Creates a convenience target that deletes all the dependency files generated. You should make clean after running this target to ensure the dependency files get regenerated. Returns a list of PTX files generated from the input source files. Compute the name of the intermediate link file used for separable compilation. Note that this is a function and not a macro. Sets the directories that should be passed to nvcc e.
These paths usually contain other. Generates the link object required by separable compilation from the given object files. Note that this is a function instead of a macro. Auto : detects local machine GPU compute arch at runtime. Common and All : cover common and entire subsets of architectures. This is where all the magic happens. Files that don't end with. You can also specify per configuration options by specifying the name of the configuration followed by the options.
General options must precede configuration specific options. Not all configurations need to be specified, only the ones provided will be used. For example:. This is because when you add the cuda file to Visual Studio it knows that this file produces an object file and will link in the resulting object file automatically.
This script will also generate a separate cmake script that is used at build time to invoke nvcc. This is for several reasons:. The script now checks the error codes and produces errors when there was a problem.
This confuses build systems into thinking the target was generated when in fact an unusable file exists. Actually generating precompiled header output could be implemented later or not at all. From my limited testing, the changes made so far are sufficient to allow for language servers to handle cuda headers. The way things are setup in Types. I think cuh is the best choice for this extension. Not having a canonical CUDA header file extension is unfortunate, but this could be addressed at the tooling level if desired.
For example, one could imagine designating a subset of headers to be built with -xcuda-header via a regex or whitelist. As far as I know, ccls and clangd don't currently have a nice way of doing additional header specific compile commands, but I can't imagine this would be particularly difficult to implement.
For the language server use case this isn't necessary much of a problem. I think ccls tries to find the closest match in filename using some sort of metric.
One could imagine trying to match cuda headers with cuda source file to get the correct values for --cuda-path and --cuda-gpu-arch. That would still require properly defined CUDA macros. Without them, -fsyntax-only will not be give you correct results on most of the CUDA code. Hence my suggestion that clang needs at least a minimum subset of CUDA headers to provide the critical subset of macros sufficient to convey critical semantics of CUDA code. It depends on how well clang can recover from the errors induced by the unexpanded CUDA macros.
This could range from OK on simple code to rather badly if we fail to instantiate templated code. Trivial CUDA headers -- maybe.
I have doubts that it would work on something more interesting. Yes, if you provide the flags and use it on complete. If the flags are supplied, then the same process will work for the header files, too. You could be able to process them with -x cuda which will apply the same magic pre-include.
However, applying the same magic to everything that has CUDA header extension as the input is not the right thing to do, IMO, as that would not be what the end user expect. In other words, the magic would be OK for tooling, but not for the general use by default.
Addressing it by the tooling looks like a good place to do it. I can't think of a good way to tell if particular source uses CUDA extensions or not, other than by trying to compile it. We would not know which mode user intended without them explicitly telling us. We may need some sort of knob what to assume if we can't figure it out some other way. There may not be a definitive single answer in some cases.
I guess, ultimately we may need to make tooling aware that the same file may have multiple compiled forms. The AST for each instance is not necessarily identical. Usually it's close, so for now we're getting by by using only host-side compilation, but that may give us incomplete picture. This is somewhat orthogonal to figuring out how to handle CUDA sources in principle, so for now let's still stick with a source-to-AST model.
This would work in some cases, but not in others. This patch will play its role, but I still believe it's a bit premature. It will make some use-cases work, but I think it may be a good chance to make tooling work with CUDA sources in a more consistent manner.
We may not need the special CUDA header type after that. Actually this already works roughly with the changes made so far. For example consider the following header:.
When saved as a. The reason why this "works" is because of the change to Driver. This change makes it so that -x cuda-header is handeled similarly identically? Further changes to Driver. I am pretty sure that header tab completion is totally unrelated to the syntactic validity of headers; it's just finding the list of files in the include path which match the text entered so far and then filtering out files without a accepted extension.
This change just adds ". Makes sense to me. Maybe this approach should always be used when building with "-fsyntax-only" regardless of whether or not the file is a header? This seems like a decent approach to me, but this will result in incorrectly issuing a diagnostic for pragma once.
This can of course be fixed by directly disabling the warning, but this does seem a bit hacky. There may also be other header specific behavior, but I can't think of any. On the whole, it does seem a bit gross for tooling to have to compile headers as though they are main files. Some changes will need to happen below this for correct handling of header files for example not warning about using pragma once.
This example makes a look like a regular host function, instead of the kernel, and that affects how the rest of the TU get parsed.
The list goes on. CUDA compilation in clang rather heavily depends on compiler understanding CUDA-related attributes and those come from the header files. You can make some things work without those attributes, but you will have more cases where you'll get wrong results ranging from slightly wrong to mostly wrong. I'm not against the change. It's just the patch does too much automatically inferring thet. I'm fine adding CUDA header kind, but without automatic inference of the type via extension.
I'm not sure if it should matter much if we only care about -fsyntax-only. The warning for pragma once is a rather unusual case where the distinction does matter.
Introducing a whole new file kind just to work around the warning looks even more hacky than silencing the warning to me. I'm OK with it as long as we have a way not to expose it to the end-users by default until we can make clang's behavior for that data type sensible. No, this part is definitely working, the full set of SDK headers is included. The only required change in v3. The implementations are otherwise completely unchanged from their CPU-only version.
This usage is unnecessary, as this is the default behavior. This is useful if you know this routine will never be needed by the host, or if you want to implement your function using operations specific to the GPU, such as fast math or texture unit operations.
The example code in main. The program then copies the particles back and computes and prints a summary of the total distance traveled by all particles. For each of steps, the program generates a random total distance on the CPU and passes it as an argument to the kernel. You can get the complete example on Github. Using make will work on this project so long as you have the CUDA 5. The following listing shows the contents of the Makefile.
When you run app you can optionally specify two command line arguments. The first is the number of particles to create and run default is 1 million particles. The second number is a random seed, to generate different sequences of particles and distance steps.
The —dc option tells nvcc to generate device code for later linking. Device code linking requires Compute Capability 2. We omit —dc in the link command to tell nvcc to link the objects. Finally, you may not recognize the option —x cu. This option tells nvcc to treat the input files as. By default, nvcc treats. This option is required to have nvcc generate device code here, but it is also a handy way to avoid renaming source files in larger projects.
When you use nvcc to link, there is nothing special to do: replace your normal compiler command with nvcc and it will take care of all the necessary steps.
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