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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). CUDA provides a significant boost in computing performance by harnessing the power of the GPU.
When working with CUDA, you might encounter the error code CUDA_ERROR_NOT_MAPPED
. This error typically occurs when a resource, such as a memory buffer or texture, is accessed without being properly mapped. The symptom is usually an abrupt termination of the program or a failure to execute a kernel that relies on the unmapped resource.
The CUDA_ERROR_NOT_MAPPED
error indicates that an attempt was made to access a resource that has not been mapped into the address space of the CUDA context. In CUDA, resources must be explicitly mapped before they can be accessed by the GPU. This mapping process involves associating a resource with a specific memory address range that the GPU can use for reading or writing data.
cudaHostRegister()
or similar functions.cudaGraphicsMapResources()
before accessing graphics resources.To resolve the CUDA_ERROR_NOT_MAPPED
error, follow these steps:
Ensure that all resources are properly mapped before access. For memory buffers, use cudaHostRegister()
to register the memory and cudaHostUnregister()
to unregister it when done. For graphics resources, use cudaGraphicsMapResources()
to map and cudaGraphicsUnmapResources()
to unmap.
cudaError_t err;
err = cudaGraphicsMapResources(1, &resource, 0);
if (err != cudaSuccess) {
// Handle error
}
Before accessing a resource, check its status to ensure it is mapped. This can be done by querying the resource state if the API provides such functionality, or by maintaining a state flag in your application logic.
Ensure that the resource lifecycle is managed correctly. Resources should be mapped before any access and unmapped after use. This is especially important in applications with complex resource management or multiple threads.
Refer to the CUDA Runtime API Documentation for detailed information on resource management functions. Additionally, the CUDA Toolkit provides comprehensive guides and examples.
By ensuring that resources are properly mapped and managed, you can avoid the CUDA_ERROR_NOT_MAPPED
error and ensure smooth execution of your CUDA applications. Proper resource management is crucial for leveraging the full power of CUDA-enabled GPUs.
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