As a CUDA developer, you will often need to control which devices your application uses. In a short-but-sweet post on the Acceleware blog, Chris Mason writes: As Chris points out, robust applications should use the CUDA API to enumerate and select devices with appropriate capabilities at run time. To learn how, read the section on Device Enumeration in the CUDA Programming Guide.
]]>In this third post of the CUDA C/C++ series, we discuss various characteristics of the wide range of CUDA-capable GPUs, how to query device properties from within a CUDA C/C++ program, and how to handle errors. In our last post, about performance metrics, we discussed how to compute the theoretical peak bandwidth of a GPU. This calculation used the GPU��s memory clock rate and bus interface��
]]>In this third post of the CUDA Fortran series we discuss various characteristics of the wide range of CUDA-capable GPUs, how to query device properties from within a CUDA Fortran program, and how to handle errors. In our last post, about performance metrics, we discussed how to compute the theoretical peak bandwidth of a GPU. This calculation used the GPU��s memory clock rate and bus interface��
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