Cuda driver api

 · You should usually only use either the runtime API or the driver API in your application although, with newer CUDA versions, runtime API code can peacefully coexist with driver API code (www.doorway.ru) An application can mix runtime API code with driver API code. While the driver wrappers branch has come a long way, it still uses some CUDA runtime API constants, types, and API calls. Some of this might be unavoidable, but many can still be replaced with no difficulty and others may be replaceable. CUDA Toolkit Update 1 Downloads. Select Target Platform. Click on the green buttons that describe your target platform. Only supported platforms will be shown. By downloading and using the software, you agree to fully comply with the terms and conditions of the CUDA EULA. Operating System.

These formats can be used to create BCn formatted CUDA arrays using the cudaMalloc[3D]Array runtime API or cuArray[3D]Create driver API. Similarly, CUDA mipmapped arrays can be created using the cudaMallocMipmappedArray runtime API or cuMipmappedArrayCreate driver API. When creating CUDA arrays with these formats, the array extents must be multiples of the compression block size (4 x 4 for 2D and 4 x 4 x 1 for 3D). You should usually only use either the runtime API or the driver API in your application although, with newer CUDA versions, runtime API code can peacefully coexist with driver API code (www.doorway.ru) An application can mix runtime API code with driver API code. Select Target Platform. Click on the green buttons that describe your target platform. Only supported platforms will be shown. By downloading and using the software, you agree to fully comply with the terms and conditions of the CUDA EULA.

23 de out. de This section describes the VDPAU interoperability functions of the low-level CUDA driver application programming interface. The documentation is grouped according to the modules of the driver API. Error Handling. www.doorway.rur — Type. CuError(code) CuError(code, meta). 17 de out. de The CUDA runtime makes it possible to compile and link your CUDA kernels into executables. This means that you don't have to distribute.