CUDA Compilers

CUDA_Compilers.md

In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

• 11.3: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
• 11.2: https://docs.nvidia.com/cuda/archive/11.2.2/index.html
• 11.1: https://docs.nvidia.com/cuda/archive/11.1.1/index.html
• 11.0: https://docs.nvidia.com/cuda/archive/11.0/cuda-toolkit-release-notes/index.html
• 10.2: https://developer.download.nvidia.com/compute/cuda/10.2/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 10.1: https://developer.download.nvidia.com/compute/cuda/10.1/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 10.0: https://developer.download.nvidia.com/compute/cuda/10.0/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 9.2: https://developer.download.nvidia.com/compute/cuda/9.2/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 9.1: https://developer.download.nvidia.com/compute/cuda/9.1/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 9.0: https://developer.download.nvidia.com/compute/cuda/9.0/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 8.0: https://developer.nvidia.com/compute/cuda/8.0/Prod2/docs/sidebar/CUDA_Toolkit_Release_Notes-pdf
• 7.5: http://developer.download.nvidia.com/compute/cuda/7.5/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf
• 7.0: http://developer.download.nvidia.com/compute/cuda/7_0/Prod/doc/CUDA_Toolkit_Release_Notes.pdf
• 6.5: http://developer.download.nvidia.com/compute/cuda/6_5/rel/docs/CUDA_Toolkit_Release_Notes.pdf
• 6.0: http://developer.download.nvidia.com/compute/cuda/6_0/rel/docs/CUDA_Toolkit_Release_Notes.pdf
• 5.5: http://developer.download.nvidia.com/compute/cuda/5_5/rel/docs/CUDA_Toolkit_Release_Notes.pdf

Version notes Nvidia HPC SDK:

• https://docs.nvidia.com/hpc-sdk/hpc-sdk-release-notes/index.html

Compatibility guarantees (quote):

• CUDA 10.0: First introduced in CUDA 10, the CUDA Forward Compatible Upgrade is designed to allow users to get access to new CUDA features and run applications built with new CUDA releases on systems with older installations of the NVIDIA datacenter GPU driver.
• CUDA 11.1: First introduced in CUDA 11.1, CUDA Enhanced Compatibility provides two benefits:
  • By leveraging semantic versioning across components in the CUDA Toolkit, an application can be built for one CUDA minor release (such as 11.1) and work across all future minor releases within the major family (such as 11.x).
  • CUDA has relaxed the minimum driver version check and thus no longer requires a driver upgrade with minor releases of the CUDA Toolkit.

nvcc

Latest, officical Compiler requirements: http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

CUDA version	SM Arch	g++	icpc	pgc++	xlC	MSVC	clang++	Linux driver	thrust	note
1.0	1.0-1.1	?	?	?
1.1	1.0-1.1	?	?	?
2.0	1.0-1.1	?	?	?
2.1	1.0-1.3	?	?	?
2.3.1	1.0-1.3	?	?	?
3.0	1.0-2.0	?	?	?
3.1	1.0-2.0	?	?	?
3.2	1.0-2.1	?	11.1	?
4.0	1.0-2.1	<=4.4	11.1	?
4.1	1.0-2.1	<=4.5	11.1	?
4.2	1.0-2.1	<=4.6	11.1	?
5.0	1.0-3.?	<=4.6	11.1	?				?	1.5.3
5.5	1.0-3.?	<=4.8	12.1	?				?	1.7.0	C++11 on host side supported; ICC fixed to build 20110811
6.0	1.0-5.0	<=4.8	13.1	?				331.62	1.7.1
6.5	1.1-5.X	<=4.8	14.0	?		?		?	1.7.2	experimenal device side C++11 support; including this version, <thrust/sort.h> skrews up __CUDA_ARCH__ (must be undefined on host); deprecation of SM 11-13 (10 removed)
7.0.17 (RC)	s. below	<=4.9	15.0	>=14.9	13.1.1	?		346.29	1.8.0	first official PGI support, first xlc string found; powerpc64 w. little endian supported
7.0.27	2.0-5.X	<=4.9	15.0	>=14.9	13.1.1	2010-13		346.46	1.8.1	official C++11 support on device side
7.5		<=4.9	15.0	15.4	13.1	2010-13	3.5-3.6	352.41?	1.8.2	clang (host) on linux supported, __CUDACC_VER__ macros added
7.5.18	2.0-5.X	<=4.9	15.0	15.4	13.1	2010-13		352.39	1.8.2
8.0.44	2.0-6.X	<=5.3	15.0(.4)-16.0	16(.3)+	13.1(.2)	2012-15	3.8-3.9	367.48	1.8.3-patch2	sm_60 (pascal) support added
8.0.61	2.0-6.X	<=5.3	15.0(.4)-17.0	16(.3)+	13.1(.2)	2012-15	3.8-3.9	375.26	1.8.3-patch2	nvcc 8 is incompatible with std::tuple in gcc 5.4+
9.0.69 (RC)	3.0-7.0	<=5.5 (<=6)	15.0(.4)-17.0	17	13.1(.2)	2012-17	3.8-3.9	???.??	1.9.0-patch4	device-side C++14; __CUDACC_VER__ deprecated for __CUDACC_VER_MAJOR/MINOR/BUILD__
9.0.103 (RC)	3.0-7.0	<=5.5 (<=6)	15.0(.4)-17.0	17	13.1(.2)	2012-17	3.8-3.9	384.59	1.9.0-patch4	same as above, __CUDACC_VER__ defined as #error rendering it fully broken
9.0.176	3.0-7.0	<=5.5 (<=6)	(15.0-)17.0	17.1	13.1(.5)	2012-17	(3.8-)3.9	384.81	1.9.0-patch5	same as above
9.1.85	3.0-7.2	<=5.5 (<=6)	(15.0-)17.0	17.X	13.1(.6)	2012-17	(3.8-)4.0	390.46	1.9.1-patch2	math_functions.hpp moved to crt/
9.1.85.1										cuBLAS 9.1.128: Volta GEMM kernels optimized
9.1.85.2										ptxas: fix address calculations using large immediate operands
9.1.85.3										cuBLAS: fixes to GEMM optimizations for convolutional sequence to sequence (seq2seq) models.
9.0-9.1										nvcc 9.0-9.1 is incompatible with std::tuple in gcc 6+
9.2.88	3.0-7.2	<=7.3.0 (<=7)	(15.0-)17.0	17-18.X	13.1(.6),16.1	2012-17	(3.8-)5.0	396.26	1.9.2	CUTLASS 1.0 added; std::tuple fixed (prior GCC 6 issues)
9.2.148								396.37	1.9.2
10.0.130	3.0-7.5	<=7	(15.0-)18.0	17-18.X	13.1, 16.1	2013-17	(3.8-)6.0	410.48	1.9.3	CUDA Forward Compatible Upgrade
10.1.105	3.0-7.5	<=8	(15.0-)19.0	17-19.X		2013-19	(3.8-)7.0	418.39	1.9.4
10.1.168							(3.8-)8.0	418.67		10.1 "Update 1"
10.1.243								418.87		10.1 "Update 2"
10.2.89	3.0-7.5	<=8	(15.0-)19.0	18-19.X	13.1, 16.1	2015-19	(3.3-)8.*	440.33.01	1.9.7	sm_30,35,37,50 deprecated; nvcc: -allow-unsupported-compiler
11.0.1 (RC) NVCC:11.0.167	3.5-8.0	(5-)9.*	(15.0-)19.1	18-20.1	13.1, 16.1	2015-19	3.2-9.0.0	450.36.06	1.9.9	macOS dropped; libs drop pre-C++11, deprecate pre-C++14 (GCC < 5, Clang < 6, and MSVC < 2017); Arm C/C++ 19.2 support
11.0.2-1 NVCC:11.0.194							(3.3/)6-9.0.0	450.51.05		nvcc: --Wext-lambda-captures-this
11.0.3 NVCC:11.0.221	?	?	?	?	?	?	?	450.51.06	?	11.0 "Update 1"; nvcc: --forward-unknown-to-host-compiler, --forward-unknown-to-host-linker flags
11.1.0 NVCC:11.1.74	3.5-8.6	3.5-10.0	(15.0-)19.1	18-20.1	13.1, 16.1	2017-19	(3.3/)6-10.0.0	455.23.05	1.9.10-1	Ubuntu@ppc64le deprecated; CUDA Enhanced Compatibility
11.1.1 NVCC:11.1.?								?	?	?
11.2.0 NVCC:11.2.67								460.27.04	1.10.0
11.2.1 NVCC:.......								460.32.03	?	"Update 1"
11.2.2 NVCC:.......								460.32.03	?	"Update 2"
11.3.0 NVCC:....								465.19.01	?	cu++flt added, Python Driver/RT bindings, alloca()
CUDA version	SM Arch	g++	icpc	pgc++	xlC	MSVC	clang++	Linux driver	thrust	note

Note: empty cells generally mean "same as above" for readability.

macOS: As of 7.0, clang seems to be the only supported compiler on OSX (but no version check found). CUDA 10.1.243 adds support for Xcode 10.2 . CUDA 11.0 dropped macOS support.

Compilers such as pgC, icc, xlC are only supported on x86 linux and little endian.

Dynamic parallelism was added with sm_35 and CUDA 5.0.

Newer CUDA releases have a per-release support matrix for compilers, which also lists supported kernel and glibc versions: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements

clang++ -x cuda

clang++ can compile CUDA C++ to ptx as well. Give it a whirl!

clang++	supported CUDA release	supported SMs
3.9-5.0	7.0-8.0	2.0-(5.0)6.0
6.0	7.0-9.0	(2.0)3.0-7.0
7.0	7.0-9.2	(2.0)3.0-7.2
8.0	7.0-10.0	(2.0)3.0-7.5
9.0	7.0-10.1	(2.0)3.0-7.5
10.0	7.0-10.1	(2.0)3.0-7.5
11.0	7.0-11.0	(2.0)3.0-8.0
12.0rc5	7.0-11.0	(2.0)3.0-8.0
main	7.0-11.2	(2.0)3.0-8.6

https://llvm.org/docs/CompileCudaWithLLVM.html

Device-Side C++ Standard Support

C++ core language features:

	supported C++ standard	notes
nvcc -6.0	c++03
nvcc 6.5	c++03, exp. c++11	undocumented
nvcc 7.0-8.0	c++03,11	only c++11 switch
nvcc 9.0-10.2	c++03,11,14	10.2 adds libcu++ (atomics); open repository: https://github.com/NVIDIA/libcudacxx/releases
nvcc 11.0.167+	c++03,11,14,17	C++11 host compiler needed for math libs; ships C++11-compatible backport of the C++20 synchronization library; device LTO added; starting with CUDA Toolkit 11.0.1, nvcc and CUDA Toolkit versions are not equivalent anymore
clang 5+	c++03,11,14,17
clang 6+	c++03,11,14,17,2a
clang 10+	c++03,11,14,17,20
clang trunk	c++03,11,14,17,20	status

CUDA-enabled C++ standard library libcu++, based on LLVM's libc++ (docs):

	introduced components	notes
CUDA 10.2	<atomic> (SM6.0+), <type_traits>	introduction of libcu++
CUDA 11.0	atomic<T>::wait/notify, <barrier>, <latch>, <counting_semaphore>(SM7.0+), <chrono>, <ratio>, <functional> w/o function	anticipated with GTC 2020 slides
CUDA 11.2	cuda::std::tuple,pair	notes
CUDA next	cuda::std::complex, backports: chrono, type_traits	notes
newer	see the release notes and api docs	all open source now

Incremental libcu++ release goals (GTC 2020):

• Version 1 (CUDA 10.2): <atomic>(SM6.0+), <type_traits>.
• Version 2 (CUDA next): atomic<T>::wait/notify, <barrier>, <latch>, <counting_semaphore>(SM7.0+), <chrono>, <ratio>, <functional>minus function.
• Future priorities: atomic_ref<T>, <complex>, <tuple>, <array>, <utility>, <cmath>, string processing, ...

NVC++

NVC++ is a unified C++ compiler and GPU-accelerated STL for the CUDA platform. It also seems to support OpenACC. NVC++ does currently not support the CUDA C++ language.

	supported C++ standard	notes
nvc++ 11.0	...,c++17	initial release, ships C++11-compatible backport of the C++20 synchronization library

All GPU compilers are cheese.

It appears that release notes are in a different location now:
https://developer.download.nvidia.com/compute/cuda/X.Y/Prod/docs/sidebar/CUDA_Toolkit_Release_Notes.pdf

Thanks! They seem to change erratically between releases. Likely because they manually upload them to some CMS as it looks.

Thanks for collecting all this information. We are currently in the process of deciding which approach (NVCC or Clang) is the future and which we should support in the build2 build system. The Clang way definitely seems saner from the build system's POV but a bit of googling suggests NVCC is still the predominantly used approach while the Clang CUDA page hasn't seen any updates in a while. Is my impression accurate?

Ideally a build system should support both. Nvcc's approach is the current (03-2020) official one and significantly harder than clang's, e.g. to propagate compilation options to depending projects. Clang is usually updating a few months after a CUDA release, so far only 10.2 is lacking a bit longer. Maybe @Artem-B knows more on this? We definitely use clang-cuda downstream for direct compile as well as CUDA JIT in Cling.

I've just updated Clang's docs a bit. My guess is that both NVCC and clang will stay around.
As it happens, CMake is about to add support for clang to its CUDA compilation, so they are going to support both clang and nvcc.

NVCC has all the bells and whistles, and will always be ahead of clang in terms of support for new GPU architectures. On the other hand, for large projects like Tensorflow, nvcc is a rather heavy maintenance burden. We are constantly fighting all sorts of corner cases that pop up due to quirks of NVCC,s front-end, the host compiler used by NVCC and multiple source code projects with various degrees of compiler portability. It's hard enough to make code portable for one compiler on multiple platforms. Making it portable for all combinations of {clang|nvcc{clang,gcc,msvc}},{windows, linux} is a constant game of whack-a-mole. Reducing it to clang everywhere makes things simpler and much more robust.

Clang only targets essential CUDA functionality, so for things like textures, __managed__, etc. one will need nvcc (though AMD folks have just sent a patch to improve surface/texture support: https://reviews.llvm.org/D76365). Things like nvcc -rdc are not integrated into compiler and have to be done by the build system (which is conceptually the place to do it, but it's currently a burden on the end-user).
On the positive side, in addition to simplifying maintenance, Clang also has a huge advantage of being open-source. If there are bugs, they are possible to fix. Our round-trip time for detect-fix-integrate ends up being as short as O(days). Being able to piggy-back on all the latest C++ features is also a plus. NVCC and MSVC are lagging behind in that respect.

Thanks, I agree with all points and thank you for the insights. Thank you for advancing Clang-CUDA, we appreciate this effort a lot for exactly the reasons mentioned.

@ax3l, @Artem-B thanks for the feedback.

I agree with Artem's points, Clang's compilation model looks a lot saner from the build system's POV. With NVCC we will most likely need to decompose all the steps that it performs under the hood (like invoking the host compiler) and perform them ourselves if we have any chance of having proper header dependency extraction with support for auto-generated headers (and thinking about C++20 modules in this context just makes my head hurt). With CUDA-Clang it seems like we could just use our standard logic that we use for the Vanilla-Clang. Unfortunately, however, the feedback I am hearing from the potential users is that they have to use NVCC for various reasons (see the build2 issue I linked above for details).

One question about the "Reducing it to clang everywhere makes things simpler and much more robust" remark: the documentation page say the "Compilation on MacOS and Windows may or may not work and currently have no maintainers". Is this still accurate?

One question about the "Reducing it to clang everywhere makes things simpler and much more robust" remark: the documentation page say the "Compilation on MacOS and Windows may or may not work and currently have no maintainers". Is this still accurate?

Yes.

CUDA-10.2 is the last release to support MacOS, so it's probably the end of the road for it in clang, too.

Clang on windows is largely driven by the Chrome team, but it only covers C++ compilation. If/when Tensorflow switches to clang, we'll likely put more resources into CUDA compilation on windows, too, but at the moment nobody's in charge.

so it's probably the end of the road for it in clang, too

I doubt it. Apple didn't drive LLVM supporting a PTX back end, Google did, precisely so that they are not dependent on nvcc.

Hi Mark, let me rephrase what the clang +cuda (prior: gpucc) author and maintainer from Google that you quote wrote: this is as of today the end for any newer CUDA on macOS. You cannot run or even build a CUDA software stack with a compiler alone on macOS. clang +cuda integrates with several parts of the CUDA-Toolkit. And there are since years no new Apple computers with Nvidia GPU to run it on.

Just to correct the history: Nvidia provided the PTX backend for LLVM, Google the CUDA frontend.

Good news everyone, CMake's integration of Clang as a CUDA compiler is moving forward.

@mabraham PTX is just text assembly. At the very least you need ptxas, libcuda and libcudart in order to compile it to the actual GPU binary and launch it. libcuda comes with the driver where NVIDIA supports CUDA and it is likely to go away once NVIDIA drops CUDA support on mac. ptxas and libcudart come with CUDA, so they will also be gone. The end result is that even though clang will still be able to generate PTX, it will be nearly useless. MacOS will end up in the same situation where FreeBSD is now -- they have clang, they've had NVIDIA drivers for a pretty long time, they can run Linux's CUDA apps, but there's no native libcuda or libcudart there (linux's ptxas could be run under linux emulation), so they can't use clang to create native CUDA apps. :-(

Just to correct the history: Nvidia provided the PTX backend for LLVM, Google the CUDA frontend.

Google has been the major contributor to NVPTX back-end development in LLVM, too. NVIDIA itself has been conspicuously missing.

Thank you for the details and clarification, really appreciate your work and insights.

Agree CUDA on Mac is dead sometime soon. Compiling CUDA with clang is a viable proposition on at least Linux now and moving forward :-)

@ax3l: since you added question marks into the support row for CUDA 11.2.
I spotted no changes so you can remove ?

CUDA 11.2.0_460.27.04

• nvcc + clang as host compiler:
  • clang 3.3+ - 10.x are supported

#if defined(__clang__) && !defined(__ibmxl_vrm__) && !defined(__ICC) && !defined(__HORIZON__) && !defined(__APPLE__)
#if (__clang_major__ >= 11) || (__clang_major__ < 3) || ((__clang_major__ == 3) &&  (__clang_minor__ < 3))
#error -- unsupported clang version! clang version must be less than 11 and greater than 3.2 . The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
//...

• nvcc + gcc as host compiler:
  • up to gcc 10.X are supported

#if defined(__GNUC__)
#if __GNUC__ > 10
#error -- unsupported GNU version! gcc versions later than 10 are not supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
//...

• pgi 18.X - 20.X

#if defined(__PGIC__)
#if ((__PGIC__ != 18) && (__PGIC__ != 19) && (__PGIC__ != 20) && !(__PGIC__ == 99 && __PGIC_MINOR__ == 99))
#error -- unsupported pgc++ configuration! Only pgc++ 18, 19 and 20 are supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.

• XL

  • ... only xlC 13.1 and 16.1 ...

• ICC

  • (15.0) - 19.1

#if (__ICC != 1500 && __ICC != 1600 && __ICC != 1700 && __ICC != 1800 && !(__ICC >= 1900 && __ICC <= 1999)) || !defined(__GNUC__) || !defined(__LP64__)

#error -- unsupported ICC configuration! Only ICC 15.0, ICC 16.0, ICC 17.0, ICC 18.0 and ICC 19.x on Linux x86_64 are supported! 

• MSC

#if _MSC_VER < 1910 || _MSC_VER >= 1930
#error -- unsupported Microsoft Visual Studio version! Only the versions between 2017 and 2019 (inclusive) are

@ax3l @bernhardmgruber pointed out that the table is saying clang 3.X is not supported for CUDA 11.X. I think it is wrong because clang 3.3+ is supported.

@Artem-B (and others)

Any idea when clang will support cuda 11? At my group, we are undecided if to choose c++20 and cuda 10.1 or c++17 and cuda 11 (with nvcc instead of clang), for an upcoming project which we will start in around 3 months.
Ideally we would prefer to use clang w/ support for cuda 11 (as we will be working with new ampere cards) but some of the features (i.e. modules) of c++ 20 are too good to ignore for a project that will be written from scratch.

Clang (top-of-the-tree) is able to compile with CUDA-11.x. It works well enough to compile TensorFlow. What's missing is the support for the full set of the new TensorCore instructions for newer GPUs (that's been the case for a while, already), ability to target sm_86, and support for bf16/tf32 types. Existing code that compiles with CUDA-10.1 is expected to compile with CUDA-11.x.

I'll likely add sm_86 support in January. The rest is a larger undertaking with no specific plans to get it done. In practice the code that would need TensorCore instructions tends to use inline assembly for the new instructions and should compile with clang now. Support for bf16/tf32 will probably not happen any time soon.

@Artem-B
Thanks for the update. We already have a few 3090 (which we will be using fp16) while we wait for the a100 system to be delivered, so we will probably start working with clang and then see if its worth to port the c++ 20 code to c++17 to be used with nvcc.

Also, any idea of how clang is comparing to nvcc in terms of optimal code/performance?
We will be using cuda/cudnn/cublas.

Also, any idea of how clang is comparing to nvcc in terms of optimal code/performance?

Performance-wise clang is usually on par. Sometimes a bit better (it tends to have a bit better optimizer), sometimes a bit worse (e.g. NVCC is much more aggressive at unrolling loops). A lot of the differences are eliminated due to the fact that both NVCC and clang in the end use ptxas which optimizes generated PTX and often produces nearly identical SASS for somewhat different PTX from both compilers.

We will be using cuda/cudnn/cublas.

For using NVIDIA's libraries compiler does not matter -- it's the same library regardless of whether you use clang or nvcc.
For CUDA sources, see above.

Great, Thanks.

@psychocoderHPC thank you for the update!

I updated the old clang versions, since the changelog does officially drop support for them. That means the library teams at Nvidia do not run CI against it anymore, so the range of usage will get narrower and less useful for those ancient compiler releases with modern CUDA releases (think: cuRand, cuBLAS, cuFFT, cub and thrust). Also, Nvidia libs transition all to C++14, so essentially you don't want to bother with gcc<5 and clang<5 anymore with CUDA11+.

With that disclaimer said, I updated the table accordingly nonetheless :) (putting ancient asserts in brackets)

Note that https://llvm.org/docs/CompileCudaWithLLVM.html still documents that latest supported CUDA is 10.1. Whose error is that?

@mabraham The documentation is in this file: https://github.com/llvm/llvm-project/blob/main/llvm/docs/CompileCudaWithLLVM.rst
You could send a PR to http://reviews.llvm.org and assign/request @Artem-B for a review :)

latest supported CUDA is 10.1.
This is still true. This is the version for which clang implements all builtins needed by CUDA headers.
More recent CUDA versions will mostly compile and work as long as you don't happen to need the new compiler builtins. I.e. if you include mma.h or cuda_fp16.h when bf16/tf32 types are enabled in CUDA-11.x, things will likely break. Most of the code that compiles with CUDA-11.1 will still compile with newer CUDA versions, so clang issues a warning, but allows compilation to proceed. The results vary.

I want to build PyTorch from source but this seems like a mystery to me. I don't know how to execute all this code, is there a tutorial for that?

You can check out the PyTorch documentation or use a from-source package manager like Spack (package: py-torch). This comment section is not the right place for support for specific CUDA-dependent software though, all we do is document CUDA compiler compatibility.