* This file is part of the GROMACS molecular simulation package.
*
* Copyright (c) 2012,2014,2015,2016,2017 by the GROMACS development team.
- * Copyright (c) 2018,2019,2020, by the GROMACS development team, led by
+ * Copyright (c) 2018,2019,2020,2021, by the GROMACS development team, led by
* Mark Abraham, David van der Spoel, Berk Hess, and Erik Lindahl,
* and including many others, as listed in the AUTHORS file in the
* top-level source directory and at http://www.gromacs.org.
#include <array>
#include <string>
+#include <type_traits>
+#include "gromacs/gpu_utils/device_stream.h"
#include "gromacs/gpu_utils/gputraits.cuh"
#include "gromacs/math/vec.h"
#include "gromacs/math/vectypes.h"
namespace
{
+/*! \brief Add the API information on the specific error to the error message.
+ *
+ * \param[in] deviceError The error to assert cudaSuccess on.
+ *
+ * \returns A description of the API error. Returns '(CUDA error #0 (cudaSuccess): no error)' in case deviceError is cudaSuccess.
+ */
+inline std::string getDeviceErrorString(const cudaError_t deviceError)
+{
+ return formatString("CUDA error #%d (%s): %s.",
+ deviceError,
+ cudaGetErrorName(deviceError),
+ cudaGetErrorString(deviceError));
+}
+
+/*! \brief Check if API returned an error and throw an exception with information on it.
+ *
+ * \param[in] deviceError The error to assert cudaSuccess on.
+ * \param[in] errorMessage Undecorated error message.
+ *
+ * \throws InternalError if deviceError is not a success.
+ */
+inline void checkDeviceError(const cudaError_t deviceError, const std::string& errorMessage)
+{
+ if (deviceError != cudaSuccess)
+ {
+ GMX_THROW(gmx::InternalError(errorMessage + " " + getDeviceErrorString(deviceError)));
+ }
+}
+
/*! \brief Helper function to ensure no pending error silently
* disrupts error handling.
*
* Asserts in a debug build if an unhandled error is present. Issues a
* warning at run time otherwise.
*
- * \todo This is similar to CU_CHECK_PREV_ERR, which should be
- * consolidated.
+ * \param[in] errorMessage Undecorated error message.
*/
-static inline void ensureNoPendingCudaError(const char* errorMessage)
+inline void ensureNoPendingDeviceError(const std::string& errorMessage)
{
// Ensure there is no pending error that would otherwise affect
// the behaviour of future error handling.
- cudaError_t stat = cudaGetLastError();
- if (stat == cudaSuccess)
+ cudaError_t deviceError = cudaGetLastError();
+ if (deviceError == cudaSuccess)
{
return;
}
// If we would find an error in a release build, we do not know
// what is appropriate to do about it, so assert only for debug
// builds.
- auto fullMessage = formatString(
- "%s An unhandled error from a previous CUDA operation was detected. %s: %s",
- errorMessage, cudaGetErrorName(stat), cudaGetErrorString(stat));
- GMX_ASSERT(stat == cudaSuccess, fullMessage.c_str());
+ const std::string fullErrorMessage =
+ errorMessage + " An unhandled error from a previous CUDA operation was detected. "
+ + gmx::getDeviceErrorString(deviceError);
+ GMX_ASSERT(deviceError == cudaSuccess, fullErrorMessage.c_str());
// TODO When we evolve a better logging framework, use that
// for release-build error reporting.
- gmx_warning("%s", fullMessage.c_str());
+ gmx_warning("%s", fullErrorMessage.c_str());
}
} // namespace
#ifdef CHECK_CUDA_ERRORS
/*! Check for CUDA error on the return status of a CUDA RT API call. */
-# define CU_RET_ERR(status, msg) \
- do \
- { \
- if (status != cudaSuccess) \
- { \
- gmx_fatal(FARGS, "%s: %s\n", msg, cudaGetErrorString(status)); \
- } \
- } while (0)
-
-/*! Check for any previously occurred uncaught CUDA error. */
-# define CU_CHECK_PREV_ERR() \
- do \
- { \
- cudaError_t _CU_CHECK_PREV_ERR_status = cudaGetLastError(); \
- if (_CU_CHECK_PREV_ERR_status != cudaSuccess) \
- { \
- gmx_warning( \
- "Just caught a previously occurred CUDA error (%s), will try to " \
- "continue.", \
- cudaGetErrorString(_CU_CHECK_PREV_ERR_status)); \
- } \
+# define CU_RET_ERR(deviceError, msg) \
+ do \
+ { \
+ if ((deviceError) != cudaSuccess) \
+ { \
+ gmx_fatal(FARGS, "%s\n", ((msg) + gmx::getDeviceErrorString(deviceError)).c_str()); \
+ } \
} while (0)
#else /* CHECK_CUDA_ERRORS */
do \
{ \
} while (0)
-# define CU_CHECK_PREV_ERR() \
- do \
- { \
- } while (0)
#endif /* CHECK_CUDA_ERRORS */
-/*! Launches synchronous or asynchronous device to host memory copy.
- *
- * The copy is launched in stream s or if not specified, in stream 0.
- */
-int cu_copy_D2H(void* h_dest, void* d_src, size_t bytes, GpuApiCallBehavior transferKind, cudaStream_t /*s = nullptr*/);
-
-/*! Launches synchronous host to device memory copy in stream 0. */
-int cu_copy_D2H_sync(void* /*h_dest*/, void* /*d_src*/, size_t /*bytes*/);
-
-/*! Launches asynchronous host to device memory copy in stream s. */
-int cu_copy_D2H_async(void* /*h_dest*/, void* /*d_src*/, size_t /*bytes*/, cudaStream_t /*s = nullptr*/);
-
-/*! Launches synchronous or asynchronous host to device memory copy.
- *
- * The copy is launched in stream s or if not specified, in stream 0.
- */
-int cu_copy_H2D(void* d_dest, const void* h_src, size_t bytes, GpuApiCallBehavior transferKind, cudaStream_t /*s = nullptr*/);
-
-/*! Launches synchronous host to device memory copy. */
-int cu_copy_H2D_sync(void* /*d_dest*/, const void* /*h_src*/, size_t /*bytes*/);
-
-/*! Launches asynchronous host to device memory copy in stream s. */
-int cu_copy_H2D_async(void* /*d_dest*/, const void* /*h_src*/, size_t /*bytes*/, cudaStream_t /*s = nullptr*/);
-
// TODO: the 2 functions below are pretty much a constructor/destructor of a simple
// GPU table object. There is also almost self-contained fetchFromParamLookupTable()
// in cuda_kernel_utils.cuh. They could all live in a separate class/struct file.
-/*! \brief Initialize parameter lookup table.
- *
- * Initializes device memory, copies data from host and binds
- * a texture to allocated device memory to be used for parameter lookup.
- *
- * \tparam[in] T Raw data type
- * \param[out] d_ptr device pointer to the memory to be allocated
- * \param[out] texObj texture object to be initialized
- * \param[in] h_ptr pointer to the host memory to be uploaded to the device
- * \param[in] numElem number of elements in the h_ptr
- */
-template<typename T>
-void initParamLookupTable(T*& d_ptr, cudaTextureObject_t& texObj, const T* h_ptr, int numElem);
-
-// Add extern declarations so each translation unit understands that
-// there will be a definition provided.
-extern template void initParamLookupTable<int>(int*&, cudaTextureObject_t&, const int*, int);
-extern template void initParamLookupTable<float>(float*&, cudaTextureObject_t&, const float*, int);
-
-/*! \brief Destroy parameter lookup table.
- *
- * Unbinds texture object, deallocates device memory.
- *
- * \tparam[in] T Raw data type
- * \param[in] d_ptr Device pointer to the memory to be deallocated
- * \param[in] texObj Texture object to be deinitialized
- */
-template<typename T>
-void destroyParamLookupTable(T* d_ptr, cudaTextureObject_t texObj);
-
-// Add extern declarations so each translation unit understands that
-// there will be a definition provided.
-extern template void destroyParamLookupTable<int>(int*, cudaTextureObject_t);
-extern template void destroyParamLookupTable<float>(float*, cudaTextureObject_t);
-
/*! \brief Add a triplets stored in a float3 to an rvec variable.
*
* \param[out] a Rvec to increment
rvec_inc(a, tmp);
}
-/*! \brief Wait for all taks in stream \p s to complete.
- *
- * \param[in] s stream to synchronize with
- */
-static inline void gpuStreamSynchronize(cudaStream_t s)
-{
- cudaError_t stat = cudaStreamSynchronize(s);
- CU_RET_ERR(stat, "cudaStreamSynchronize failed");
-}
-
/*! \brief Returns true if all tasks in \p s have completed.
*
- * \param[in] s stream to check
+ * \param[in] deviceStream CUDA stream to check.
*
- * \returns True if all tasks enqueued in the stream \p s (at the time of this call) have completed.
+ * \returns True if all tasks enqueued in the stream \p deviceStream (at the time of this call) have completed.
*/
-static inline bool haveStreamTasksCompleted(cudaStream_t s)
+static inline bool haveStreamTasksCompleted(const DeviceStream& deviceStream)
{
- cudaError_t stat = cudaStreamQuery(s);
+ cudaError_t stat = cudaStreamQuery(deviceStream.stream());
if (stat == cudaErrorNotReady)
{
return false;
}
- GMX_ASSERT(stat != cudaErrorInvalidResourceHandle, "Stream idnetifier not valid");
+ GMX_ASSERT(stat != cudaErrorInvalidResourceHandle,
+ ("Stream identifier not valid. " + gmx::getDeviceErrorString(stat)).c_str());
// cudaSuccess and cudaErrorNotReady are the expected return values
- CU_RET_ERR(stat, "Unexpected cudaStreamQuery failure");
+ CU_RET_ERR(stat, "Unexpected cudaStreamQuery failure. ");
GMX_ASSERT(stat == cudaSuccess,
- "Values other than cudaSuccess should have been explicitly handled");
+ ("Values other than cudaSuccess should have been explicitly handled. "
+ + gmx::getDeviceErrorString(stat))
+ .c_str());
return true;
}
const CurrentArg* argPtr,
const RemainingArgs*... otherArgsPtrs)
{
- (*kernelArgsPtr)[argIndex] = (void*)argPtr;
+ (*kernelArgsPtr)[argIndex] = const_cast<void*>(static_cast<const void*>(argPtr));
prepareGpuKernelArgument(kernel, kernelArgsPtr, argIndex + 1, otherArgsPtrs...);
}
* \tparam Args Types of all the kernel arguments
* \param[in] kernel Kernel function handle
* \param[in] config Kernel configuration for launching
+ * \param[in] deviceStream GPU stream to launch kernel in
* \param[in] kernelName Human readable kernel description, for error handling only
* \param[in] kernelArgs Array of the pointers to the kernel arguments, prepared by
- * prepareGpuKernelArguments() \throws gmx::InternalError on kernel launch failure
+ * prepareGpuKernelArguments()
+ * \throws gmx::InternalError on kernel launch failure
*/
template<typename... Args>
void launchGpuKernel(void (*kernel)(Args...),
const KernelLaunchConfig& config,
+ const DeviceStream& deviceStream,
CommandEvent* /*timingEvent */,
const char* kernelName,
const std::array<void*, sizeof...(Args)>& kernelArgs)
{
dim3 blockSize(config.blockSize[0], config.blockSize[1], config.blockSize[2]);
dim3 gridSize(config.gridSize[0], config.gridSize[1], config.gridSize[2]);
- cudaLaunchKernel((void*)kernel, gridSize, blockSize, const_cast<void**>(kernelArgs.data()),
- config.sharedMemorySize, config.stream);
-
- cudaError_t status = cudaGetLastError();
- if (cudaSuccess != status)
- {
- const std::string errorMessage =
- "GPU kernel (" + std::string(kernelName)
- + ") failed to launch: " + std::string(cudaGetErrorString(status));
- GMX_THROW(gmx::InternalError(errorMessage));
- }
+ cudaLaunchKernel(reinterpret_cast<void*>(kernel),
+ gridSize,
+ blockSize,
+ const_cast<void**>(kernelArgs.data()),
+ config.sharedMemorySize,
+ deviceStream.stream());
+
+ gmx::ensureNoPendingDeviceError("GPU kernel (" + std::string(kernelName)
+ + ") failed to launch.");
}
#endif
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