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If you are using compiler-rt, use the same version with the compiler.Early versions provided FP16 builtins in a different ABI. A workaround is to usea small code snippet to check the ABI if you cannot make sure of it.


Any PTX code loaded by an application at runtime is compiled further to binary code by the device driver. This is called just-in-time compilation. Just-in-time compilation increases application load time, but allows the application to benefit from any new compiler improvements coming with each new device driver. It is also the only way for applications to run on devices that did not exist at the time the application was compiled, as detailed in Application Compatibility.


Some PTX instructions are only supported on devices of higher compute capabilities. For example, Warp Shuffle Functions are only supported on devices of compute capability 5.0 and above. The -arch compiler option specifies the compute capability that is assumed when compiling C++ to PTX code. So, code that contains warp shuffle, for example, must be compiled with -arch=compute_50 (or higher).


#define CUDA_API_PER_THREAD_DEFAULT_STREAM 1 cannot be used to enable this behavior when the code is compiled by nvcc as nvcc implicitly includes cuda_runtime.h at the top of the translation unit. In this case the --default-stream per-thread compilation flag needs to be used or the CUDA_API_PER_THREAD_DEFAULT_STREAM macro needs to be defined with the -DCUDA_API_PER_THREAD_DEFAULT_STREAM=1 compiler flag.


When the call stack overflows, the kernel call fails with a stack overflow error if the application is run via a CUDA debugger (CUDA-GDB, Nsight) or an unspecified launch error, otherwise.When the compiler cannot determine the stack size, it issues a warning saying Stack size cannot be statically determined. This is usually the case with recursive functions.Once this warning is issued, user will need to set stack size manually if default stack size is not sufficient.


nvcc assumes that the host compiler is installed with the standard method designed by the compiler provider. If the host compiler installation is non-standard, the user must make sure that the environment is set appropriately and use relevant nvcc compile options.





On all platforms, the default host compiler executable (gcc and g++ on Linux and cl.exe on Windows) found in the current execution search path will be used, unless specified otherwise with appropriate options (see File and Path Specifications).


CUDA compilation works as follows: the input program is preprocessed for device compilation and is compiled to CUDA binary (cubin) and/or PTX intermediate code, which are placed in a fatbinary. The input program is preprocessed once again for host compilation and is synthesized to embed the fatbinary and transform CUDA specific C++ extensions into standard C++ constructs. Then the C++ host compiler compiles the synthesized host code with the embedded fatbinary into a host object. The exact steps that are followed to achieve this are displayed in Figure 1.


Split compilation attempts to reduce compile time by enabling the compiler to run certain optimization passes concurrently. It does this by splitting the device code into smaller translation units, each containing one or more device functions, and running optimization passes on each unit concurrently across multiple threads. It will then link back the split units prior to code generation.The option accepts a numerical value that specifies the maximum number of threads the compiler can use. One can also allow the compiler to use the maximum threads available on the system by setting --split-compile=0. Setting --split-compile=1 will cause this option to be ignored.This option can work in conjunction with device Link Time Optimization (-dlto) as well as --threads.


Compiler front-end: imp

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