OpenCL-Wrapper

Getting started:

Key simplifications:

No need to:

Example (OpenCL vector addition)

main.cpp

#include "opencl.hpp"
int main() {
	Device device(select_device_with_most_flops()); // compile OpenCL C code for the fastest available device
	const uint N = 1024u; // size of vectors
	Memory<float> A(device, N); // allocate memory on both host and device
	Memory<float> B(device, N);
	Memory<float> C(device, N);
	Kernel add_kernel(device, N, "add_kernel", A, B, C); // kernel that runs on the device
	for(uint n=0u; n<N; n++) {
		A[n] = 3.0f; // initialize memory
		B[n] = 2.0f;
		C[n] = 1.0f;
	}
	print_info("Value before kernel execution: C[0] = "+to_string(C[0]));
	A.write_to_device(); // copy data from host memory to device memory
	B.write_to_device();
	add_kernel.run(); // run add_kernel on the device
	C.read_from_device(); // copy data from device memory to host memory
	print_info("Value after kernel execution: C[0] = "+to_string(C[0]));
	wait();
	return 0;
}

kernel.cpp

#include "kernel.hpp" // note: unbalanced round brackets () are not allowed and string literals can't be arbitrarily long, so periodically interrupt with )+R(
string opencl_c_container() { return R( // ########################## begin of OpenCL C code ####################################################################
kernel void add_kernel(global float* A, global float* B, global float* C) { // equivalent to "for(uint n=0u; n<N; n++) {", but executed in parallel
	const uint n = get_global_id(0);
	C[n] = A[n]+B[n];
}
);} // ############################################################### end of OpenCL C code #####################################################################

For comparison, the very same OpenCL vector addition example looks like this when directly using the OpenCL C++ bindings:

#define CL_HPP_MINIMUM_OPENCL_VERSION 100
#define CL_HPP_TARGET_OPENCL_VERSION 300
#include <CL/opencl.hpp>
#include "utilities.hpp"
#define WORKGROUP_SIZE 64
int main() {
	// 1. select device
	vector<cl::Device> cl_devices; // get all devices of all platforms
	{
		vector<cl::Platform> cl_platforms; // get all platforms (drivers)
		cl::Platform::get(&cl_platforms);
		for(uint i=0u; i<(uint)cl_platforms.size(); i++) {
			vector<cl::Device> cl_devices_available;
			cl_platforms[i].getDevices(CL_DEVICE_TYPE_ALL, &cl_devices_available);
			for(uint j=0u; j<(uint)cl_devices_available.size(); j++) {
				cl_devices.push_back(cl_devices_available[j]);
			}
		}
	}
	cl::Device cl_device; // select fastest available device
	{
		float best_value = 0.0f;
		uint best_i = 0u; // index of fastest device
		for(uint i=0u; i<(uint)cl_devices.size(); i++) { // find device with highest (estimated) floating point performance
			const string name = trim(cl_device.getInfo<CL_DEVICE_NAME>()); // device name
			const string vendor = trim(cl_device.getInfo<CL_DEVICE_VENDOR>()); // device vendor
			const uint compute_units = (uint)cl_device.getInfo<CL_DEVICE_MAX_COMPUTE_UNITS>(); // compute units (CUs) can contain multiple cores depending on the microarchitecture
			const uint clock_frequency = (uint)cl_device.getInfo<CL_DEVICE_MAX_CLOCK_FREQUENCY>(); // in MHz
			const bool is_gpu = cl_device.getInfo<CL_DEVICE_TYPE>()==CL_DEVICE_TYPE_GPU;
			const int vendor_id = (int)cl_device.getInfo<CL_DEVICE_VENDOR_ID>(); // AMD=0x1002, Intel=0x8086, Nvidia=0x10DE, Apple=0x1027F00
			float cores_per_cu = 1.0f;
			if(vendor_id==0x1002) { // AMD GPU/CPU
				const bool amd_128_cores_per_dualcu = contains(to_lower(name), "gfx10"); // identify RDNA/RDNA2 GPUs where dual CUs are reported
				const bool amd_256_cores_per_dualcu = contains(to_lower(name), "gfx11"); // identify RDNA3 GPUs where dual CUs are reported
				cores_per_cu = is_gpu ? (amd_256_cores_per_dualcu ? 256.0f : amd_128_cores_per_dualcu ? 128.0f : 64.0f) : 0.5f; // 64 cores/CU (GCN, CDNA), 128 cores/dualCU (RDNA, RDNA2), 256 cores/dualCU (RDNA3), 1/2 core/CU (CPUs)
			} else if(vendor_id==0x8086) { // Intel GPU/CPU
				const bool intel_16_cores_per_cu = contains_any(to_lower(name), {"gpu max", "140v", "130v", "b580", "b570"}); // identify PVC/Xe2 GPUs
				cores_per_cu = is_gpu ? (intel_16_cores_per_cu ? 16.0f : 8.0f) : 0.5f; // Intel GPUs have 16 cores/CU (PVC) or 8 cores/CU (integrated/Arc), Intel CPUs (with HT) have 1/2 core/CU
			} else if(vendor_id==0x10DE||vendor_id==0x13B5) { // Nvidia GPU/CPU
				const uint nvidia_compute_capability = 10u*(uint)cl_device.getInfo<CL_DEVICE_COMPUTE_CAPABILITY_MAJOR_NV>()+(uint)cl_device.getInfo<CL_DEVICE_COMPUTE_CAPABILITY_MINOR_NV>();
				const bool nvidia__32_cores_per_cu = (nvidia_compute_capability <30); // identify Fermi GPUs
				const bool nvidia_192_cores_per_cu = (nvidia_compute_capability>=30&&nvidia_compute_capability<50); // identify Kepler GPUs
				const bool nvidia__64_cores_per_cu = (nvidia_compute_capability>=70&&nvidia_compute_capability<80)||contains_any(to_lower(name), {"p100", "a100", "a30"}); // identify Volta, Turing, P100, A100, A30
				cores_per_cu = is_gpu ? (nvidia__32_cores_per_cu ? 32.0f : nvidia_192_cores_per_cu ? 192.0f : nvidia__64_cores_per_cu ? 64.0f : 128.0f) : 1.0f; // 32 (Fermi), 192 (Kepler), 64 (Volta, Turing, P100, A100, A30), 128 (Maxwell, Pascal, Ampere, Hopper, Ada, Blackwell) or 1 (CPUs)
			} else if(vendor_id==0x1027F00) { // Apple iGPU
				cores_per_cu = 128.0f; // Apple ARM GPUs usually have 128 cores/CU
			} else if(vendor_id==0x1022||vendor_id==0x10006||vendor_id==0x6C636F70) { // x86 CPUs with PoCL runtime
				cores_per_cu = 0.5f; // CPUs typically have 1/2 cores/CU due to SMT/hyperthreading
			} else if(contains(to_lower(vendor), "arm")) { // ARM
				cores_per_cu = is_gpu ? 8.0f : 1.0f; // ARM GPUs usually have 8 cores/CU, ARM CPUs have 1 core/CU
			}
			const uint ipc = is_gpu ? 2u : 32u; // IPC (instructions per cycle) is 2 for GPUs and 32 for most modern CPUs
			const uint cores = to_uint((float)compute_units*cores_per_cu); // for CPUs, compute_units is the number of threads (twice the number of cores with hyperthreading)
			const float tflops = 1E-6f*(float)cores*(float)ipc*(float)clock_frequency; // estimated device floating point performance in TeraFLOPs/s
			if(tflops>best_value) {
				best_value = tflops;
				best_i = i;
			}
		}
		const string name = trim(cl_devices[best_i].getInfo<CL_DEVICE_NAME>()); // device name
		cl_device = cl_devices[best_i];
		print_info(name); // print device name
	}
	// 2. embed OpenCL C code (raw string literal breaks syntax highlighting)
	string opencl_c_code = R"(
		kernel void add_kernel(global float* A, global float* B, global float* C) { // equivalent to "for(uint n=0u; n<N; n++) {", but executed in parallel
			const uint n = get_global_id(0);
			C[n] = A[n]+B[n];
		}
	)";
	// 3. compile OpenCL C code
	cl::Context cl_context;
	cl::Program cl_program;
	cl::CommandQueue cl_queue;
	{
		cl_context = cl::Context(cl_device);
		cl_queue = cl::CommandQueue(cl_context, cl_device);
		cl::CommandQueue cl_queue(cl_context, cl_device); // queue to push commands for the device
		cl::Program::Sources cl_source;
		cl_source.push_back({ opencl_c_code.c_str(), opencl_c_code.length() });
		cl_program = cl::Program(cl_context, cl_source);
		int error = cl_program.build({ cl_device }, "-cl-finite-math-only -cl-no-signed-zeros -cl-mad-enable -w"); // compile OpenCL C code, disable warnings
		if(error) print_warning(cl_program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(cl_device)); // print build log
		if(error) print_error("OpenCL C code compilation failed.");
		else print_info("OpenCL C code successfully compiled.");
	}
	// 4. allocate memory on host and device
	const uint N = 1024u;
	float* host_A;
	float* host_B;
	float* host_C;
	cl::Buffer device_A;
	cl::Buffer device_B;
	cl::Buffer device_C;
	{
		host_A = new float[N];
		host_B = new float[N];
		host_C = new float[N];
		for(uint i=0u; i<N; i++) {
			host_A[i] = 0.0f; // zero all buffers
			host_B[i] = 0.0f;
			host_C[i] = 0.0f;
		}
		int error = 0;
		device_A = cl::Buffer(cl_context, CL_MEM_READ_WRITE, N*sizeof(float), nullptr, &error);
		if(error) print_error("OpenCL Buffer allocation failed with error code "+to_string(error)+".");
		device_B = cl::Buffer(cl_context, CL_MEM_READ_WRITE, N*sizeof(float), nullptr, &error);
		if(error) print_error("OpenCL Buffer allocation failed with error code "+to_string(error)+".");
		device_C = cl::Buffer(cl_context, CL_MEM_READ_WRITE, N*sizeof(float), nullptr, &error);
		if(error) print_error("OpenCL Buffer allocation failed with error code "+to_string(error)+".");
		cl_queue.enqueueWriteBuffer(device_A, true, 0u, N*sizeof(float), (void*)host_A); // have to keep track of buffer range and buffer data type
		cl_queue.enqueueWriteBuffer(device_B, true, 0u, N*sizeof(float), (void*)host_B);
		cl_queue.enqueueWriteBuffer(device_C, true, 0u, N*sizeof(float), (void*)host_C);
	}
	// 5. create Kernel object and link input parameters
	cl::NDRange cl_range_global, cl_range_local;
	cl::Kernel cl_kernel;
	{
		cl_kernel = cl::Kernel(cl_program, "add_kernel");
		cl_kernel.setArg(0, device_A);
		cl_kernel.setArg(1, device_B);
		cl_kernel.setArg(2, device_C);
		cl_range_local = cl::NDRange(WORKGROUP_SIZE);
		cl_range_global = cl::NDRange(((N+WORKGROUP_SIZE-1)/WORKGROUP_SIZE)*WORKGROUP_SIZE); // make global range a multiple of local range
	}
	// 6. finally run the actual program
	{
		for(uint i=0u; i<N; i++) {
			host_A[i] = 3.0f; // initialize buffers on host
			host_B[i] = 2.0f;
			host_C[i] = 1.0f;
		}
		print_info("Value before kernel execution: C[0] = "+to_string(host_C[0]));
		cl_queue.enqueueWriteBuffer(device_A, true, 0u, N*sizeof(float), (void*)host_A); // copy A and B to device
		cl_queue.enqueueWriteBuffer(device_B, true, 0u, N*sizeof(float), (void*)host_B); // have to keep track of buffer range and buffer data type
		cl_queue.enqueueNDRangeKernel(cl_kernel, cl::NullRange, cl_range_global, cl_range_local); // have to keep track of kernel ranges
		cl_queue.finish(); // don't forget to finish the queue
		cl_queue.enqueueReadBuffer(device_C, true, 0u, N*sizeof(float), (void*)host_C);
		print_info("Value after kernel execution: C[0] = "+to_string(host_C[0]));
	}
	wait();
	return 0;
}