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vdr-plugin-softhdcuvid/drvapi_error_string.h
Dirk Nehring 177e44de98 - switch from indent to clang-format, which is supported by Visual Studio Code 
- reindent all sources to common code style
- fix include sort errors from clang-format
- update AGPL 3.0 license file
2021-12-27 20:02:45 +01:00

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/*
* Copyright 1993-2013 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/
#ifndef _DRVAPI_ERROR_STRING_H_
#define _DRVAPI_ERROR_STRING_H_
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// Error Code string definitions here
typedef struct {
char const *error_string;
unsigned int error_id;
} s_CudaErrorStr;
/**
* Error codes
*/
s_CudaErrorStr sCudaDrvErrorString[] = {
/**
* The API call returned with no errors. In the case of query calls, this
* can also mean that the operation being queried is complete (see
* ::cuEventQuery() and ::cuStreamQuery()).
*/
{"CUDA_SUCCESS", 0},
/**
* This indicates that one or more of the parameters passed to the API call
* is not within an acceptable range of values.
*/
{"CUDA_ERROR_INVALID_VALUE", 1},
/**
* The API call failed because it was unable to allocate enough memory to
* perform the requested operation.
*/
{"CUDA_ERROR_OUT_OF_MEMORY", 2},
/**
* This indicates that the CUDA driver has not been initialized with
* ::cuInit() or that initialization has failed.
*/
{"CUDA_ERROR_NOT_INITIALIZED", 3},
/**
* This indicates that the CUDA driver is in the process of shutting down.
*/
{"CUDA_ERROR_DEINITIALIZED", 4},
/**
* This indicates profiling APIs are called while application is running
* in visual profiler mode.
*/
{"CUDA_ERROR_PROFILER_DISABLED", 5},
/**
* This indicates profiling has not been initialized for this context.
* Call cuProfilerInitialize() to resolve this.
*/
{"CUDA_ERROR_PROFILER_NOT_INITIALIZED", 6},
/**
* This indicates profiler has already been started and probably
* cuProfilerStart() is incorrectly called.
*/
{"CUDA_ERROR_PROFILER_ALREADY_STARTED", 7},
/**
* This indicates profiler has already been stopped and probably
* cuProfilerStop() is incorrectly called.
*/
{"CUDA_ERROR_PROFILER_ALREADY_STOPPED", 8},
/**
* This indicates that no CUDA-capable devices were detected by the
* installed CUDA driver.
*/
{"CUDA_ERROR_NO_DEVICE (no CUDA-capable devices were detected)", 100},
/**
* This indicates that the device ordinal supplied by the user does not
* correspond to a valid CUDA device.
*/
{"CUDA_ERROR_INVALID_DEVICE (device specified is not a valid CUDA device)", 101},
/**
* This indicates that the device kernel image is invalid. This can also
* indicate an invalid CUDA module.
*/
{"CUDA_ERROR_INVALID_IMAGE", 200},
/**
* This most frequently indicates that there is no context bound to the
* current thread. This can also be returned if the context passed to an
* API call is not a valid handle (such as a context that has had
* ::cuCtxDestroy() invoked on it). This can also be returned if a user
* mixes different API versions (i.e. 3010 context with 3020 API calls).
* See ::cuCtxGetApiVersion() for more details.
*/
{"CUDA_ERROR_INVALID_CONTEXT", 201},
/**
* This indicated that the context being supplied as a parameter to the
* API call was already the active context.
* \deprecated
* This error return is deprecated as of CUDA 3.2. It is no longer an
* error to attempt to push the active context via ::cuCtxPushCurrent().
*/
{"CUDA_ERROR_CONTEXT_ALREADY_CURRENT", 202},
/**
* This indicates that a map or register operation has failed.
*/
{"CUDA_ERROR_MAP_FAILED", 205},
/**
* This indicates that an unmap or unregister operation has failed.
*/
{"CUDA_ERROR_UNMAP_FAILED", 206},
/**
* This indicates that the specified array is currently mapped and thus
* cannot be destroyed.
*/
{"CUDA_ERROR_ARRAY_IS_MAPPED", 207},
/**
* This indicates that the resource is already mapped.
*/
{"CUDA_ERROR_ALREADY_MAPPED", 208},
/**
* This indicates that there is no kernel image available that is suitable
* for the device. This can occur when a user specifies code generation
* options for a particular CUDA source file that do not include the
* corresponding device configuration.
*/
{"CUDA_ERROR_NO_BINARY_FOR_GPU", 209},
/**
* This indicates that a resource has already been acquired.
*/
{"CUDA_ERROR_ALREADY_ACQUIRED", 210},
/**
* This indicates that a resource is not mapped.
*/
{"CUDA_ERROR_NOT_MAPPED", 211},
/**
* This indicates that a mapped resource is not available for access as an
* array.
*/
{"CUDA_ERROR_NOT_MAPPED_AS_ARRAY", 212},
/**
* This indicates that a mapped resource is not available for access as a
* pointer.
*/
{"CUDA_ERROR_NOT_MAPPED_AS_POINTER", 213},
/**
* This indicates that an uncorrectable ECC error was detected during
* execution.
*/
{"CUDA_ERROR_ECC_UNCORRECTABLE", 214},
/**
* This indicates that the ::CUlimit passed to the API call is not
* supported by the active device.
*/
{"CUDA_ERROR_UNSUPPORTED_LIMIT", 215},
/**
* This indicates that the ::CUcontext passed to the API call can
* only be bound to a single CPU thread at a time but is already
* bound to a CPU thread.
*/
{"CUDA_ERROR_CONTEXT_ALREADY_IN_USE", 216},
/**
* This indicates that peer access is not supported across the given
* devices.
*/
{"CUDA_ERROR_PEER_ACCESS_UNSUPPORTED", 217},
/**
* This indicates that a PTX JIT compilation failed.
*/
{"CUDA_ERROR_INVALID_PTX", 218},
/**
* This indicates an error with OpenGL or DirectX context.
*/
{"CUDA_ERROR_INVALID_GRAPHICS_CONTEXT", 219},
/**
* This indicates that an uncorrectable NVLink error was detected during the
* execution.
*/
{"CUDA_ERROR_NVLINK_UNCORRECTABLE", 220},
/**
* This indicates that the PTX JIT compiler library was not found.
*/
{"CUDA_ERROR_JIT_COMPILER_NOT_FOUND", 221},
/**
* This indicates that the device kernel source is invalid.
*/
{"CUDA_ERROR_INVALID_SOURCE", 300},
/**
* This indicates that the file specified was not found.
*/
{"CUDA_ERROR_FILE_NOT_FOUND", 301},
/**
* This indicates that a link to a shared object failed to resolve.
*/
{"CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND", 302},
/**
* This indicates that initialization of a shared object failed.
*/
{"CUDA_ERROR_SHARED_OBJECT_INIT_FAILED", 303},
/**
* This indicates that an OS call failed.
*/
{"CUDA_ERROR_OPERATING_SYSTEM", 304},
/**
* This indicates that a resource handle passed to the API call was not
* valid. Resource handles are opaque types like ::CUstream and ::CUevent.
*/
{"CUDA_ERROR_INVALID_HANDLE", 400},
/**
* This indicates that a named symbol was not found. Examples of symbols
* are global/constant variable names, texture names }, and surface names.
*/
{"CUDA_ERROR_NOT_FOUND", 500},
/**
* This indicates that asynchronous operations issued previously have not
* completed yet. This result is not actually an error, but must be
* indicated differently than ::CUDA_SUCCESS (which indicates completion).
* Calls that may return this value include ::cuEventQuery() and
* ::cuStreamQuery().
*/
{"CUDA_ERROR_NOT_READY", 600},
/**
* While executing a kernel, the device encountered a
* load or store instruction on an invalid memory address.
* This leaves the process in an inconsistent state and any further CUDA
* work will return the same error. To continue using CUDA, the process must
* be terminated and relaunched.
*/
{"CUDA_ERROR_ILLEGAL_ADDRESS", 700},
/**
* This indicates that a launch did not occur because it did not have
* appropriate resources. This error usually indicates that the user has
* attempted to pass too many arguments to the device kernel, or the
* kernel launch specifies too many threads for the kernel's register
* count. Passing arguments of the wrong size (i.e. a 64-bit pointer
* when a 32-bit int is expected) is equivalent to passing too many
* arguments and can also result in this error.
*/
{"CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES", 701},
/**
* This indicates that the device kernel took too long to execute. This can
* only occur if timeouts are enabled - see the device attribute
* ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. The
* context cannot be used (and must be destroyed similar to
* ::CUDA_ERROR_LAUNCH_FAILED). All existing device memory allocations from
* this context are invalid and must be reconstructed if the program is to
* continue using CUDA.
*/
{"CUDA_ERROR_LAUNCH_TIMEOUT", 702},
/**
* This error indicates a kernel launch that uses an incompatible texturing
* mode.
*/
{"CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING", 703},
/**
* This error indicates that a call to ::cuCtxEnablePeerAccess() is
* trying to re-enable peer access to a context which has already
* had peer access to it enabled.
*/
{"CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED", 704},
/**
* This error indicates that ::cuCtxDisablePeerAccess() is
* trying to disable peer access which has not been enabled yet
* via ::cuCtxEnablePeerAccess().
*/
{"CUDA_ERROR_PEER_ACCESS_NOT_ENABLED", 705},
/**
* This error indicates that the primary context for the specified device
* has already been initialized.
*/
{"CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE", 708},
/**
* This error indicates that the context current to the calling thread
* has been destroyed using ::cuCtxDestroy }, or is a primary context which
* has not yet been initialized.
*/
{"CUDA_ERROR_CONTEXT_IS_DESTROYED", 709},
/**
* A device-side assert triggered during kernel execution. The context
* cannot be used anymore, and must be destroyed. All existing device
* memory allocations from this context are invalid and must be
* reconstructed if the program is to continue using CUDA.
*/
{"CUDA_ERROR_ASSERT", 710},
/**
* This error indicates that the hardware resources required to enable
* peer access have been exhausted for one or more of the devices
* passed to ::cuCtxEnablePeerAccess().
*/
{"CUDA_ERROR_TOO_MANY_PEERS", 711},
/**
* This error indicates that the memory range passed to
* ::cuMemHostRegister() has already been registered.
*/
{"CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED", 712},
/**
* This error indicates that the pointer passed to ::cuMemHostUnregister()
* does not correspond to any currently registered memory region.
*/
{"CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED", 713},
/**
* While executing a kernel, the device encountered a stack error.
* This can be due to stack corruption or exceeding the stack size limit.
* This leaves the process in an inconsistent state and any further CUDA
* work will return the same error. To continue using CUDA, the process must
* be terminated and relaunched.
*/
{"CUDA_ERROR_HARDWARE_STACK_ERROR", 714},
/**
* While executing a kernel, the device encountered an illegal instruction.
* This leaves the process in an inconsistent state and any further CUDA
* work will return the same error. To continue using CUDA, the process must
* be terminated and relaunched.
*/
{"CUDA_ERROR_ILLEGAL_INSTRUCTION", 715},
/**
* While executing a kernel, the device encountered a load or store
* instruction on a memory address which is not aligned. This leaves the
* process in an inconsistent state and any further CUDA work will return
* the same error. To continue using CUDA, the process must be terminated
* and relaunched.
*/
{"CUDA_ERROR_MISALIGNED_ADDRESS", 716},
/**
* While executing a kernel, the device encountered an instruction
* which can only operate on memory locations in certain address spaces
* (global, shared, or local), but was supplied a memory address not
* belonging to an allowed address space.
* This leaves the process in an inconsistent state and any further CUDA
* work will return the same error. To continue using CUDA, the process must
* be terminated and relaunched.
*/
{"CUDA_ERROR_INVALID_ADDRESS_SPACE", 717},
/**
* While executing a kernel, the device program counter wrapped its address
* space. This leaves the process in an inconsistent state and any further
* CUDA work will return the same error. To continue using CUDA, the process
* must be terminated and relaunched.
*/
{"CUDA_ERROR_INVALID_PC", 718},
/**
* An exception occurred on the device while executing a kernel. Common
* causes include dereferencing an invalid device pointer and accessing
* out of bounds shared memory. The context cannot be used }, so it must
* be destroyed (and a new one should be created). All existing device
* memory allocations from this context are invalid and must be
* reconstructed if the program is to continue using CUDA.
*/
{"CUDA_ERROR_LAUNCH_FAILED", 719},
/**
* This error indicates that the number of blocks launched per grid for a
* kernel that was launched via either ::cuLaunchCooperativeKernel or
* ::cuLaunchCooperativeKernelMultiDevice exceeds the maximum number of
* blocks as allowed by ::cuOccupancyMaxActiveBlocksPerMultiprocessor or
* ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number
* of multiprocessors as specified by the device attribute
* ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
*/
{"CUDA_ERROR_COOPERATIVE_LAUNCH_TOO_LARGE", 720},
/**
* This error indicates that the attempted operation is not permitted.
*/
{"CUDA_ERROR_NOT_PERMITTED", 800},
/**
* This error indicates that the attempted operation is not supported
* on the current system or device.
*/
{"CUDA_ERROR_NOT_SUPPORTED", 801},
/**
* This indicates that an unknown internal error has occurred.
*/
{"CUDA_ERROR_UNKNOWN", 999},
{NULL, -1}};
// This is just a linear search through the array, since the error_id's are not
// always ocurring consecutively
static inline const char *getCudaDrvErrorString(CUresult error_id) {
int index = 0;
while (sCudaDrvErrorString[index].error_id != error_id && (int)sCudaDrvErrorString[index].error_id != -1) {
index++;
}
if (sCudaDrvErrorString[index].error_id == error_id)
return (const char *)sCudaDrvErrorString[index].error_string;
else
return (const char *)"CUDA_ERROR not found!";
}
#endif
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