562 lines
21 KiB
ReStructuredText
562 lines
21 KiB
ReStructuredText
=========================
|
|
Compiling CUDA with clang
|
|
=========================
|
|
|
|
.. contents::
|
|
:local:
|
|
|
|
Introduction
|
|
============
|
|
|
|
This document describes how to compile CUDA code with clang, and gives some
|
|
details about LLVM and clang's CUDA implementations.
|
|
|
|
This document assumes a basic familiarity with CUDA. Information about CUDA
|
|
programming can be found in the
|
|
`CUDA programming guide
|
|
<http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html>`_.
|
|
|
|
Compiling CUDA Code
|
|
===================
|
|
|
|
Prerequisites
|
|
-------------
|
|
|
|
CUDA is supported since llvm 3.9. Clang currently supports CUDA 7.0 through
|
|
11.5. If clang detects a newer CUDA version, it will issue a warning and will
|
|
attempt to use detected CUDA SDK it as if it were CUDA 11.5.
|
|
|
|
Before you build CUDA code, you'll need to have installed the CUDA SDK. See
|
|
`NVIDIA's CUDA installation guide
|
|
<https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>`_ for
|
|
details. Note that clang `maynot support
|
|
<https://bugs.llvm.org/show_bug.cgi?id=26966>`_ the CUDA toolkit as installed by
|
|
some Linux package managers. Clang does attempt to deal with specific details of
|
|
CUDA installation on a handful of common Linux distributions, but in general the
|
|
most reliable way to make it work is to install CUDA in a single directory from
|
|
NVIDIA's `.run` package and specify its location via `--cuda-path=...` argument.
|
|
|
|
CUDA compilation is supported on Linux. Compilation on MacOS and Windows may or
|
|
may not work and currently have no maintainers.
|
|
|
|
Invoking clang
|
|
--------------
|
|
|
|
Invoking clang for CUDA compilation works similarly to compiling regular C++.
|
|
You just need to be aware of a few additional flags.
|
|
|
|
You can use `this <https://gist.github.com/855e277884eb6b388cd2f00d956c2fd4>`_
|
|
program as a toy example. Save it as ``axpy.cu``. (Clang detects that you're
|
|
compiling CUDA code by noticing that your filename ends with ``.cu``.
|
|
Alternatively, you can pass ``-x cuda``.)
|
|
|
|
To build and run, run the following commands, filling in the parts in angle
|
|
brackets as described below:
|
|
|
|
.. code-block:: console
|
|
|
|
$ clang++ axpy.cu -o axpy --cuda-gpu-arch=<GPU arch> \
|
|
-L<CUDA install path>/<lib64 or lib> \
|
|
-lcudart_static -ldl -lrt -pthread
|
|
$ ./axpy
|
|
y[0] = 2
|
|
y[1] = 4
|
|
y[2] = 6
|
|
y[3] = 8
|
|
|
|
On MacOS, replace `-lcudart_static` with `-lcudart`; otherwise, you may get
|
|
"CUDA driver version is insufficient for CUDA runtime version" errors when you
|
|
run your program.
|
|
|
|
* ``<CUDA install path>`` -- the directory where you installed CUDA SDK.
|
|
Typically, ``/usr/local/cuda``.
|
|
|
|
Pass e.g. ``-L/usr/local/cuda/lib64`` if compiling in 64-bit mode; otherwise,
|
|
pass e.g. ``-L/usr/local/cuda/lib``. (In CUDA, the device code and host code
|
|
always have the same pointer widths, so if you're compiling 64-bit code for
|
|
the host, you're also compiling 64-bit code for the device.) Note that as of
|
|
v10.0 CUDA SDK `no longer supports compilation of 32-bit
|
|
applications <https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#deprecated-features>`_.
|
|
|
|
* ``<GPU arch>`` -- the `compute capability
|
|
<https://developer.nvidia.com/cuda-gpus>`_ of your GPU. For example, if you
|
|
want to run your program on a GPU with compute capability of 3.5, specify
|
|
``--cuda-gpu-arch=sm_35``.
|
|
|
|
Note: You cannot pass ``compute_XX`` as an argument to ``--cuda-gpu-arch``;
|
|
only ``sm_XX`` is currently supported. However, clang always includes PTX in
|
|
its binaries, so e.g. a binary compiled with ``--cuda-gpu-arch=sm_30`` would be
|
|
forwards-compatible with e.g. ``sm_35`` GPUs.
|
|
|
|
You can pass ``--cuda-gpu-arch`` multiple times to compile for multiple archs.
|
|
|
|
The `-L` and `-l` flags only need to be passed when linking. When compiling,
|
|
you may also need to pass ``--cuda-path=/path/to/cuda`` if you didn't install
|
|
the CUDA SDK into ``/usr/local/cuda`` or ``/usr/local/cuda-X.Y``.
|
|
|
|
Flags that control numerical code
|
|
---------------------------------
|
|
|
|
If you're using GPUs, you probably care about making numerical code run fast.
|
|
GPU hardware allows for more control over numerical operations than most CPUs,
|
|
but this results in more compiler options for you to juggle.
|
|
|
|
Flags you may wish to tweak include:
|
|
|
|
* ``-ffp-contract={on,off,fast}`` (defaults to ``fast`` on host and device when
|
|
compiling CUDA) Controls whether the compiler emits fused multiply-add
|
|
operations.
|
|
|
|
* ``off``: never emit fma operations, and prevent ptxas from fusing multiply
|
|
and add instructions.
|
|
* ``on``: fuse multiplies and adds within a single statement, but never
|
|
across statements (C11 semantics). Prevent ptxas from fusing other
|
|
multiplies and adds.
|
|
* ``fast``: fuse multiplies and adds wherever profitable, even across
|
|
statements. Doesn't prevent ptxas from fusing additional multiplies and
|
|
adds.
|
|
|
|
Fused multiply-add instructions can be much faster than the unfused
|
|
equivalents, but because the intermediate result in an fma is not rounded,
|
|
this flag can affect numerical code.
|
|
|
|
* ``-fcuda-flush-denormals-to-zero`` (default: off) When this is enabled,
|
|
floating point operations may flush `denormal
|
|
<https://en.wikipedia.org/wiki/Denormal_number>`_ inputs and/or outputs to 0.
|
|
Operations on denormal numbers are often much slower than the same operations
|
|
on normal numbers.
|
|
|
|
* ``-fcuda-approx-transcendentals`` (default: off) When this is enabled, the
|
|
compiler may emit calls to faster, approximate versions of transcendental
|
|
functions, instead of using the slower, fully IEEE-compliant versions. For
|
|
example, this flag allows clang to emit the ptx ``sin.approx.f32``
|
|
instruction.
|
|
|
|
This is implied by ``-ffast-math``.
|
|
|
|
Standard library support
|
|
========================
|
|
|
|
In clang and nvcc, most of the C++ standard library is not supported on the
|
|
device side.
|
|
|
|
``<math.h>`` and ``<cmath>``
|
|
----------------------------
|
|
|
|
In clang, ``math.h`` and ``cmath`` are available and `pass
|
|
<https://github.com/llvm/llvm-test-suite/blob/main/External/CUDA/math_h.cu>`_
|
|
`tests
|
|
<https://github.com/llvm/llvm-test-suite/blob/main/External/CUDA/cmath.cu>`_
|
|
adapted from libc++'s test suite.
|
|
|
|
In nvcc ``math.h`` and ``cmath`` are mostly available. Versions of ``::foof``
|
|
in namespace std (e.g. ``std::sinf``) are not available, and where the standard
|
|
calls for overloads that take integral arguments, these are usually not
|
|
available.
|
|
|
|
.. code-block:: c++
|
|
|
|
#include <math.h>
|
|
#include <cmath.h>
|
|
|
|
// clang is OK with everything in this function.
|
|
__device__ void test() {
|
|
std::sin(0.); // nvcc - ok
|
|
std::sin(0); // nvcc - error, because no std::sin(int) override is available.
|
|
sin(0); // nvcc - same as above.
|
|
|
|
sinf(0.); // nvcc - ok
|
|
std::sinf(0.); // nvcc - no such function
|
|
}
|
|
|
|
``<std::complex>``
|
|
------------------
|
|
|
|
nvcc does not officially support ``std::complex``. It's an error to use
|
|
``std::complex`` in ``__device__`` code, but it often works in ``__host__
|
|
__device__`` code due to nvcc's interpretation of the "wrong-side rule" (see
|
|
below). However, we have heard from implementers that it's possible to get
|
|
into situations where nvcc will omit a call to an ``std::complex`` function,
|
|
especially when compiling without optimizations.
|
|
|
|
As of 2016-11-16, clang supports ``std::complex`` without these caveats. It is
|
|
tested with libstdc++ 4.8.5 and newer, but is known to work only with libc++
|
|
newer than 2016-11-16.
|
|
|
|
``<algorithm>``
|
|
---------------
|
|
|
|
In C++14, many useful functions from ``<algorithm>`` (notably, ``std::min`` and
|
|
``std::max``) become constexpr. You can therefore use these in device code,
|
|
when compiling with clang.
|
|
|
|
Detecting clang vs NVCC from code
|
|
=================================
|
|
|
|
Although clang's CUDA implementation is largely compatible with NVCC's, you may
|
|
still want to detect when you're compiling CUDA code specifically with clang.
|
|
|
|
This is tricky, because NVCC may invoke clang as part of its own compilation
|
|
process! For example, NVCC uses the host compiler's preprocessor when
|
|
compiling for device code, and that host compiler may in fact be clang.
|
|
|
|
When clang is actually compiling CUDA code -- rather than being used as a
|
|
subtool of NVCC's -- it defines the ``__CUDA__`` macro. ``__CUDA_ARCH__`` is
|
|
defined only in device mode (but will be defined if NVCC is using clang as a
|
|
preprocessor). So you can use the following incantations to detect clang CUDA
|
|
compilation, in host and device modes:
|
|
|
|
.. code-block:: c++
|
|
|
|
#if defined(__clang__) && defined(__CUDA__) && !defined(__CUDA_ARCH__)
|
|
// clang compiling CUDA code, host mode.
|
|
#endif
|
|
|
|
#if defined(__clang__) && defined(__CUDA__) && defined(__CUDA_ARCH__)
|
|
// clang compiling CUDA code, device mode.
|
|
#endif
|
|
|
|
Both clang and nvcc define ``__CUDACC__`` during CUDA compilation. You can
|
|
detect NVCC specifically by looking for ``__NVCC__``.
|
|
|
|
Dialect Differences Between clang and nvcc
|
|
==========================================
|
|
|
|
There is no formal CUDA spec, and clang and nvcc speak slightly different
|
|
dialects of the language. Below, we describe some of the differences.
|
|
|
|
This section is painful; hopefully you can skip this section and live your life
|
|
blissfully unaware.
|
|
|
|
Compilation Models
|
|
------------------
|
|
|
|
Most of the differences between clang and nvcc stem from the different
|
|
compilation models used by clang and nvcc. nvcc uses *split compilation*,
|
|
which works roughly as follows:
|
|
|
|
* Run a preprocessor over the input ``.cu`` file to split it into two source
|
|
files: ``H``, containing source code for the host, and ``D``, containing
|
|
source code for the device.
|
|
|
|
* For each GPU architecture ``arch`` that we're compiling for, do:
|
|
|
|
* Compile ``D`` using nvcc proper. The result of this is a ``ptx`` file for
|
|
``P_arch``.
|
|
|
|
* Optionally, invoke ``ptxas``, the PTX assembler, to generate a file,
|
|
``S_arch``, containing GPU machine code (SASS) for ``arch``.
|
|
|
|
* Invoke ``fatbin`` to combine all ``P_arch`` and ``S_arch`` files into a
|
|
single "fat binary" file, ``F``.
|
|
|
|
* Compile ``H`` using an external host compiler (gcc, clang, or whatever you
|
|
like). ``F`` is packaged up into a header file which is force-included into
|
|
``H``; nvcc generates code that calls into this header to e.g. launch
|
|
kernels.
|
|
|
|
clang uses *merged parsing*. This is similar to split compilation, except all
|
|
of the host and device code is present and must be semantically-correct in both
|
|
compilation steps.
|
|
|
|
* For each GPU architecture ``arch`` that we're compiling for, do:
|
|
|
|
* Compile the input ``.cu`` file for device, using clang. ``__host__`` code
|
|
is parsed and must be semantically correct, even though we're not
|
|
generating code for the host at this time.
|
|
|
|
The output of this step is a ``ptx`` file ``P_arch``.
|
|
|
|
* Invoke ``ptxas`` to generate a SASS file, ``S_arch``. Note that, unlike
|
|
nvcc, clang always generates SASS code.
|
|
|
|
* Invoke ``fatbin`` to combine all ``P_arch`` and ``S_arch`` files into a
|
|
single fat binary file, ``F``.
|
|
|
|
* Compile ``H`` using clang. ``__device__`` code is parsed and must be
|
|
semantically correct, even though we're not generating code for the device
|
|
at this time.
|
|
|
|
``F`` is passed to this compilation, and clang includes it in a special ELF
|
|
section, where it can be found by tools like ``cuobjdump``.
|
|
|
|
(You may ask at this point, why does clang need to parse the input file
|
|
multiple times? Why not parse it just once, and then use the AST to generate
|
|
code for the host and each device architecture?
|
|
|
|
Unfortunately this can't work because we have to define different macros during
|
|
host compilation and during device compilation for each GPU architecture.)
|
|
|
|
clang's approach allows it to be highly robust to C++ edge cases, as it doesn't
|
|
need to decide at an early stage which declarations to keep and which to throw
|
|
away. But it has some consequences you should be aware of.
|
|
|
|
Overloading Based on ``__host__`` and ``__device__`` Attributes
|
|
---------------------------------------------------------------
|
|
|
|
Let "H", "D", and "HD" stand for "``__host__`` functions", "``__device__``
|
|
functions", and "``__host__ __device__`` functions", respectively. Functions
|
|
with no attributes behave the same as H.
|
|
|
|
nvcc does not allow you to create H and D functions with the same signature:
|
|
|
|
.. code-block:: c++
|
|
|
|
// nvcc: error - function "foo" has already been defined
|
|
__host__ void foo() {}
|
|
__device__ void foo() {}
|
|
|
|
However, nvcc allows you to "overload" H and D functions with different
|
|
signatures:
|
|
|
|
.. code-block:: c++
|
|
|
|
// nvcc: no error
|
|
__host__ void foo(int) {}
|
|
__device__ void foo() {}
|
|
|
|
In clang, the ``__host__`` and ``__device__`` attributes are part of a
|
|
function's signature, and so it's legal to have H and D functions with
|
|
(otherwise) the same signature:
|
|
|
|
.. code-block:: c++
|
|
|
|
// clang: no error
|
|
__host__ void foo() {}
|
|
__device__ void foo() {}
|
|
|
|
HD functions cannot be overloaded by H or D functions with the same signature:
|
|
|
|
.. code-block:: c++
|
|
|
|
// nvcc: error - function "foo" has already been defined
|
|
// clang: error - redefinition of 'foo'
|
|
__host__ __device__ void foo() {}
|
|
__device__ void foo() {}
|
|
|
|
// nvcc: no error
|
|
// clang: no error
|
|
__host__ __device__ void bar(int) {}
|
|
__device__ void bar() {}
|
|
|
|
When resolving an overloaded function, clang considers the host/device
|
|
attributes of the caller and callee. These are used as a tiebreaker during
|
|
overload resolution. See `IdentifyCUDAPreference
|
|
<https://clang.llvm.org/doxygen/SemaCUDA_8cpp.html>`_ for the full set of rules,
|
|
but at a high level they are:
|
|
|
|
* D functions prefer to call other Ds. HDs are given lower priority.
|
|
|
|
* Similarly, H functions prefer to call other Hs, or ``__global__`` functions
|
|
(with equal priority). HDs are given lower priority.
|
|
|
|
* HD functions prefer to call other HDs.
|
|
|
|
When compiling for device, HDs will call Ds with lower priority than HD, and
|
|
will call Hs with still lower priority. If it's forced to call an H, the
|
|
program is malformed if we emit code for this HD function. We call this the
|
|
"wrong-side rule", see example below.
|
|
|
|
The rules are symmetrical when compiling for host.
|
|
|
|
Some examples:
|
|
|
|
.. code-block:: c++
|
|
|
|
__host__ void foo();
|
|
__device__ void foo();
|
|
|
|
__host__ void bar();
|
|
__host__ __device__ void bar();
|
|
|
|
__host__ void test_host() {
|
|
foo(); // calls H overload
|
|
bar(); // calls H overload
|
|
}
|
|
|
|
__device__ void test_device() {
|
|
foo(); // calls D overload
|
|
bar(); // calls HD overload
|
|
}
|
|
|
|
__host__ __device__ void test_hd() {
|
|
foo(); // calls H overload when compiling for host, otherwise D overload
|
|
bar(); // always calls HD overload
|
|
}
|
|
|
|
Wrong-side rule example:
|
|
|
|
.. code-block:: c++
|
|
|
|
__host__ void host_only();
|
|
|
|
// We don't codegen inline functions unless they're referenced by a
|
|
// non-inline function. inline_hd1() is called only from the host side, so
|
|
// does not generate an error. inline_hd2() is called from the device side,
|
|
// so it generates an error.
|
|
inline __host__ __device__ void inline_hd1() { host_only(); } // no error
|
|
inline __host__ __device__ void inline_hd2() { host_only(); } // error
|
|
|
|
__host__ void host_fn() { inline_hd1(); }
|
|
__device__ void device_fn() { inline_hd2(); }
|
|
|
|
// This function is not inline, so it's always codegen'ed on both the host
|
|
// and the device. Therefore, it generates an error.
|
|
__host__ __device__ void not_inline_hd() { host_only(); }
|
|
|
|
For the purposes of the wrong-side rule, templated functions also behave like
|
|
``inline`` functions: They aren't codegen'ed unless they're instantiated
|
|
(usually as part of the process of invoking them).
|
|
|
|
clang's behavior with respect to the wrong-side rule matches nvcc's, except
|
|
nvcc only emits a warning for ``not_inline_hd``; device code is allowed to call
|
|
``not_inline_hd``. In its generated code, nvcc may omit ``not_inline_hd``'s
|
|
call to ``host_only`` entirely, or it may try to generate code for
|
|
``host_only`` on the device. What you get seems to depend on whether or not
|
|
the compiler chooses to inline ``host_only``.
|
|
|
|
Member functions, including constructors, may be overloaded using H and D
|
|
attributes. However, destructors cannot be overloaded.
|
|
|
|
Using a Different Class on Host/Device
|
|
--------------------------------------
|
|
|
|
Occasionally you may want to have a class with different host/device versions.
|
|
|
|
If all of the class's members are the same on the host and device, you can just
|
|
provide overloads for the class's member functions.
|
|
|
|
However, if you want your class to have different members on host/device, you
|
|
won't be able to provide working H and D overloads in both classes. In this
|
|
case, clang is likely to be unhappy with you.
|
|
|
|
.. code-block:: c++
|
|
|
|
#ifdef __CUDA_ARCH__
|
|
struct S {
|
|
__device__ void foo() { /* use device_only */ }
|
|
int device_only;
|
|
};
|
|
#else
|
|
struct S {
|
|
__host__ void foo() { /* use host_only */ }
|
|
double host_only;
|
|
};
|
|
|
|
__device__ void test() {
|
|
S s;
|
|
// clang generates an error here, because during host compilation, we
|
|
// have ifdef'ed away the __device__ overload of S::foo(). The __device__
|
|
// overload must be present *even during host compilation*.
|
|
S.foo();
|
|
}
|
|
#endif
|
|
|
|
We posit that you don't really want to have classes with different members on H
|
|
and D. For example, if you were to pass one of these as a parameter to a
|
|
kernel, it would have a different layout on H and D, so would not work
|
|
properly.
|
|
|
|
To make code like this compatible with clang, we recommend you separate it out
|
|
into two classes. If you need to write code that works on both host and
|
|
device, consider writing an overloaded wrapper function that returns different
|
|
types on host and device.
|
|
|
|
.. code-block:: c++
|
|
|
|
struct HostS { ... };
|
|
struct DeviceS { ... };
|
|
|
|
__host__ HostS MakeStruct() { return HostS(); }
|
|
__device__ DeviceS MakeStruct() { return DeviceS(); }
|
|
|
|
// Now host and device code can call MakeStruct().
|
|
|
|
Unfortunately, this idiom isn't compatible with nvcc, because it doesn't allow
|
|
you to overload based on the H/D attributes. Here's an idiom that works with
|
|
both clang and nvcc:
|
|
|
|
.. code-block:: c++
|
|
|
|
struct HostS { ... };
|
|
struct DeviceS { ... };
|
|
|
|
#ifdef __NVCC__
|
|
#ifndef __CUDA_ARCH__
|
|
__host__ HostS MakeStruct() { return HostS(); }
|
|
#else
|
|
__device__ DeviceS MakeStruct() { return DeviceS(); }
|
|
#endif
|
|
#else
|
|
__host__ HostS MakeStruct() { return HostS(); }
|
|
__device__ DeviceS MakeStruct() { return DeviceS(); }
|
|
#endif
|
|
|
|
// Now host and device code can call MakeStruct().
|
|
|
|
Hopefully you don't have to do this sort of thing often.
|
|
|
|
Optimizations
|
|
=============
|
|
|
|
Modern CPUs and GPUs are architecturally quite different, so code that's fast
|
|
on a CPU isn't necessarily fast on a GPU. We've made a number of changes to
|
|
LLVM to make it generate good GPU code. Among these changes are:
|
|
|
|
* `Straight-line scalar optimizations <https://goo.gl/4Rb9As>`_ -- These
|
|
reduce redundancy within straight-line code.
|
|
|
|
* `Aggressive speculative execution
|
|
<https://llvm.org/docs/doxygen/html/SpeculativeExecution_8cpp_source.html>`_
|
|
-- This is mainly for promoting straight-line scalar optimizations, which are
|
|
most effective on code along dominator paths.
|
|
|
|
* `Memory space inference
|
|
<https://llvm.org/doxygen/NVPTXInferAddressSpaces_8cpp_source.html>`_ --
|
|
In PTX, we can operate on pointers that are in a particular "address space"
|
|
(global, shared, constant, or local), or we can operate on pointers in the
|
|
"generic" address space, which can point to anything. Operations in a
|
|
non-generic address space are faster, but pointers in CUDA are not explicitly
|
|
annotated with their address space, so it's up to LLVM to infer it where
|
|
possible.
|
|
|
|
* `Bypassing 64-bit divides
|
|
<https://llvm.org/docs/doxygen/html/BypassSlowDivision_8cpp_source.html>`_ --
|
|
This was an existing optimization that we enabled for the PTX backend.
|
|
|
|
64-bit integer divides are much slower than 32-bit ones on NVIDIA GPUs.
|
|
Many of the 64-bit divides in our benchmarks have a divisor and dividend
|
|
which fit in 32-bits at runtime. This optimization provides a fast path for
|
|
this common case.
|
|
|
|
* Aggressive loop unrolling and function inlining -- Loop unrolling and
|
|
function inlining need to be more aggressive for GPUs than for CPUs because
|
|
control flow transfer in GPU is more expensive. More aggressive unrolling and
|
|
inlining also promote other optimizations, such as constant propagation and
|
|
SROA, which sometimes speed up code by over 10x.
|
|
|
|
(Programmers can force unrolling and inline using clang's `loop unrolling pragmas
|
|
<https://clang.llvm.org/docs/AttributeReference.html#pragma-unroll-pragma-nounroll>`_
|
|
and ``__attribute__((always_inline))``.)
|
|
|
|
Publication
|
|
===========
|
|
|
|
The team at Google published a paper in CGO 2016 detailing the optimizations
|
|
they'd made to clang/LLVM. Note that "gpucc" is no longer a meaningful name:
|
|
The relevant tools are now just vanilla clang/LLVM.
|
|
|
|
| `gpucc: An Open-Source GPGPU Compiler <http://dl.acm.org/citation.cfm?id=2854041>`_
|
|
| Jingyue Wu, Artem Belevich, Eli Bendersky, Mark Heffernan, Chris Leary, Jacques Pienaar, Bjarke Roune, Rob Springer, Xuetian Weng, Robert Hundt
|
|
| *Proceedings of the 2016 International Symposium on Code Generation and Optimization (CGO 2016)*
|
|
|
|
|
| `Slides from the CGO talk <http://wujingyue.github.io/docs/gpucc-talk.pdf>`_
|
|
|
|
|
| `Tutorial given at CGO <http://wujingyue.github.io/docs/gpucc-tutorial.pdf>`_
|
|
|
|
Obtaining Help
|
|
==============
|
|
|
|
To obtain help on LLVM in general and its CUDA support, see `the LLVM
|
|
community <https://llvm.org/docs/#mailing-lists>`_.
|