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// =================================================================================================
// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
// width of 100 characters per line.
//
// Author(s):
// Cedric Nugteren <www.cedricnugteren.nl>
//
// This file implements a bunch of C++11 classes that act as wrappers around OpenCL objects and API
// calls. The main benefits are increased abstraction, automatic memory management, and portability.
// Portability here means that a similar header exists for CUDA with the same classes and
// interfaces. In other words, moving from the OpenCL API to the CUDA API becomes a one-line change.
//
// This file is taken from the CLCudaAPI project <https://github.com/CNugteren/CLCudaAPI> and
// therefore contains the following header copyright notice:
//
// =================================================================================================
//
// Copyright 2015 SURFsara
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// =================================================================================================
#ifndef CLBLAST_CUPP11_H_
#define CLBLAST_CUPP11_H_
// C++
#include <algorithm> // std::copy
#include <string> // std::string
#include <vector> // std::vector
#include <memory> // std::shared_ptr
#include <cstring> // std::strlen
// CUDA
#define CUDA_NO_HALF // Incompatible with CLBlast's definition; TODO: resolve this
#include <cuda.h> // CUDA driver API
#include <nvrtc.h> // NVIDIA runtime compilation API
// Exception classes
#include "cxpp11_common.hpp"
namespace clblast {
// =================================================================================================
// Max-length of strings
constexpr auto kStringLength = 256;
// =================================================================================================
// Represents a runtime error returned by a CUDA driver API function
class CLCudaAPIError : public ErrorCode<DeviceError, CUresult> {
public:
explicit CLCudaAPIError(CUresult status, const std::string &where):
ErrorCode(status, where, "CUDA error: " + where + ": " +
GetErrorName(status) + " --> " + GetErrorString(status)) {
}
static void Check(const CUresult status, const std::string &where) {
if (status != CUDA_SUCCESS) {
throw CLCudaAPIError(status, where);
}
}
static void CheckDtor(const CUresult status, const std::string &where) {
if (status != CUDA_SUCCESS) {
fprintf(stderr, "CLCudaAPI: %s (ignoring)\n", CLCudaAPIError(status, where).what());
}
}
private:
std::string GetErrorName(CUresult status) const {
const char* status_code;
cuGetErrorName(status, &status_code);
return std::string(status_code);
}
std::string GetErrorString(CUresult status) const {
const char* status_string;
cuGetErrorString(status, &status_string);
return std::string(status_string);
}
};
// Represents a runtime error returned by a CUDA runtime compilation API function
class CLCudaAPINVRTCError : public ErrorCode<DeviceError, nvrtcResult> {
public:
explicit CLCudaAPINVRTCError(nvrtcResult status, const std::string &where):
ErrorCode(status, where, "CUDA NVRTC error: " + where + ": " + GetErrorString(status)) {
}
static void Check(const nvrtcResult status, const std::string &where) {
if (status != NVRTC_SUCCESS) {
throw CLCudaAPINVRTCError(status, where);
}
}
static void CheckDtor(const nvrtcResult status, const std::string &where) {
if (status != NVRTC_SUCCESS) {
fprintf(stderr, "CLCudaAPI: %s (ignoring)\n", CLCudaAPINVRTCError(status, where).what());
}
}
private:
std::string GetErrorString(nvrtcResult status) const {
const char* status_string = nvrtcGetErrorString(status);
return std::string(status_string);
}
};
// Exception returned when building a program
using CLCudaAPIBuildError = CLCudaAPINVRTCError;
// =================================================================================================
// Error occurred in CUDA driver or runtime compilation API
#define CheckError(call) CLCudaAPIError::Check(call, CLCudaAPIError::TrimCallString(#call))
#define CheckErrorNVRTC(call) CLCudaAPINVRTCError::Check(call, CLCudaAPINVRTCError::TrimCallString(#call))
// Error occurred in CUDA driver or runtime compilation API (no-exception version for destructors)
#define CheckErrorDtor(call) CLCudaAPIError::CheckDtor(call, CLCudaAPIError::TrimCallString(#call))
#define CheckErrorDtorNVRTC(call) CLCudaAPINVRTCError::CheckDtor(call, CLCudaAPINVRTCError::TrimCallString(#call))
// =================================================================================================
// C++11 version of two 'CUevent' pointers
class Event {
public:
// Note that there is no constructor based on the regular CUDA data-type because of extra state
// Regular constructor with memory management
explicit Event():
start_(new CUevent, [](CUevent* e) { CheckErrorDtor(cuEventDestroy(*e)); delete e; }),
end_(new CUevent, [](CUevent* e) { CheckErrorDtor(cuEventDestroy(*e)); delete e; }) {
CheckError(cuEventCreate(start_.get(), CU_EVENT_DEFAULT));
CheckError(cuEventCreate(end_.get(), CU_EVENT_DEFAULT));
}
// Waits for completion of this event (not implemented for CUDA)
void WaitForCompletion() const { } // not needed due to cuStreamSynchronize call after each kernel launch
// Retrieves the elapsed time of the last recorded event
float GetElapsedTime() const {
auto result = 0.0f;
cuEventElapsedTime(&result, *start_, *end_);
return result;
}
// Accessors to the private data-members
const CUevent& start() const { return *start_; }
const CUevent& end() const { return *end_; }
Event* pointer() { return this; }
private:
std::shared_ptr<CUevent> start_;
std::shared_ptr<CUevent> end_;
};
// Pointer to a CUDA event
using EventPointer = Event*;
// =================================================================================================
// Raw platform ID type
using RawPlatformID = size_t;
// The CUDA platform: initializes the CUDA driver API
class Platform {
public:
// Initializes the platform. Note that the platform ID variable is not actually used for CUDA.
explicit Platform(const size_t platform_id) : platform_id_(0) {
if (platform_id != 0) { throw LogicError("CUDA back-end requires a platform ID of 0"); }
CheckError(cuInit(0));
}
// Methods to retrieve platform information
std::string Name() const { return "CUDA"; }
std::string Vendor() const { return "NVIDIA Corporation"; }
std::string Version() const {
auto result = 0;
CheckError(cuDriverGetVersion(&result));
return "CUDA driver "+std::to_string(result);
}
// Returns the number of devices on this platform
size_t NumDevices() const {
auto result = 0;
CheckError(cuDeviceGetCount(&result));
return static_cast<size_t>(result);
}
// Accessor to the raw ID (which doesn't exist in the CUDA back-end, this is always just 0)
const RawPlatformID& operator()() const { return platform_id_; }
private:
const size_t platform_id_;
};
// Retrieves a vector with all platforms. Note that there is just one platform in CUDA.
inline std::vector<Platform> GetAllPlatforms() {
auto all_platforms = std::vector<Platform>{ Platform(size_t{0}) };
return all_platforms;
}
// =================================================================================================
// Raw device ID type
using RawDeviceID = CUdevice;
// C++11 version of 'CUdevice'
class Device {
public:
// Constructor based on the regular CUDA data-type
explicit Device(const CUdevice device): device_(device) { }
// Initialization
explicit Device(const Platform &platform, const size_t device_id) {
auto num_devices = platform.NumDevices();
if (num_devices == 0) {
throw RuntimeError("Device: no devices found");
}
if (device_id >= num_devices) {
throw RuntimeError("Device: invalid device ID "+std::to_string(device_id));
}
CheckError(cuDeviceGet(&device_, device_id));
}
// Methods to retrieve device information
RawPlatformID PlatformID() const { return 0; }
std::string Version() const {
auto result = 0;
CheckError(cuDriverGetVersion(&result));
return "CUDA driver "+std::to_string(result);
}
size_t VersionNumber() const {
auto result = 0;
CheckError(cuDriverGetVersion(&result));
return static_cast<size_t>(result);
}
std::string Vendor() const { return "NVIDIA Corporation"; }
std::string Name() const {
auto result = std::string{};
result.resize(kStringLength);
CheckError(cuDeviceGetName(&result[0], result.size(), device_));
result.resize(strlen(result.c_str())); // Removes any trailing '\0'-characters
return result;
}
std::string Type() const { return "GPU"; }
size_t MaxWorkGroupSize() const {return GetInfo(CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK); }
size_t MaxWorkItemDimensions() const { return size_t{3}; }
std::vector<size_t> MaxWorkItemSizes() const {
return std::vector<size_t>{GetInfo(CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X),
GetInfo(CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y),
GetInfo(CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z)};
}
unsigned long LocalMemSize() const {
return static_cast<unsigned long>(GetInfo(CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK));
}
std::string Capabilities() const {
const auto major = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR);
const auto minor = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR);
return "SM"+std::to_string(major)+"."+std::to_string(minor);
}
std::string ComputeArch() const {
const auto major = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR);
const auto minor = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR);
return "compute_"+std::to_string(major)+std::to_string(minor);
}
bool HasExtension(const std::string &extension) const { return false; }
bool SupportsFP64() const { return true; }
bool SupportsFP16() const {
const auto major = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR);
const auto minor = GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR);
if (major > 5) { return true; } // SM 6.x, 7.x and higher
if (major == 5 && minor == 3) { return true; } // SM 5.3
return false;
}
size_t CoreClock() const { return 1e-3*GetInfo(CU_DEVICE_ATTRIBUTE_CLOCK_RATE); }
size_t ComputeUnits() const { return GetInfo(CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT); }
unsigned long MemorySize() const {
auto result = size_t{0};
CheckError(cuDeviceTotalMem(&result, device_));
return static_cast<unsigned long>(result);
}
unsigned long MaxAllocSize() const { return MemorySize(); }
size_t MemoryClock() const { return 1e-3*GetInfo(CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE); }
size_t MemoryBusWidth() const { return GetInfo(CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH); }
// Configuration-validity checks
bool IsLocalMemoryValid(const size_t local_mem_usage) const {
return (local_mem_usage <= LocalMemSize());
}
bool IsThreadConfigValid(const std::vector<size_t> &local) const {
auto local_size = size_t{1};
for (const auto &item: local) { local_size *= item; }
for (auto i=size_t{0}; i<local.size(); ++i) {
if (local[i] > MaxWorkItemSizes()[i]) { return false; }
}
if (local_size > MaxWorkGroupSize()) { return false; }
if (local.size() > MaxWorkItemDimensions()) { return false; }
return true;
}
// Query for a specific type of device or brand
bool IsCPU() const { return false; }
bool IsGPU() const { return true; }
bool IsAMD() const { return false; }
bool IsNVIDIA() const { return true; }
bool IsIntel() const { return false; }
bool IsARM() const { return false; }
bool IsQualcomm() const { return false; }
// Platform specific extensions
std::string AMDBoardName() const { return ""; }
std::string NVIDIAComputeCapability() const { return Capabilities(); }
// Returns if the Nvidia chip is a Volta or later archicture (major version 7 or higher)
bool IsPostNVIDIAVolta() const {
return GetInfo(CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR) >= 7;
}
// Retrieves the above extra information
std::string GetExtraInfo() const { return NVIDIAComputeCapability(); }
// Accessor to the private data-member
const RawDeviceID& operator()() const { return device_; }
private:
CUdevice device_;
// Private helper function
size_t GetInfo(const CUdevice_attribute info) const {
auto result = 0;
CheckError(cuDeviceGetAttribute(&result, info, device_));
return static_cast<size_t>(result);
}
};
// =================================================================================================
// Raw context type
using RawContext = CUcontext;
// C++11 version of 'CUcontext'
class Context {
public:
// Constructor based on the regular CUDA data-type: memory management is handled elsewhere
explicit Context(const CUcontext context):
context_(new CUcontext) {
*context_ = context;
}
// Regular constructor with memory management
explicit Context(const Device &device):
context_(new CUcontext, [](CUcontext* c) {
if (*c) { CheckErrorDtor(cuCtxDestroy(*c)); }
delete c;
}) {
CheckError(cuCtxCreate(context_.get(), 0, device()));
}
// Accessor to the private data-member
const RawContext& operator()() const { return *context_; }
RawContext* pointer() const { return &(*context_); }
private:
std::shared_ptr<CUcontext> context_;
};
// Pointer to a raw CUDA context
using ContextPointer = CUcontext*;
// =================================================================================================
// C++11 version of 'nvrtcProgram'. Additionally holds the program's source code.
class Program {
public:
Program() = default;
// Note that there is no constructor based on the regular CUDA data-type because of extra state
// Source-based constructor with memory management
explicit Program(const Context &, std::string source):
program_(new nvrtcProgram, [](nvrtcProgram* p) {
if (*p) { CheckErrorDtorNVRTC(nvrtcDestroyProgram(p)); }
delete p;
}),
source_(std::move(source)),
from_binary_(false) {
const auto source_ptr = &source_[0];
CheckErrorNVRTC(nvrtcCreateProgram(program_.get(), source_ptr, nullptr, 0, nullptr, nullptr));
}
// PTX-based constructor
explicit Program(const Device &device, const Context &context, const std::string &binary):
program_(nullptr), // not used
source_(binary),
from_binary_(true) {
}
// Compiles the device program and checks whether or not there are any warnings/errors
void Build(const Device &device, std::vector<std::string> &options) {
options.push_back("-arch=" + device.ComputeArch());
if (from_binary_) { return; }
auto raw_options = std::vector<const char*>();
for (const auto &option: options) {
raw_options.push_back(option.c_str());
}
auto status = nvrtcCompileProgram(*program_, raw_options.size(), raw_options.data());
CLCudaAPINVRTCError::Check(status, "nvrtcCompileProgram");
CheckError(cuModuleLoadDataEx(&module_, GetIR().data(), 0, nullptr, nullptr));
}
// Confirms whether a certain status code is an actual compilation error or warning
bool StatusIsCompilationWarningOrError(const nvrtcResult status) const {
return (status == NVRTC_ERROR_COMPILATION);
}
// Retrieves the warning/error message from the compiler (if any)
std::string GetBuildInfo(const Device &) const {
if (from_binary_) { return std::string{}; }
auto bytes = size_t{0};
CheckErrorNVRTC(nvrtcGetProgramLogSize(*program_, &bytes));
auto result = std::string{};
result.resize(bytes);
CheckErrorNVRTC(nvrtcGetProgramLog(*program_, &result[0]));
return result;
}
// Retrieves an intermediate representation of the compiled program (i.e. PTX)
std::string GetIR() const {
if (from_binary_) { return source_; } // holds the PTX
auto bytes = size_t{0};
CheckErrorNVRTC(nvrtcGetPTXSize(*program_, &bytes));
auto result = std::string{};
result.resize(bytes);
CheckErrorNVRTC(nvrtcGetPTX(*program_, &result[0]));
return result;
}
// Accessor to the private data-members
const CUmodule GetModule() const { return module_; }
const nvrtcProgram& operator()() const { return *program_; }
private:
std::shared_ptr<nvrtcProgram> program_;
CUmodule module_;
std::string source_;
bool from_binary_;
};
// =================================================================================================
// Raw command-queue type
using RawCommandQueue = CUstream;
// C++11 version of 'CUstream'
class Queue {
public:
// Note that there is no constructor based on the regular CUDA data-type because of extra state
// Regular constructor with memory management
explicit Queue(const Context &context, const Device &device):
queue_(new CUstream, [](CUstream* s) {
if (*s) { CheckErrorDtor(cuStreamDestroy(*s)); }
delete s;
}),
context_(context),
device_(device) {
CheckError(cuStreamCreate(queue_.get(), CU_STREAM_NON_BLOCKING));
}
// Synchronizes the queue and optionally also an event
void Finish(Event &event) const {
CheckError(cuEventSynchronize(event.end()));
Finish();
}
void Finish() const {
CheckError(cuStreamSynchronize(*queue_));
}
// Retrieves the corresponding context or device
Context GetContext() const { return context_; }
Device GetDevice() const { return device_; }
// Accessor to the private data-member
const RawCommandQueue& operator()() const { return *queue_; }
private:
std::shared_ptr<CUstream> queue_;
const Context context_;
const Device device_;
};
// =================================================================================================
// C++11 version of page-locked host memory
template <typename T>
class BufferHost {
public:
// Regular constructor with memory management
explicit BufferHost(const Context &, const size_t size):
buffer_(new void*, [](void** m) { CheckError(cuMemFreeHost(*m)); delete m; }),
size_(size) {
CheckError(cuMemAllocHost(buffer_.get(), size*sizeof(T)));
}
// Retrieves the actual allocated size in bytes
size_t GetSize() const {
return size_*sizeof(T);
}
// Compatibility with std::vector
size_t size() const { return size_; }
T* begin() { return &static_cast<T*>(*buffer_)[0]; }
T* end() { return &static_cast<T*>(*buffer_)[size_-1]; }
T& operator[](const size_t i) { return static_cast<T*>(*buffer_)[i]; }
T* data() { return static_cast<T*>(*buffer_); }
const T* data() const { return static_cast<T*>(*buffer_); }
private:
std::shared_ptr<void*> buffer_;
const size_t size_;
};
// =================================================================================================
// Enumeration of buffer access types
enum class BufferAccess { kReadOnly, kWriteOnly, kReadWrite, kNotOwned };
// C++11 version of 'CUdeviceptr'
template <typename T>
class Buffer {
public:
// Constructor based on the regular CUDA data-type: memory management is handled elsewhere
explicit Buffer(const CUdeviceptr buffer):
buffer_(new CUdeviceptr),
access_(BufferAccess::kNotOwned) {
*buffer_ = buffer;
}
// Regular constructor with memory management. If this class does not own the buffer object, then
// the memory will not be freed automatically afterwards.
explicit Buffer(const Context &, const BufferAccess access, const size_t size):
buffer_(new CUdeviceptr, [access, size](CUdeviceptr* m) {
if (access != BufferAccess::kNotOwned && size > 0) { CheckError(cuMemFree(*m)); }
delete m;
}),
access_(access) {
if (size > 0) { CheckError(cuMemAlloc(buffer_.get(), size*sizeof(T))); }
}
// As above, but now with read/write access as a default
explicit Buffer(const Context &context, const size_t size):
Buffer<T>(context, BufferAccess::kReadWrite, size) {
}
// Constructs a new buffer based on an existing host-container
template <typename Iterator>
explicit Buffer(const Context &context, const Queue &queue, Iterator start, Iterator end):
Buffer(context, BufferAccess::kReadWrite, static_cast<size_t>(end - start)) {
auto size = static_cast<size_t>(end - start);
auto pointer = &*start;
CheckError(cuMemcpyHtoDAsync(*buffer_, pointer, size*sizeof(T), queue()));
queue.Finish();
}
// Copies from device to host: reading the device buffer a-synchronously
void ReadAsync(const Queue &queue, const size_t size, T* host, const size_t offset = 0) const {
if (access_ == BufferAccess::kWriteOnly) {
throw LogicError("Buffer: reading from a write-only buffer");
}
CheckError(cuMemcpyDtoHAsync(host, *buffer_ + offset*sizeof(T), size*sizeof(T), queue()));
}
void ReadAsync(const Queue &queue, const size_t size, std::vector<T> &host,
const size_t offset = 0) const {
if (host.size() < size) {
throw LogicError("Buffer: target host buffer is too small");
}
ReadAsync(queue, size, host.data(), offset);
}
void ReadAsync(const Queue &queue, const size_t size, BufferHost<T> &host,
const size_t offset = 0) const {
if (host.size() < size) {
throw LogicError("Buffer: target host buffer is too small");
}
ReadAsync(queue, size, host.data(), offset);
}
// Copies from device to host: reading the device buffer
void Read(const Queue &queue, const size_t size, T* host, const size_t offset = 0) const {
ReadAsync(queue, size, host, offset);
queue.Finish();
}
void Read(const Queue &queue, const size_t size, std::vector<T> &host,
const size_t offset = 0) const {
Read(queue, size, host.data(), offset);
}
void Read(const Queue &queue, const size_t size, BufferHost<T> &host,
const size_t offset = 0) const {
Read(queue, size, host.data(), offset);
}
// Copies from host to device: writing the device buffer a-synchronously
void WriteAsync(const Queue &queue, const size_t size, const T* host, const size_t offset = 0) {
if (access_ == BufferAccess::kReadOnly) {
throw LogicError("Buffer: writing to a read-only buffer");
}
if (GetSize() < (offset+size)*sizeof(T)) {
throw LogicError("Buffer: target device buffer is too small");
}
CheckError(cuMemcpyHtoDAsync(*buffer_ + offset*sizeof(T), host, size*sizeof(T), queue()));
}
void WriteAsync(const Queue &queue, const size_t size, const std::vector<T> &host,
const size_t offset = 0) {
WriteAsync(queue, size, host.data(), offset);
}
void WriteAsync(const Queue &queue, const size_t size, const BufferHost<T> &host,
const size_t offset = 0) {
WriteAsync(queue, size, host.data(), offset);
}
// Copies from host to device: writing the device buffer
void Write(const Queue &queue, const size_t size, const T* host, const size_t offset = 0) {
WriteAsync(queue, size, host, offset);
queue.Finish();
}
void Write(const Queue &queue, const size_t size, const std::vector<T> &host,
const size_t offset = 0) {
Write(queue, size, host.data(), offset);
}
void Write(const Queue &queue, const size_t size, const BufferHost<T> &host,
const size_t offset = 0) {
Write(queue, size, host.data(), offset);
}
// Copies the contents of this buffer into another device buffer
void CopyToAsync(const Queue &queue, const size_t size, const Buffer<T> &destination,
EventPointer event = nullptr) const {
CheckError(cuMemcpyDtoDAsync(destination(), *buffer_, size*sizeof(T), queue()));
}
void CopyTo(const Queue &queue, const size_t size, const Buffer<T> &destination) const {
CopyToAsync(queue, size, destination);
queue.Finish();
}
// Retrieves the actual allocated size in bytes
size_t GetSize() const {
auto result = size_t{0};
CheckError(cuMemGetAddressRange(nullptr, &result, *buffer_));
return result;
}
// Accessors to the private data-members
CUdeviceptr operator()() const { return *buffer_; }
CUdeviceptr& operator()() { return *buffer_; }
private:
std::shared_ptr<CUdeviceptr> buffer_;
BufferAccess access_;
};
// =================================================================================================
// C++11 version of 'CUfunction'
class Kernel {
public:
// Constructor based on the regular CUDA data-type: memory management is handled elsewhere
explicit Kernel(const CUfunction kernel):
name_("unknown"),
kernel_(kernel) {
}
// Regular constructor with memory management
explicit Kernel(const std::shared_ptr<Program> program, const std::string &name): name_(name) {
CheckError(cuModuleGetFunction(&kernel_, program->GetModule(), name.c_str()));
}
// Sets a kernel argument at the indicated position. This stores both the value of the argument
// (as raw bytes) and the index indicating where this value can be found.
template <typename T>
void SetArgument(const size_t index, const T &value) {
if (index >= arguments_indices_.size()) { arguments_indices_.resize(index+1); }
arguments_indices_[index] = arguments_data_.size();
for (auto j=size_t(0); j<sizeof(T); ++j) {
arguments_data_.push_back(reinterpret_cast<const char*>(&value)[j]);
}
}
template <typename T>
void SetArgument(const size_t index, Buffer<T> &value) {
SetArgument(index, value());
}
// Sets all arguments in one go using parameter packs. Note that this resets all previously set
// arguments using 'SetArgument' or 'SetArguments'.
template <typename... Args>
void SetArguments(Args&... args) {
arguments_indices_.clear();
arguments_data_.clear();
SetArgumentsRecursive(0, args...);
}
// Retrieves the amount of local memory used per work-group for this kernel. Note that this the
// shared memory in CUDA terminology.
unsigned long LocalMemUsage(const Device &) const {
auto result = 0;
CheckError(cuFuncGetAttribute(&result, CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES, kernel_));
return static_cast<unsigned long>(result);
}
// Retrieves the name of the kernel
std::string GetFunctionName() const {
return name_;
}
// Launches a kernel onto the specified queue
void Launch(const Queue &queue, const std::vector<size_t> &global,
const std::vector<size_t> &local, EventPointer event) {
// TODO: Currently this CUDA launch is always synchronous due to a cuStreamSynchronize call
if (local.size() == 0) {
throw LogicError("Kernel: launching with a default workgroup size is not implemented for the CUDA back-end");
}
// Creates the grid (number of threadblocks) and sets the block sizes (threads per block)
auto grid = std::vector<size_t>{1, 1, 1};
auto block = std::vector<size_t>{1, 1, 1};
if (global.size() != local.size()) { throw LogicError("invalid thread/workgroup dimensions"); }
for (auto i=size_t{0}; i<local.size(); ++i) { grid[i] = global[i]/local[i]; }
for (auto i=size_t{0}; i<local.size(); ++i) { block[i] = local[i]; }
// Creates the array of pointers from the arrays of indices & data
std::vector<void*> pointers;
for (auto &index: arguments_indices_) {
pointers.push_back(&arguments_data_[index]);
}
// Launches the kernel, its execution time is recorded by events
if (event) { CheckError(cuEventRecord(event->start(), queue())); }
CheckError(cuLaunchKernel(kernel_, grid[0], grid[1], grid[2], block[0], block[1], block[2],
0, queue(), pointers.data(), nullptr));
cuStreamSynchronize(queue());
if (event) { CheckError(cuEventRecord(event->end(), queue())); }
}
// As above, but with an event waiting list
void Launch(const Queue &queue, const std::vector<size_t> &global,
const std::vector<size_t> &local, EventPointer event,
const std::vector<Event>& waitForEvents) {
for (auto &waitEvent : waitForEvents) {
waitEvent.WaitForCompletion(); // note: doesn't do anything, every kernel call is synchronous
}
return Launch(queue, global, local, event);
}
// Accessors to the private data-members
const CUfunction& operator()() const { return kernel_; }
CUfunction operator()() { return kernel_; }
private:
const std::string name_;
CUfunction kernel_;
std::vector<size_t> arguments_indices_; // Indices of the arguments
std::vector<char> arguments_data_; // The arguments data as raw bytes
// Internal implementation for the recursive SetArguments function.
template <typename T>
void SetArgumentsRecursive(const size_t index, T &first) {
SetArgument(index, first);
}
template <typename T, typename... Args>
void SetArgumentsRecursive(const size_t index, T &first, Args&... args) {
SetArgument(index, first);
SetArgumentsRecursive(index+1, args...);
}
};
// =================================================================================================
} // namespace clblast
// CLBLAST_CUPP11_H_
#endif
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