2 * This file is part of the GROMACS molecular simulation package.
4 * Copyright (c) 2012,2014,2015,2016,2017 by the GROMACS development team.
5 * Copyright (c) 2018,2019,2020, by the GROMACS development team, led by
6 * Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
7 * and including many others, as listed in the AUTHORS file in the
8 * top-level source directory and at http://www.gromacs.org.
10 * GROMACS is free software; you can redistribute it and/or
11 * modify it under the terms of the GNU Lesser General Public License
12 * as published by the Free Software Foundation; either version 2.1
13 * of the License, or (at your option) any later version.
15 * GROMACS is distributed in the hope that it will be useful,
16 * but WITHOUT ANY WARRANTY; without even the implied warranty of
17 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18 * Lesser General Public License for more details.
20 * You should have received a copy of the GNU Lesser General Public
21 * License along with GROMACS; if not, see
22 * http://www.gnu.org/licenses, or write to the Free Software Foundation,
23 * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
25 * If you want to redistribute modifications to GROMACS, please
26 * consider that scientific software is very special. Version
27 * control is crucial - bugs must be traceable. We will be happy to
28 * consider code for inclusion in the official distribution, but
29 * derived work must not be called official GROMACS. Details are found
30 * in the README & COPYING files - if they are missing, get the
31 * official version at http://www.gromacs.org.
33 * To help us fund GROMACS development, we humbly ask that you cite
34 * the research papers on the package. Check out http://www.gromacs.org.
36 #ifndef GMX_GPU_UTILS_CUDAUTILS_CUH
37 #define GMX_GPU_UTILS_CUDAUTILS_CUH
44 #include "gromacs/gpu_utils/gputraits.cuh"
45 #include "gromacs/math/vec.h"
46 #include "gromacs/math/vectypes.h"
47 #include "gromacs/utility/exceptions.h"
48 #include "gromacs/utility/fatalerror.h"
49 #include "gromacs/utility/gmxassert.h"
50 #include "gromacs/utility/stringutil.h"
57 /*! \brief Helper function to ensure no pending error silently
58 * disrupts error handling.
60 * Asserts in a debug build if an unhandled error is present. Issues a
61 * warning at run time otherwise.
63 * \todo This is similar to CU_CHECK_PREV_ERR, which should be
66 static inline void ensureNoPendingCudaError(const char* errorMessage)
68 // Ensure there is no pending error that would otherwise affect
69 // the behaviour of future error handling.
70 cudaError_t stat = cudaGetLastError();
71 if (stat == cudaSuccess)
76 // If we would find an error in a release build, we do not know
77 // what is appropriate to do about it, so assert only for debug
79 auto fullMessage = formatString(
80 "%s An unhandled error from a previous CUDA operation was detected. %s: %s",
81 errorMessage, cudaGetErrorName(stat), cudaGetErrorString(stat));
82 GMX_ASSERT(stat == cudaSuccess, fullMessage.c_str());
83 // TODO When we evolve a better logging framework, use that
84 // for release-build error reporting.
85 gmx_warning("%s", fullMessage.c_str());
91 enum class GpuApiCallBehavior;
93 /* TODO error checking needs to be rewritten. We have 2 types of error checks needed
94 based on where they occur in the code:
95 - non performance-critical: these errors are unsafe to be ignored and must be
96 _always_ checked for, e.g. initializations
97 - performance critical: handling errors might hurt performance so care need to be taken
98 when/if we should check for them at all, e.g. in cu_upload_X. However, we should be
99 able to turn the check for these errors on!
101 Probably we'll need two sets of the macros below...
104 #define CHECK_CUDA_ERRORS
106 #ifdef CHECK_CUDA_ERRORS
108 /*! Check for CUDA error on the return status of a CUDA RT API call. */
109 # define CU_RET_ERR(status, msg) \
112 if (status != cudaSuccess) \
114 gmx_fatal(FARGS, "%s: %s\n", msg, cudaGetErrorString(status)); \
118 /*! Check for any previously occurred uncaught CUDA error. */
119 # define CU_CHECK_PREV_ERR() \
122 cudaError_t _CU_CHECK_PREV_ERR_status = cudaGetLastError(); \
123 if (_CU_CHECK_PREV_ERR_status != cudaSuccess) \
126 "Just caught a previously occurred CUDA error (%s), will try to " \
128 cudaGetErrorString(_CU_CHECK_PREV_ERR_status)); \
132 #else /* CHECK_CUDA_ERRORS */
134 # define CU_RET_ERR(status, msg) \
138 # define CU_CHECK_PREV_ERR() \
143 #endif /* CHECK_CUDA_ERRORS */
145 /*! Launches synchronous or asynchronous device to host memory copy.
147 * The copy is launched in stream s or if not specified, in stream 0.
149 int cu_copy_D2H(void* h_dest, void* d_src, size_t bytes, GpuApiCallBehavior transferKind, cudaStream_t /*s = nullptr*/);
151 /*! Launches synchronous host to device memory copy in stream 0. */
152 int cu_copy_D2H_sync(void* /*h_dest*/, void* /*d_src*/, size_t /*bytes*/);
154 /*! Launches asynchronous host to device memory copy in stream s. */
155 int cu_copy_D2H_async(void* /*h_dest*/, void* /*d_src*/, size_t /*bytes*/, cudaStream_t /*s = nullptr*/);
157 /*! Launches synchronous or asynchronous host to device memory copy.
159 * The copy is launched in stream s or if not specified, in stream 0.
161 int cu_copy_H2D(void* d_dest, const void* h_src, size_t bytes, GpuApiCallBehavior transferKind, cudaStream_t /*s = nullptr*/);
163 /*! Launches synchronous host to device memory copy. */
164 int cu_copy_H2D_sync(void* /*d_dest*/, const void* /*h_src*/, size_t /*bytes*/);
166 /*! Launches asynchronous host to device memory copy in stream s. */
167 int cu_copy_H2D_async(void* /*d_dest*/, const void* /*h_src*/, size_t /*bytes*/, cudaStream_t /*s = nullptr*/);
169 // TODO: the 2 functions below are pretty much a constructor/destructor of a simple
170 // GPU table object. There is also almost self-contained fetchFromParamLookupTable()
171 // in cuda_kernel_utils.cuh. They could all live in a separate class/struct file.
173 /*! \brief Add a triplets stored in a float3 to an rvec variable.
175 * \param[out] a Rvec to increment
176 * \param[in] b Float triplet to increment with.
178 static inline void rvec_inc(rvec a, const float3 b)
180 rvec tmp = { b.x, b.y, b.z };
184 /*! \brief Returns true if all tasks in \p s have completed.
186 * \param[in] deviceStream CUDA stream to check.
188 * \returns True if all tasks enqueued in the stream \p deviceStream (at the time of this call) have completed.
190 static inline bool haveStreamTasksCompleted(const DeviceStream& deviceStream)
192 cudaError_t stat = cudaStreamQuery(deviceStream.stream());
194 if (stat == cudaErrorNotReady)
196 // work is still in progress in the stream
200 GMX_ASSERT(stat != cudaErrorInvalidResourceHandle, "Stream idnetifier not valid");
202 // cudaSuccess and cudaErrorNotReady are the expected return values
203 CU_RET_ERR(stat, "Unexpected cudaStreamQuery failure");
205 GMX_ASSERT(stat == cudaSuccess,
206 "Values other than cudaSuccess should have been explicitly handled");
211 /* Kernel launch helpers */
214 * A function for setting up a single CUDA kernel argument.
215 * This is the tail of the compile-time recursive function below.
216 * It has to be seen by the compiler first.
218 * \tparam totalArgsCount Number of the kernel arguments
219 * \tparam KernelPtr Kernel function handle type
220 * \param[in] argIndex Index of the current argument
222 template<size_t totalArgsCount, typename KernelPtr>
223 void prepareGpuKernelArgument(KernelPtr /*kernel*/,
224 std::array<void*, totalArgsCount>* /* kernelArgsPtr */,
225 size_t gmx_used_in_debug argIndex)
227 GMX_ASSERT(argIndex == totalArgsCount, "Tail expansion");
231 * Compile-time recursive function for setting up a single CUDA kernel argument.
232 * This function copies a kernel argument pointer \p argPtr into \p kernelArgsPtr,
233 * and calls itself on the next argument, eventually calling the tail function above.
235 * \tparam CurrentArg Type of the current argument
236 * \tparam RemainingArgs Types of remaining arguments after the current one
237 * \tparam totalArgsCount Number of the kernel arguments
238 * \tparam KernelPtr Kernel function handle type
239 * \param[in] kernel Kernel function handle
240 * \param[in,out] kernelArgsPtr Pointer to the argument array to be filled in
241 * \param[in] argIndex Index of the current argument
242 * \param[in] argPtr Pointer to the current argument
243 * \param[in] otherArgsPtrs Pack of pointers to arguments remaining to process after the current one
245 template<typename CurrentArg, typename... RemainingArgs, size_t totalArgsCount, typename KernelPtr>
246 void prepareGpuKernelArgument(KernelPtr kernel,
247 std::array<void*, totalArgsCount>* kernelArgsPtr,
249 const CurrentArg* argPtr,
250 const RemainingArgs*... otherArgsPtrs)
252 (*kernelArgsPtr)[argIndex] = (void*)argPtr;
253 prepareGpuKernelArgument(kernel, kernelArgsPtr, argIndex + 1, otherArgsPtrs...);
257 * A wrapper function for setting up all the CUDA kernel arguments.
258 * Calls the recursive functions above.
260 * \tparam KernelPtr Kernel function handle type
261 * \tparam Args Types of all the kernel arguments
262 * \param[in] kernel Kernel function handle
263 * \param[in] argsPtrs Pointers to all the kernel arguments
264 * \returns A prepared parameter pack to be used with launchGpuKernel() as the last argument.
266 template<typename KernelPtr, typename... Args>
267 std::array<void*, sizeof...(Args)> prepareGpuKernelArguments(KernelPtr kernel,
268 const KernelLaunchConfig& /*config */,
269 const Args*... argsPtrs)
271 std::array<void*, sizeof...(Args)> kernelArgs;
272 prepareGpuKernelArgument(kernel, &kernelArgs, 0, argsPtrs...);
276 /*! \brief Launches the CUDA kernel and handles the errors.
278 * \tparam Args Types of all the kernel arguments
279 * \param[in] kernel Kernel function handle
280 * \param[in] config Kernel configuration for launching
281 * \param[in] deviceStream GPU stream to launch kernel in
282 * \param[in] kernelName Human readable kernel description, for error handling only
283 * \param[in] kernelArgs Array of the pointers to the kernel arguments, prepared by
284 * prepareGpuKernelArguments() \throws gmx::InternalError on kernel launch failure
286 template<typename... Args>
287 void launchGpuKernel(void (*kernel)(Args...),
288 const KernelLaunchConfig& config,
289 const DeviceStream& deviceStream,
290 CommandEvent* /*timingEvent */,
291 const char* kernelName,
292 const std::array<void*, sizeof...(Args)>& kernelArgs)
294 dim3 blockSize(config.blockSize[0], config.blockSize[1], config.blockSize[2]);
295 dim3 gridSize(config.gridSize[0], config.gridSize[1], config.gridSize[2]);
296 cudaLaunchKernel((void*)kernel, gridSize, blockSize, const_cast<void**>(kernelArgs.data()),
297 config.sharedMemorySize, deviceStream.stream());
299 cudaError_t status = cudaGetLastError();
300 if (cudaSuccess != status)
302 const std::string errorMessage =
303 "GPU kernel (" + std::string(kernelName)
304 + ") failed to launch: " + std::string(cudaGetErrorString(status));
305 GMX_THROW(gmx::InternalError(errorMessage));