int
MatrixMulDouble::setupCL(void)
{
cl_int status = 0;
cl_device_type dType;
if(sampleArgs->deviceType.compare("cpu") == 0)
{
dType = CL_DEVICE_TYPE_CPU;
}
else //deviceType = "gpu"
{
dType = CL_DEVICE_TYPE_GPU;
if(sampleArgs->isThereGPU() == false)
{
std::cout << "GPU not found. Falling back to CPU device" << std::endl;
dType = CL_DEVICE_TYPE_CPU;
}
}
/*
* Have a look at the available platforms and pick either
* the AMD one if available or a reasonable default.
*/
status = cl::Platform::get(&platforms);
CHECK_OPENCL_ERROR(status, "Platform::get() failed.");
std::vector<cl::Platform>::iterator i;
if(platforms.size() > 0)
{
if(sampleArgs->isPlatformEnabled())
{
i = platforms.begin() + sampleArgs->platformId;
}
else
{
for(i = platforms.begin(); i != platforms.end(); ++i)
{
if(!strcmp((*i).getInfo<CL_PLATFORM_VENDOR>().c_str(),
"Advanced Micro Devices, Inc."))
{
break;
}
}
}
}
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)(*i)(),
0
};
if(NULL == (*i)())
{
error("NULL platform found so Exiting Application.");
return SDK_FAILURE;
}
context = cl::Context(dType, cps, NULL, NULL, &status);
CHECK_OPENCL_ERROR(status, "Context::Context() failed.");
devices = context.getInfo<CL_CONTEXT_DEVICES>();
CHECK_OPENCL_ERROR(status, "Context::getInfo() failed.");
std::cout << "Platform :" << (*i).getInfo<CL_PLATFORM_VENDOR>().c_str() << "\n";
int deviceCount = (int)devices.size();
int j = 0;
for (std::vector<cl::Device>::iterator i = devices.begin(); i != devices.end();
++i, ++j)
{
std::cout << "Device " << j << " : ";
std::string deviceName = (*i).getInfo<CL_DEVICE_NAME>();
std::cout << deviceName.c_str() << "\n";
}
std::cout << "\n";
if (deviceCount == 0)
{
std::cerr << "No device available\n";
return SDK_FAILURE;
}
if(validateDeviceId(sampleArgs->deviceId, deviceCount))
{
error("validateDeviceId() failed");
return SDK_FAILURE;
}
std::string extensions =
devices[sampleArgs->deviceId].getInfo<CL_DEVICE_EXTENSIONS>();
std::string buildOptions = std::string("");
// Check if cl_khr_fp64 extension is supported
if(strstr(extensions.c_str(), "cl_khr_fp64"))
{
buildOptions.append("-D KHR_DP_EXTENSION");
}
else
{
// Check if cl_amd_fp64 extension is supported
if(!strstr(extensions.c_str(), "cl_amd_fp64"))
{
OPENCL_EXPECTED_ERROR("Device does not support cl_amd_fp64 extension!");
}
}
cl_uint localMemType;
// Get device specific information
status = devices[sampleArgs->deviceId].getInfo<cl_uint>(
CL_DEVICE_LOCAL_MEM_TYPE,
&localMemType);
CHECK_OPENCL_ERROR(status, "Device::getInfo CL_DEVICE_LOCAL_MEM_TYPE) failed.");
// If scratchpad is available then update the flag
if(localMemType == CL_LOCAL)
{
lds = true;
}
// Get Device specific Information
status = devices[sampleArgs->deviceId].getInfo<size_t>(
CL_DEVICE_MAX_WORK_GROUP_SIZE,
&maxWorkGroupSize);
CHECK_OPENCL_ERROR(status,
"Device::getInfo(CL_DEVICE_MAX_WORK_GROUP_SIZE) failed.");
status = devices[sampleArgs->deviceId].getInfo<cl_uint>(
CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS,
&maxDimensions);
CHECK_OPENCL_ERROR(status,
"Device::getInfo(CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS) failed.");
maxWorkItemSizes = (size_t*)malloc(maxDimensions * sizeof(size_t));
std::vector<size_t> workItems =
devices[sampleArgs->deviceId].getInfo<CL_DEVICE_MAX_WORK_ITEM_SIZES>();
for(cl_uint i = 0; i < maxDimensions; ++i)
{
maxWorkItemSizes[i] = workItems[i];
}
status = devices[sampleArgs->deviceId].getInfo<cl_ulong>(
CL_DEVICE_LOCAL_MEM_SIZE,
&totalLocalMemory);
CHECK_OPENCL_ERROR(status,
"Device::getInfo(CL_DEVICE_LOCAL_MEM_SIZES) failed.");
// Set command queue properties
cl_command_queue_properties prop = 0;
if(!eAppGFLOPS)
{
prop |= CL_QUEUE_PROFILING_ENABLE;
}
commandQueue = cl::CommandQueue(context, devices[sampleArgs->deviceId], prop,
&status);
CHECK_OPENCL_ERROR(status, "CommandQueue::CommandQueue() failed.");
// Set Persistent memory only for AMD platform
cl_mem_flags inMemFlags = CL_MEM_READ_ONLY;
if(sampleArgs->isAmdPlatform())
{
inMemFlags |= CL_MEM_USE_PERSISTENT_MEM_AMD;
}
// Create buffer for matrix A
inputBufA = cl::Buffer(
context,
inMemFlags,
sizeof(cl_double) * widthA * heightA,
NULL,
&status);
CHECK_OPENCL_ERROR(status, "cl::Buffer failed. (inputBufA)");
// Create buffer for matrix B
inputBufB = cl::Buffer(
context,
inMemFlags,
sizeof(cl_double) * widthB * heightB,
NULL,
&status);
CHECK_OPENCL_ERROR(status, "cl::Buffer failed. (inputBufB)");
outputBuf = cl::Buffer(
context,
CL_MEM_WRITE_ONLY | CL_MEM_ALLOC_HOST_PTR,
sizeof(cl_double) * heightA * widthB,
NULL,
&status);
CHECK_OPENCL_ERROR(status, "cl::Buffer failed. (outputBuf)");
device.push_back(devices[sampleArgs->deviceId]);
// create a CL program using the kernel source
SDKFile kernelFile;
std::string kernelPath = getPath();
if(sampleArgs->isLoadBinaryEnabled())
{
kernelPath.append(sampleArgs->loadBinary.c_str());
if(kernelFile.readBinaryFromFile(kernelPath.c_str()) != SDK_SUCCESS)
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Binaries programBinary(1,std::make_pair(
(const void*)kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, device, programBinary, NULL, &status);
CHECK_OPENCL_ERROR(status, "Program::Program(Binary) failed.");
}
else
{
kernelPath.append("MatrixMulDouble_Kernels.cl");
if(!kernelFile.open(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Sources programSource(
1,
std::make_pair(kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, programSource, &status);
CHECK_OPENCL_ERROR(status, "Program::Program(Source) failed.");
}
std::string flagsStr = std::string("");
// Get build options if any
flagsStr.append(buildOptions.c_str());
// Get additional options
if(sampleArgs->isComplierFlagsSpecified())
{
SDKFile flagsFile;
std::string flagsPath = getPath();
flagsPath.append(sampleArgs->flags.c_str());
if(!flagsFile.open(flagsPath.c_str()))
{
std::cout << "Failed to load flags file: " << flagsPath << std::endl;
return SDK_FAILURE;
}
flagsFile.replaceNewlineWithSpaces();
const char * flags = flagsFile.source().c_str();
flagsStr.append(flags);
}
if(flagsStr.size() != 0)
{
std::cout << "Build Options are : " << flagsStr.c_str() << std::endl;
}
status = program.build(device, flagsStr.c_str());
if(status != CL_SUCCESS)
{
if(status == CL_BUILD_PROGRAM_FAILURE)
{
std::string str = program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(devices[sampleArgs->deviceId]);
std::cout << " \n\t\t\tBUILD LOG\n";
std::cout << " ************************************************\n";
std::cout << str << std::endl;
std::cout << " ************************************************\n";
}
}
CHECK_OPENCL_ERROR(status, "Program::build() failed.");
// Create kernel
// If local memory is present then use the specific kernel
if(lds)
{
kernel = cl::Kernel(program, "mmmKernel_local", &status);
}
else
{
kernel = cl::Kernel(program, "mmmKernel", &status);
}
CHECK_OPENCL_ERROR(status, "cl::Kernel failed.");
status = kernel.getWorkGroupInfo<cl_ulong>(
devices[sampleArgs->deviceId],
CL_KERNEL_LOCAL_MEM_SIZE,
&usedLocalMemory);
CHECK_OPENCL_ERROR(status,
"Kernel::getWorkGroupInfo(CL_KERNEL_LOCAL_MEM_SIZE) failed"
".(usedLocalMemory)");
availableLocalMemory = totalLocalMemory - usedLocalMemory;
if(lds)
{
neededLocalMemory = (blockSize * 4) * (blockSize * 4) * sizeof(cl_double);
}
else
{
neededLocalMemory = 0;
}
if(neededLocalMemory > availableLocalMemory)
{
std::cout << "Unsupported: Insufficient local memory on device." << std::endl;
return SDK_FAILURE;
}
// Check group size against group size returned by kernel
kernelWorkGroupSize = kernel.getWorkGroupInfo<CL_KERNEL_WORK_GROUP_SIZE>
(devices[sampleArgs->deviceId], &status);
CHECK_OPENCL_ERROR(status, "Kernel::getWorkGroupInfo() failed.");
if((cl_uint)(blockSize * blockSize) > kernelWorkGroupSize)
{
if(kernelWorkGroupSize >= 64)
{
blockSize = 8;
}
else if(kernelWorkGroupSize >= 32)
{
blockSize = 4;
}
else
{
std::cout << "Out of Resources!" << std::endl;
std::cout << "Group Size specified : " << blockSize * blockSize << std::endl;
std::cout << "Max Group Size supported on the kernel : "
<< kernelWorkGroupSize<<std::endl;
return SDK_FAILURE;
}
}
if(blockSize > maxWorkItemSizes[0] ||
blockSize > maxWorkItemSizes[1] ||
blockSize * blockSize > maxWorkGroupSize)
{
error("Unsupported: Device does not support requested number of work items.");
return SDK_FAILURE;
}
return SDK_SUCCESS;
}
int
GaussianNoise::setupCL()
{
cl_int err = CL_SUCCESS;
cl_device_type dType;
if(deviceType.compare("cpu") == 0)
{
dType = CL_DEVICE_TYPE_CPU;
}
else //deviceType = "gpu"
{
dType = CL_DEVICE_TYPE_GPU;
if(isThereGPU() == false)
{
std::cout << "GPU not found. Falling back to CPU device" << std::endl;
dType = CL_DEVICE_TYPE_CPU;
}
}
/*
* Have a look at the available platforms and pick either
* the AMD one if available or a reasonable default.
*/
err = cl::Platform::get(&platforms);
CHECK_OPENCL_ERROR(err, "Platform::get() failed.");
std::vector<cl::Platform>::iterator i;
if(platforms.size() > 0)
{
if(isPlatformEnabled())
{
i = platforms.begin() + platformId;
}
else
{
for(i = platforms.begin(); i != platforms.end(); ++i)
{
if(!strcmp((*i).getInfo<CL_PLATFORM_VENDOR>().c_str(),
"Advanced Micro Devices, Inc."))
{
break;
}
}
}
}
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)(*i)(),
0
};
context = cl::Context(dType, cps, NULL, NULL, &err);
CHECK_OPENCL_ERROR(err, "Context::Context() failed.");
devices = context.getInfo<CL_CONTEXT_DEVICES>();
CHECK_OPENCL_ERROR(err, "Context::getInfo() failed.");
std::cout << "Platform :" << (*i).getInfo<CL_PLATFORM_VENDOR>().c_str() << "\n";
int deviceCount = (int)devices.size();
int j = 0;
for (std::vector<cl::Device>::iterator i = devices.begin(); i != devices.end(); ++i, ++j)
{
std::cout << "Device " << j << " : ";
std::string deviceName = (*i).getInfo<CL_DEVICE_NAME>();
std::cout << deviceName.c_str() << "\n";
}
std::cout << "\n";
if (deviceCount == 0)
{
std::cerr << "No device available\n";
return SDK_FAILURE;
}
if(sampleCommon->validateDeviceId(deviceId, deviceCount))
{
sampleCommon->error("sampleCommon::validateDeviceId() failed");
return SDK_FAILURE;
}
commandQueue = cl::CommandQueue(context, devices[deviceId], 0, &err);
CHECK_OPENCL_ERROR(err, "CommandQueue::CommandQueue() failed.");
/*
* Create and initialize memory objects
*/
// Set Presistent memory only for AMD platform
cl_mem_flags inMemFlags = CL_MEM_READ_ONLY;
if(isAmdPlatform())
inMemFlags |= CL_MEM_USE_PERSISTENT_MEM_AMD;
// Create memory object for input Image
inputImageBuffer = cl::Buffer(context,
inMemFlags,
width * height * pixelSize,
0,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (inputImageBuffer)");
// Create memory object for output Image
outputImageBuffer = cl::Buffer(context,
CL_MEM_WRITE_ONLY,
width * height * pixelSize,
NULL,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (outputImageBuffer)");
device.push_back(devices[deviceId]);
// create a CL program using the kernel source
streamsdk::SDKFile kernelFile;
std::string kernelPath = sampleCommon->getPath();
if(isLoadBinaryEnabled())
{
kernelPath.append(loadBinary.c_str());
if(!kernelFile.readBinaryFromFile(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Binaries programBinary(1,std::make_pair(
(const void*)kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, device, programBinary, NULL, &err);
CHECK_OPENCL_ERROR(err, "Program::Program(Binary) failed.");
}
else
{
kernelPath.append("GaussianNoise_Kernels.cl");
if(!kernelFile.open(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Sources programSource(1,
std::make_pair(kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, programSource, &err);
CHECK_OPENCL_ERROR(err, "Program::Program(Source) failed.");
}
std::string flagsStr = std::string("");
// Get additional options
if(isComplierFlagsSpecified())
{
streamsdk::SDKFile flagsFile;
std::string flagsPath = sampleCommon->getPath();
flagsPath.append(flags.c_str());
if(!flagsFile.open(flagsPath.c_str()))
{
std::cout << "Failed to load flags file: " << flagsPath << std::endl;
return SDK_FAILURE;
}
flagsFile.replaceNewlineWithSpaces();
const char * flags = flagsFile.source().c_str();
flagsStr.append(flags);
}
if(flagsStr.size() != 0)
std::cout << "Build Options are : " << flagsStr.c_str() << std::endl;
err = program.build(device, flagsStr.c_str());
if(err != CL_SUCCESS)
{
if(err == CL_BUILD_PROGRAM_FAILURE)
{
std::string str = program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(devices[deviceId]);
std::cout << " \n\t\t\tBUILD LOG\n";
std::cout << " ************************************************\n";
std::cout << str << std::endl;
std::cout << " ************************************************\n";
}
}
CHECK_OPENCL_ERROR(err, "Program::build() failed.");
// Create kernel
kernel = cl::Kernel(program, "gaussian_transform", &err);
CHECK_OPENCL_ERROR(err, "Kernel::Kernel() failed.");
// Check group size against group size returned by kernel
kernelWorkGroupSize = kernel.getWorkGroupInfo<CL_KERNEL_WORK_GROUP_SIZE>(devices[deviceId], &err);
CHECK_OPENCL_ERROR(err, "Kernel::getWorkGroupInfo() failed.");
if((blockSizeX * blockSizeY) > kernelWorkGroupSize)
{
if(!quiet)
{
std::cout << "Out of Resources!" << std::endl;
std::cout << "Group Size specified : "
<< blockSizeX * blockSizeY << std::endl;
std::cout << "Max Group Size supported on the kernel : "
<< kernelWorkGroupSize << std::endl;
std::cout << "Falling back to " << kernelWorkGroupSize << std::endl;
}
if(blockSizeX > kernelWorkGroupSize)
{
blockSizeX = kernelWorkGroupSize;
blockSizeY = 1;
}
}
return SDK_SUCCESS;
}
int
UnsharpMask::setupCL()
{
try
{
cl_int err = CL_SUCCESS;
cl_device_type dType;
if(sampleArgs->deviceType.compare("cpu") == 0)
{
dType = CL_DEVICE_TYPE_CPU;
}
else //deviceType == "gpu"
{
dType = CL_DEVICE_TYPE_GPU;
if(sampleArgs->isThereGPU() == false)
{
std::cout << "GPU not found. Falling back to CPU device" << std::endl;
dType = CL_DEVICE_TYPE_CPU;
}
}
/*
* Have a look at the available platforms and pick either
* the AMD one if available or a reasonable default.
*/
cl::Platform::get(&platforms);
std::vector<cl::Platform>::iterator i;
if(platforms.size() > 0)
{
if(sampleArgs->isPlatformEnabled())
{
i = platforms.begin() + sampleArgs->platformId;
}
else
{
for(i = platforms.begin(); i != platforms.end(); ++i)
{
if(!strcmp((*i).getInfo<CL_PLATFORM_VENDOR>().c_str(),
"Advanced Micro Devices, Inc."))
{
break;
}
}
}
}
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)(*i)(),
0
};
context = cl::Context(dType, cps, NULL, NULL);
devices = context.getInfo<CL_CONTEXT_DEVICES>();
std::cout << "Platform :" << (*i).getInfo<CL_PLATFORM_VENDOR>().c_str() << "\n";
int deviceCount = (int)devices.size();
int j = 0;
for (std::vector<cl::Device>::iterator i = devices.begin(); i != devices.end();
++i, ++j)
{
std::cout << "Device " << j << " : ";
std::string deviceName = (*i).getInfo<CL_DEVICE_NAME>();
std::cout << deviceName.c_str() << "\n";
}
std::cout << "\n";
if (deviceCount == 0)
{
std::cerr << "No device available\n";
return SDK_FAILURE;
}
if(validateDeviceId(sampleArgs->deviceId, deviceCount))
{
error("validateDeviceId() failed");
return SDK_FAILURE;
}
commandQueue = cl::CommandQueue(context, devices[sampleArgs->deviceId], 0);
device.push_back(devices[sampleArgs->deviceId]);
// create a CL program using the kernel source
SDKFile kernelFile;
std::string kernelPath = getPath();
if(sampleArgs->isLoadBinaryEnabled())
{
kernelPath.append(sampleArgs->loadBinary.c_str());
if(kernelFile.readBinaryFromFile(kernelPath.c_str()) != SDK_SUCCESS)
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Binaries programBinary(1,std::make_pair(
(const void*)kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, device, programBinary, NULL);
}
else
{
kernelPath.append("UnsharpMask_Kernels.cl");
if(!kernelFile.open(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Sources programSource(1,
std::make_pair(kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, programSource);
}
std::string flagsStr = std::string("");
// Get additional options
if(sampleArgs->isComplierFlagsSpecified())
{
SDKFile flagsFile;
std::string flagsPath = getPath();
flagsPath.append(sampleArgs->flags.c_str());
if(!flagsFile.open(flagsPath.c_str()))
{
std::cout << "Failed to load flags file: " << flagsPath << std::endl;
return SDK_FAILURE;
}
flagsFile.replaceNewlineWithSpaces();
const char * flags = flagsFile.source().c_str();
flagsStr.append(flags);
}
if(flagsStr.size() != 0)
{
std::cout << "Build Options are : " << flagsStr.c_str() << std::endl;
}
program.build(device, flagsStr.c_str());
queue = cl::CommandQueue(context, devices[sampleArgs->deviceId], 0, &err);
int dimen = 2*radius+1;
cl::ImageFormat format(CL_BGRA,CL_UNSIGNED_INT8);
if(loadInputImage()!=SDK_SUCCESS)
{
return SDK_FAILURE;
}
gaussian1DBuffer = cl::Buffer(context,CL_MEM_READ_ONLY, dimen*sizeof(float));
// create the 1D Gaussian kernel
if(dImageBuffer)
{
gaussian2DBuffer = cl::Buffer(context,CL_MEM_READ_ONLY,
dimen*dimen*sizeof(float));
inputBuffer = cl::Buffer(context,CL_MEM_READ_ONLY, imageSize);
outputBuffer = cl::Buffer (context,CL_MEM_WRITE_ONLY, imageSize);
unsharp_mask_filter = cl::Kernel(program, "unsharp_mask_filter");
}
else
{
inputImageObj = cl::Image2D(context, CL_MEM_READ_ONLY, format, width, height);
sharpenImageObj = cl::Image2D(context, CL_MEM_WRITE_ONLY, format, width,
height);
tmpImageObj = cl::Buffer(context,CL_MEM_READ_WRITE,
width*height*sizeof(cl_float4));
// Create kernel
unsharp_mask_pass1 = cl::Kernel(program, "unsharp_mask_pass1");
unsharp_mask_pass2 = cl::Kernel(program, "unsharp_mask_pass2");
}
}
catch (cl::Error e)
{
if(e.err() == CL_BUILD_PROGRAM_FAILURE)
{
std::string str = program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(devices[sampleArgs->deviceId]);
std::cout << " \n\t\t\tBUILD LOG\n";
std::cout << " ************************************************\n";
std::cout << str << std::endl;
std::cout << " ************************************************\n";
}
else
{
std::cout << e.what() << " failed!"<< std::endl;
std::cout << "Error code: " << e.err() << std::endl;
}
return SDK_FAILURE;
}
return SDK_SUCCESS;
}
int
HDRToneMapping::setupCL()
{
cl_int err = CL_SUCCESS;
cl_device_type dType;
if(deviceType.compare("cpu") == 0)
{
dType = CL_DEVICE_TYPE_CPU;
}
else //deviceType = "gpu"
{
dType = CL_DEVICE_TYPE_GPU;
if(isThereGPU() == false)
{
std::cout << "GPU not found. Falling back to CPU device" << std::endl;
dType = CL_DEVICE_TYPE_CPU;
}
}
/*
* Have a look at the available platforms and pick either
* the AMD one if available or a reasonable default.
*/
err = cl::Platform::get(&platforms);
CHECK_OPENCL_ERROR(err, "Platform::get() failed.");
std::vector<cl::Platform>::iterator i;
if(platforms.size() > 0)
{
if(isPlatformEnabled())
{
i = platforms.begin() + platformId;
}
else
{
for(i = platforms.begin(); i != platforms.end(); ++i)
{
if(!strcmp((*i).getInfo<CL_PLATFORM_VENDOR>().c_str(),
"Advanced Micro Devices, Inc."))
{
break;
}
}
}
}
cl_context_properties cps[3] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)(*i)(),
0
};
context = cl::Context(dType, cps, NULL, NULL, &err);
CHECK_OPENCL_ERROR(err, "Context::Context() failed.");
devices = context.getInfo<CL_CONTEXT_DEVICES>();
CHECK_OPENCL_ERROR(err, "Context::getInfo() failed.");
std::cout << "Platform :" << (*i).getInfo<CL_PLATFORM_VENDOR>().c_str() << "\n";
int deviceCount = (int)devices.size();
int j = 0;
for (std::vector<cl::Device>::iterator i = devices.begin(); i != devices.end(); ++i, ++j)
{
std::cout << "Device " << j << " : ";
std::string deviceName = (*i).getInfo<CL_DEVICE_NAME>();
std::cout << deviceName.c_str() << "\n";
}
std::cout << "\n";
if (deviceCount == 0)
{
std::cout << "No device available\n";
return SDK_FAILURE;
}
if(sampleCommon->validateDeviceId(deviceId, deviceCount) != SDK_SUCCESS)
{
std::cout << "sampleCommon::validateDeviceId() failed";
return SDK_FAILURE;
}
// Get Device specific Information
err = devices[deviceId].getInfo<size_t>(
CL_DEVICE_MAX_WORK_GROUP_SIZE,
&maxWorkGroupSize);
CHECK_OPENCL_ERROR(err, "Device::getInfo(CL_DEVICE_MAX_WORK_GROUP_SIZE) failed.");
err = devices[deviceId].getInfo<cl_uint>(
CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS,
&maxDimensions);
CHECK_OPENCL_ERROR(err, "Device::getInfo(CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS) failed.");
maxWorkItemSizes = (size_t*)malloc(maxDimensions * sizeof(size_t));
std::vector<size_t> workItems = devices[deviceId].getInfo<CL_DEVICE_MAX_WORK_ITEM_SIZES>();
for(cl_uint i = 0; i < maxDimensions; ++i)
maxWorkItemSizes[i] = workItems[i];
err = devices[deviceId].getInfo<cl_ulong>(
CL_DEVICE_LOCAL_MEM_SIZE,
&totalLocalMemory);
CHECK_OPENCL_ERROR(err, "Device::getInfo(CL_DEVICE_LOCAL_MEM_SIZES) failed.");
commandQueue = cl::CommandQueue(context, devices[deviceId], 0, &err);
CHECK_OPENCL_ERROR(err, "CommandQueue::CommandQueue() failed.");
/*
* Create and initialize memory objects
*/
// Set Presistent memory only for AMD platform
cl_mem_flags inMemFlags = CL_MEM_READ_ONLY;
if(isAmdPlatform())
inMemFlags |= CL_MEM_USE_PERSISTENT_MEM_AMD;
// Create memory object for input Image
/**
* We use CL_MEM_USE_HOST_PTR for CPU as the CPU device is running the kernel
* on the actual buffer provided by the application
*/
if (dType == CL_DEVICE_TYPE_CPU)
{
inputImageBuffer = cl::Buffer(context,
CL_MEM_USE_HOST_PTR | CL_MEM_READ_ONLY,
width * height * numChannels * sizeof(cl_float),
input,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (inputImageBuffer)");
// Create memory object for output Image
outputImageBuffer = cl::Buffer(context,
CL_MEM_WRITE_ONLY | CL_MEM_USE_HOST_PTR,
width * height * numChannels * sizeof(cl_float),
output,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (outputImageBuffer)");
}
else if (dType == CL_DEVICE_TYPE_GPU)
{
inputImageBuffer = cl::Buffer(context,
inMemFlags,
width * height * numChannels * sizeof(cl_float),
0,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (inputImageBuffer)");
// Create memory object for output Image
outputImageBuffer = cl::Buffer(context,
CL_MEM_WRITE_ONLY,
width * height * numChannels * sizeof(cl_float),
NULL,
&err);
CHECK_OPENCL_ERROR(err, "Buffer::Buffer() failed. (outputImageBuffer)");
}
device.push_back(devices[deviceId]);
// create a CL program using the kernel source
streamsdk::SDKFile kernelFile;
std::string kernelPath = sampleCommon->getPath();
if(isLoadBinaryEnabled())
{
kernelPath.append(loadBinary.c_str());
if(kernelFile.readBinaryFromFile(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Binaries programBinary(1,std::make_pair(
(const void*)kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, device, programBinary, NULL, &err);
CHECK_OPENCL_ERROR(err, "Program::Program(Binary) failed.");
}
else
{
kernelPath.append("HDRToneMapping_Kernels.cl");
if(!kernelFile.open(kernelPath.c_str()))
{
std::cout << "Failed to load kernel file : " << kernelPath << std::endl;
return SDK_FAILURE;
}
cl::Program::Sources programSource(1,
std::make_pair(kernelFile.source().data(),
kernelFile.source().size()));
program = cl::Program(context, programSource, &err);
CHECK_OPENCL_ERROR(err, "Program::Program(Source) failed.");
}
std::string flagsStr = std::string("");
// Get additional options
if(isComplierFlagsSpecified())
{
streamsdk::SDKFile flagsFile;
std::string flagsPath = sampleCommon->getPath();
flagsPath.append(flags.c_str());
if(!flagsFile.open(flagsPath.c_str()))
{
std::cout << "Failed to load flags file: " << flagsPath << std::endl;
return SDK_FAILURE;
}
flagsFile.replaceNewlineWithSpaces();
const char * flags = flagsFile.source().c_str();
flagsStr.append(flags);
}
if(flagsStr.size() != 0)
std::cout << "Build Options are : " << flagsStr.c_str() << std::endl;
err = program.build(device, flagsStr.c_str());
if(err != CL_SUCCESS)
{
if(err == CL_BUILD_PROGRAM_FAILURE)
{
std::string str = program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(devices[deviceId]);
std::cout << " \n\t\t\tBUILD LOG\n";
std::cout << " ************************************************\n";
std::cout << str << std::endl;
std::cout << " ************************************************\n";
}
}
CHECK_OPENCL_ERROR(err, "Program::build() failed.");
// Create kernel
kernel = cl::Kernel(program, "toneMappingPattanaik", &err);
CHECK_OPENCL_ERROR(err, "Kernel::Kernel() failed.");
// Check group size against group size returned by kernel
kernelWorkGroupSize = kernel.getWorkGroupInfo<CL_KERNEL_WORK_GROUP_SIZE>(devices[deviceId], &err);
CHECK_OPENCL_ERROR(err, "Kernel::getWorkGroupInfo() failed.");
/**
* For CPU device the kernel work group size is 1024.
* Workgroup creation/replacement is an overhead -
* avoid workgroups with small number of workitems (we pay more for replacing a WG than running more WI in a for loop).
*/
if (kernelWorkGroupSize >= 1024)
{
blockSizeX = 32;
blockSizeY = 32;
}
if((cl_uint)(blockSizeX * blockSizeY) > kernelWorkGroupSize)
{
if(kernelWorkGroupSize >= 64)
{
blockSizeX = 8;
blockSizeY = 8;
}
else if(kernelWorkGroupSize >= 32)
{
blockSizeX = 4;
blockSizeY = 4;
}
else
{
std::cout << "Out of Resources!" << std::endl;
std::cout << "Group Size specified : " << blockSizeX * blockSizeY << std::endl;
std::cout << "Max Group Size supported on the kernel : "
<< kernelWorkGroupSize<<std::endl;
return SDK_FAILURE;
}
}
if(blockSizeX > maxWorkItemSizes[0] ||
blockSizeY > maxWorkItemSizes[1] ||
blockSizeX * blockSizeY > maxWorkGroupSize)
{
std::cout << "Unsupported: Device does not support requested number of work items." << std::endl;
return SDK_FAILURE;
}
return SDK_SUCCESS;
}
