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CUDA, which stands for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing, an approach known as GPGPU (General-Purpose computing on Graphics Processing Units). The primary purpose of CUDA is to enable dramatic increases in computing performance by harnessing the power of the GPU.
When working with CUDA, you might encounter the error code CUDA_ERROR_NOT_MAPPED_AS_POINTER
. This error typically manifests when a resource is accessed as a pointer, but it has not been mapped as such. The symptom is usually an abrupt termination of the program or a failure to execute a CUDA kernel that relies on the resource in question.
The error CUDA_ERROR_NOT_MAPPED_AS_POINTER
occurs when a resource, such as a memory buffer, is accessed as a pointer without being properly mapped. In CUDA, resources need to be explicitly mapped to be accessed in a specific manner. This error indicates that the mapping step was either skipped or incorrectly executed. For more details on CUDA error codes, you can refer to the CUDA Runtime API documentation.
Ensure that the resource you are trying to access as a pointer is indeed mapped as such. You can do this by checking the code section where the mapping is supposed to occur. Look for functions like cudaHostRegister
or cudaHostGetDevicePointer
that are responsible for mapping host memory to device pointers.
If the mapping is missing or incorrect, you need to add or correct it. Use the following command to map a host memory to a device pointer:
cudaHostRegister(hostPtr, size, cudaHostRegisterMapped);
cudaHostGetDevicePointer(&devicePtr, hostPtr, 0);
Ensure that hostPtr
is the pointer to your host memory and devicePtr
is the pointer you will use in your CUDA kernel.
After mapping, validate that the resource is correctly mapped by checking the return status of the mapping functions. They should return cudaSuccess
. If not, investigate further based on the specific error code returned.
For more information on memory management in CUDA, you can visit the NVIDIA Developer Blog. This resource provides a comprehensive guide on how to manage memory effectively in CUDA applications.
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