err)
{
if(err != CL_SUCCESS)
{
std::cout << "OpenCL error: " << getErrorString(err) << std::endl;
return false;
}
return true;
}

145
Linear/Jacobi.cpp
View File

@ -0,0 +1,145 @@
#include "LinearTests.h"
#include <iostream>
void Jacobi::generateLinEq()
{
Jx_c[0] = new float[Jn];
Jx_c[1] = new float[Jn];
Jx_g[0] = new float[Jn];
Jx_g[1] = new float[Jn];
for (int i = 0; i < Jn; ++i) {
Jx_c[0][i] = 0.0f;
Jx_c[1][i] = 0.0f;
Jx_g[0][i] = 0.0f;
Jx_g[1][i] = 0.0f;
}
JA_c = new float[Jn * Jn];
JA_g = new float[Jn * Jn];
for (int i = 0; i < Jn; ++i) {
for (int j = 0; j < Jn; ++j) {
float v = 0.0f;
if (i == j) {
v = 0.5f;
}
JA_c[i + j * Jn] = v;
JA_g[i + j * Jn] = v;
}
}
Jb_c = new float[Jn];
Jb_g = new float[Jn];
for (int i = 0; i < Jn; ++i) {
Jb_c[i] = 1.0f;
Jb_g[i] = 1.0f;
}
}
void Jacobi::cpuScalarMV(int n, int m, float* y, const float* A, const float* x, const float* b)
{
for (int i = 0; i < n; ++i) {
float yi = b[i];
for (int j = 0; j < m; ++j) {
yi += A[i * m + j] * x[j];
}
y[i] = yi;
}
}
void Jacobi::printMatrix(int n, int m, float* A)
{
for (int i = 0; i < n; ++i) {
for (int j = 0; j < m; ++j) {
std::cout << A[j + i * n];
if (j < m - 1) std::cout << ", ";
}
std::cout << std::endl;
}
}
MatrixVectorMultiplier* Jacobi::MethodFactory(MVType type, cl::Context* context, cl::CommandQueue* queue, cl::Program* program)
{
if (type == MVType::SimpleMV) {
return new Simple(context, queue, program);
}
else if (type == MVType::ReduceMV) {
return new Reduce(context, queue, program);
}
else if (type == MVType::LargeMV) {
return new Large(context, queue, program);
}
else {
return NULL;
}
}
Jacobi::Jacobi(MVType _type)
{
type = _type;
generateLinEq();
}
void Jacobi::collect_results(cl::CommandQueue* queue)
{
}
void Jacobi::gpu_compute(cl::Context* context, cl::CommandQueue* queue, cl::Program* program, cl::Event* Event)
{
MatrixVectorMultiplier* MVMultiplier = MethodFactory(type, context, queue, program);
if (MVMultiplier != NULL) {
int inputBuffer = 0;
const int iterations = 20;
for (int i = 0; i < iterations; ++i) {
MVMultiplier->dewIt(Jn, Jn, Jx_g[(inputBuffer + 1) % 2], JA_g, Jx_g[inputBuffer], Jb_g);
printMatrix(1, Jn, Jx_g[inputBuffer]);
inputBuffer = (inputBuffer + 1) % 2;
}
}
else {
std::cout << "Invalid factory parameter" << std::endl;
exit(-1);
}
}
void Jacobi::cpu_compute()
{
int inputBuffer = 0;
const int iterations = 20;
for (int i = 0; i < iterations; ++i) {
cpuScalarMV(Jn, Jn, Jx_c[(inputBuffer + 1) % 2], JA_c, Jx_c[inputBuffer], Jb_c);
//printMatrix(1, Jn, Jx_c[inputBuffer + 1]);
inputBuffer = (inputBuffer + 1) % 2;
}
}
bool Jacobi::validate_results()
{
bool result = true;
// Actual validation
//printMatrix(Jn, Jn, JA_c);
//printMatrix(1, Jn, Jx_c[0]);
//printMatrix(1, Jn, Jx_c[1]);
// Cleanup
if (Jx_c[0] == 0) delete[] Jx_c[0];
if (Jx_c[1] == 0) delete[] Jx_c[1];
if (Jx_g[0] == 0) delete[] Jx_g[0];
if (Jx_g[1] == 0) delete[] Jx_g[1];
if (JA_c == 0) delete[] JA_c;
if (Jb_c == 0) delete[] Jb_c;
if (JA_g == 0) delete[] JA_g;
if (Jb_g == 0) delete[] Jb_g;
return result;
}
std::string Jacobi::description()
{
return std::string();
}

11
Linear/Large.cpp
View File

@ -0,0 +1,11 @@
#include "LinearTests.h"
Large::Large(cl::Context* context, cl::CommandQueue* queue, cl::Program* program)
{
//TODO: Implement
}
void Large::dewIt(int n, int m, float* y, const float* A, const float* x, const float* b)
{
//TODO: Implement
}

179
Linear/Linear.cpp
View File

@ -9,72 +9,14 @@
// OpenCL C++ API
#include "cl.hpp"
#include <iostream>
#include <OpenCLHandler.h>
#include "LinearTests.h"
// Gaussian elimination
const int GAn = 4;
const int GAm = 3;
float GA[] = { 2, 1, -1, 8,
-3, -1, 2, -11,
-2, 1, 2, -3 };
int GBn = 6;
int GBm = 3;
float GB[] = { 2, -1, 0, 1, 0, 0,
-1, 2, -1, 0, 1, 0,
0, -1, 2, 0, 0, 1 };
void scalarMV(int n, int m, float* y, const float* A, const float* x, const float* b) {
for (int i = 0; i<n; ++i) {
float yi = b[i];
for (int j = 0; j<m; ++j) {
yi += A[i * m + j] * x[j];
}
y[i] = yi;
}
}
// Jacobi iteration
const int Jn = 8;
float* Jx[2] = { NULL, NULL };
float* JA = NULL;
float* Jb = NULL;
void generateLinEq()
{
Jx[0] = new float[Jn];
Jx[1] = new float[Jn];
for (int i = 0; i < Jn; ++i) {
Jx[0][i] = 0.0f;
Jx[1][i] = 0.0f;
}
JA = new float[Jn * Jn];
for (int i = 0; i < Jn; ++i) {
for (int j = 0; j < Jn; ++j) {
float v = 0.0f;
if (i == j) {
v = 0.5f;
}
JA[i + j * Jn] = v;
}
}
Jb = new float[Jn];
for (int i = 0; i < Jn; ++i) {
Jb[i] = 1.0f;
}
}
void releaseLinEq()
{
if (Jx[0] == 0) delete[] Jx[0];
if (Jx[1] == 0) delete[] Jx[1];
if (JA == 0) delete[] JA;
if (Jb == 0) delete[] Jb;
}
void capi()
{
// Get a platform ID
@ -96,7 +38,7 @@ void capi()
queue = clCreateCommandQueue(context, deviceID, CL_QUEUE_PROFILING_ENABLE, NULL);
// Create an OpenCL program
std::string source = FileToString("../kernels/programs.cl");
std::string source = FileToString("../kernels/linear.cl");
const char* csource = source.c_str();
cl_program program = clCreateProgramWithSource(context, 1, &csource, NULL, NULL);
cl_int err = clBuildProgram(program, 1, &deviceID, NULL, NULL, NULL);
@ -110,121 +52,16 @@ void capi()
delete[] log;
exit(-1);
}
// Get the kernel handle
cl_kernel kernel = clCreateKernel(program, "gaussian", &err);
if(!CheckCLError(err)) exit(-1);
// Allocate and upload the input data
// ...
cl_mem inputBuffer;
inputBuffer = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(float) * GAn * GAm, NULL, &err);
if (!CheckCLError(err)) exit(-1);
clEnqueueWriteBuffer(queue, inputBuffer, CL_TRUE, 0, sizeof(float) * GAn * GAm, GA, 0, NULL, NULL);
// Set the kernel paramateres
clSetKernelArg(kernel, 0, sizeof(int), &GAn);
clSetKernelArg(kernel, 1, sizeof(int), &GAm);
clSetKernelArg(kernel, 2, sizeof(cl_mem), &inputBuffer);
// Enqueue the kernel
size_t workSize = GAm;
size_t workGroupSize = GAm;
clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &workSize, &workGroupSize, 0, NULL, NULL);
// Copy the result back to the host
clEnqueueReadBuffer(queue, inputBuffer, CL_TRUE, 0, sizeof(float) * GAm * GAn, GA, 0, NULL, NULL);
for (int i = 0; i < GAm; ++i) {
for (int j = 0; j < GAn; ++j) {
std::cout << GA[j + i * GAn];
if (j < GAn - 1) std::cout << ", ";
}
std::cout << std::endl;
}
clReleaseMemObject(inputBuffer);
clReleaseKernel(kernel);
std::cout << "Finished" << std::endl;
}
void cppapi()
{
cl_int err = CL_SUCCESS;
// Get a platform ID
std::vector<cl::Platform> platforms;
cl::Platform::get(&platforms);
if (platforms.size() == 0)
{
std::cout << "Unable to find suitable platform." << std::endl;
exit(-1);
}
// Create a context
cl_context_properties properties[] =
{ CL_CONTEXT_PLATFORM, (cl_context_properties)(platforms[0])(), 0 };
cl::Context context(CL_DEVICE_TYPE_GPU, properties);
// Enumerate the devices
std::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
// Create the command queue
cl::Event event;
cl::CommandQueue queue(context, devices[0], 0, &err);
// Create the OpenCL program
std::string programSource = FileToString("../kernels/programs.cl");
cl::Program program = cl::Program(context, programSource);
program.build(devices);
// Get the kernel handle
cl::Kernel kernel(program, "gaussian", &err);
CheckCLError(err);
// Allocate and upload the input data
// ...
cl::Buffer clInputBuffer = cl::Buffer(context, CL_MEM_READ_ONLY, sizeof(float) * GAn * GAm, NULL, &err);
queue.enqueueWriteBuffer(clInputBuffer, true, 0, sizeof(float) * GAn * GAm, GA);
// Set the kernel parameters
kernel.setArg(0, GAn);
kernel.setArg(1, GAm);
kernel.setArg(2, clInputBuffer);
// Enqueue the kernel
queue.enqueueNDRangeKernel(kernel,
cl::NullRange,
cl::NDRange(GAm, 1),
cl::NDRange(GAm, 1),
NULL,
&event);
event.wait();
// Copy result back to host
queue.enqueueReadBuffer(clInputBuffer, true, 0, sizeof(float) * GAn * GAm, GA);
// Validate the result
for (int i = 0; i < GAm; ++i) {
for (int j = 0; j < GAn; ++j) {
std::cout << GA[j + i * GAn];
if (j < GAn - 1) std::cout << ", ";
}
std::cout << std::endl;
}
std::cout << "Finished" << std::endl;
}
int main()
{
capi();
cppapi();
//cppapi();
OpenCLHandler handler("../kernels/linear.cl");
Jacobi j(MVType::SimpleMV);
handler.run_test(&j);
return 0;
}

14
Linear/Linear.vcxproj
View File

@ -45,12 +45,13 @@
<ClCompile>
<WarningLevel>Level3</WarningLevel>
<Optimization>Disabled</Optimization>
<AdditionalIncludeDirectories>$(CUDA_PATH)\include</AdditionalIncludeDirectories>
<AdditionalIncludeDirectories>..\Common;$(CUDA_PATH)\include</AdditionalIncludeDirectories>
</ClCompile>
<Link>
<GenerateDebugInformation>true</GenerateDebugInformation>
<AdditionalLibraryDirectories>$(CUDA_PATH)\lib\Win32\</AdditionalLibraryDirectories>
<AdditionalDependencies>OpenCL.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)</AdditionalDependencies>
<SubSystem>Console</SubSystem>
</Link>
</ItemDefinitionGroup>
<ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
@ -70,11 +71,11 @@
</Link>
</ItemDefinitionGroup>
<ItemGroup>
<ClInclude Include="cl.hpp" />
<ClInclude Include="Common.h" />
</ItemGroup>
<ItemGroup>
<ClCompile Include="Jacobi.cpp" />
<ClCompile Include="Large.cpp" />
<ClCompile Include="Linear.cpp" />
<ClCompile Include="Reduce.cpp" />
<ClCompile Include="Simple.cpp" />
</ItemGroup>
<ItemGroup>
<None Include="..\kernels\linear.cl" />
@ -84,6 +85,9 @@
<Project>{f66311cb-c60d-43de-890c-7e6d8179ca44}</Project>
</ProjectReference>
</ItemGroup>
<ItemGroup>
<ClInclude Include="LinearTests.h" />
</ItemGroup>
<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
<ImportGroup Label="ExtensionTargets">
</ImportGroup>

25
Linear/Linear.vcxproj.filters
View File

@ -17,22 +17,31 @@
<UniqueIdentifier>{fd13ccc5-a98b-4e30-9eba-12f62c7dd566}</UniqueIdentifier>
</Filter>
</ItemGroup>
<ItemGroup>
<ClInclude Include="cl.hpp">
<Filter>Header Files</Filter>
</ClInclude>
<ClInclude Include="Common.h">
<Filter>Header Files</Filter>
</ClInclude>
</ItemGroup>
<ItemGroup>
<ClCompile Include="Linear.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="Jacobi.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="Simple.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="Reduce.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="Large.cpp">
<Filter>Source Files</Filter>
</ClCompile>
</ItemGroup>
<ItemGroup>
<None Include="..\kernels\linear.cl">
<Filter>Kernels</Filter>
</None>
</ItemGroup>
<ItemGroup>
<ClInclude Include="LinearTests.h">
<Filter>Header Files</Filter>
</ClInclude>
</ItemGroup>
</Project>

62
Linear/LinearTests.h
View File

@ -0,0 +1,62 @@
#pragma once
#include "Tests.h"
enum class MVType {
SimpleMV, ReduceMV, LargeMV
};
class MatrixVectorMultiplier {
protected:
cl::Context* context;
cl::CommandQueue* queue;
cl::Program* program;
public:
virtual void dewIt(int n, int m, float* y, const float* A, const float* x, const float* b) = 0;
};
class Jacobi : public TestCase {
private:
const int Jn = 8;
// CPU
float* Jx_c[2] = { NULL, NULL };
float* JA_c = NULL;
float* Jb_c = NULL;
// GPU
float* Jx_g[2] = { NULL, NULL };
float* JA_g = NULL;
float* Jb_g = NULL;
MVType type;
void generateLinEq();
void cpuScalarMV(int n, int m, float* y, const float* A, const float* x, const float* b);
void printMatrix(int n, int m, float* A);
MatrixVectorMultiplier* MethodFactory(MVType type, cl::Context* context, cl::CommandQueue* queue, cl::Program* program);
public:
Jacobi(MVType type);
void collect_results(cl::CommandQueue* queue);
void gpu_compute(cl::Context* context, cl::CommandQueue* queue, cl::Program* program, cl::Event* Event);
void cpu_compute();
bool validate_results();
std::string description();
};
class Simple : public MatrixVectorMultiplier {
public:
Simple(cl::Context* context, cl::CommandQueue* queue, cl::Program* program);
void dewIt(int n, int m, float* y, const float* A, const float* x, const float* b);
};
class Reduce : public MatrixVectorMultiplier {
public:
Reduce(cl::Context* context, cl::CommandQueue* queue, cl::Program* program);
void dewIt(int n, int m, float* y, const float* A, const float* x, const float* b);
};
class Large : public MatrixVectorMultiplier {
public:
Large(cl::Context* context, cl::CommandQueue* queue, cl::Program* program);
void dewIt(int n, int m, float* y, const float* A, const float* x, const float* b);
};

11
Linear/Reduce.cpp
View File

@ -0,0 +1,11 @@
#include "LinearTests.h"
Reduce::Reduce(cl::Context* context, cl::CommandQueue* queue, cl::Program* program)
{
//TODO: Implement
}
void Reduce::dewIt(int n, int m, float* y, const float* A, const float* x, const float* b)
{
//TODO: Implement
}

51
Linear/Simple.cpp
View File

@ -0,0 +1,51 @@
#include "LinearTests.h"
#include <Common.h>
Simple::Simple(cl::Context* _context, cl::CommandQueue* _queue, cl::Program* _program)
{
context = _context;
queue = _queue;
program = _program;
}
void Simple::dewIt(int n, int m, float* y, const float* A, const float* x, const float* b)
{
cl_int err = CL_SUCCESS;
cl::Event _event;
cl::Kernel kernel = cl::Kernel(*program, "simpleMV", &err);
CheckCLError(err);
cl::Buffer ABuffer(*context, CL_MEM_READ_ONLY, sizeof(float) * n *m, NULL, &err);
CheckCLError(err);
queue->enqueueWriteBuffer(ABuffer, true, 0, sizeof(float) * n *m, A);
cl::Buffer XBuffer(*context, CL_MEM_READ_ONLY, sizeof(float) * m, NULL, &err); // Ot kell kiirogatni
CheckCLError(err);
queue->enqueueWriteBuffer(XBuffer, true, 0, sizeof(float) * m, x);
cl::Buffer YBuffer(*context, CL_MEM_WRITE_ONLY, sizeof(float) * n, NULL, &err);
CheckCLError(err);
queue->enqueueWriteBuffer(YBuffer, true, 0, sizeof(float) * n, y);
cl::Buffer BBuffer(*context, CL_MEM_READ_ONLY, sizeof(float) * n, NULL, &err);
CheckCLError(err);
queue->enqueueWriteBuffer(BBuffer, true, 0, sizeof(float) * n, b);
kernel.setArg(0, n);
kernel.setArg(1, m);
kernel.setArg(2, YBuffer);
kernel.setArg(3, ABuffer);
kernel.setArg(4, XBuffer);
kernel.setArg(5, BBuffer);
queue->enqueueNDRangeKernel(kernel,
cl::NullRange, // Indexek nem eloffszetelve
cl::NDRange(n, 1), // Minden elemet egy szál
cl::NullRange, // Workgroup méret? - ez az auto, ha nem indul, 1024-re, onnan csökkent, amig elindul
NULL, //
&_event);
_event.wait();
queue->enqueueReadBuffer(YBuffer, true, 0, sizeof(int) * n, y);
}

12936
Linear/cl.hpp
View File

File diff suppressed because it is too large

37
kernels/linear.cl
View File

@ -11,7 +11,15 @@
// END IF
__kernel
void simpleMV(const int n, const int m, __global float* y, __global float* A, __global float* x, __global float* b){
int i = get_local_id(0);
if (i < n) {
float yi = b[i];
for (int j = 0; j < m; j++) {
yi += A[j + i * m] * x[j];
}
y[i] = yi;
}
}
// TODO: Matrix-vector multiplication with parallelization of the dot product
@ -30,7 +38,8 @@ void simpleMV(const int n, const int m, __global float* y, __global float* A, __
//
__kernel
void reduceMV(const int n, const int M, __global float* y, __global float* A, __global float* x, __global float* b, __local float* Q){
int i = get_group_id(0);
int j = get_local_id(0);
}
// TODO: General solution for matrix-vector multiplication, every thread processes a chunk of the dot product and visits multiple rows of the result
@ -51,8 +60,34 @@ void largeMV(const int n, const int m, __global float* y, __global float* A, __g
}
// TODO: Gaussian elimination as shown in the lecture
// for k := 1 .. n-1 do
// for i : = k + 1 ..n do
// l : = aik / akk
// bi : = bi – l * bk
// for j : = k ..n do
// aij : = aij – l * akj
// end for
// end for
// end for
// (execute the 2nd loop of the sequential implemential in parallel)
__kernel void gaussian(const int n, const int m, __global float* A){
int i = get_global_id(0);
int lid = get_local_id(0);
/*if (i < n) {
for (size_t k = 1; k < n - 1; k++) {
int l = A[k * i] / A[k * k];
for (size_t j = k; k < n; j++) {
A[i * j] = A[i * j] - l * A[k * j];
}
}
}*/
for (size_t k = 1; k < n - 1; k++) {
float l = A[k * n + i] / A[k * n + k];
for (size_t j = k; j < n; j++) {
A[i * n + j] = A[i * n + j] - l * A[k * n + j];
}
}
}

Write Preview
Loading…
Cancel
Save