562 lines
21 KiB
ReStructuredText
562 lines
21 KiB
ReStructuredText
=========================
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Compiling CUDA with clang
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=========================
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.. contents::
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:local:
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Introduction
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============
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This document describes how to compile CUDA code with clang, and gives some
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details about LLVM and clang's CUDA implementations.
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This document assumes a basic familiarity with CUDA. Information about CUDA
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programming can be found in the
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`CUDA programming guide
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<http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html>`_.
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Compiling CUDA Code
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===================
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Prerequisites
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-------------
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CUDA is supported since llvm 3.9. Clang currently supports CUDA 7.0 through
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10.1. If clang detects a newer CUDA version, it will issue a warning and will
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attempt to use detected CUDA SDK it as if it were CUDA-10.1.
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Before you build CUDA code, you'll need to have installed the CUDA SDK. See
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`NVIDIA's CUDA installation guide
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<https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>`_ for
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details. Note that clang `maynot support
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<https://bugs.llvm.org/show_bug.cgi?id=26966>`_ the CUDA toolkit as installed by
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some Linux package managers. Clang does attempt to deal with specific details of
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CUDA installation on a handful of common Linux distributions, but in general the
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most reliable way to make it work is to install CUDA in a single directory from
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NVIDIA's `.run` package and specify its location via `--cuda-path=...` argument.
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CUDA compilation is supported on Linux. Compilation on MacOS and Windows may or
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may not work and currently have no maintainers.
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Invoking clang
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--------------
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Invoking clang for CUDA compilation works similarly to compiling regular C++.
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You just need to be aware of a few additional flags.
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You can use `this <https://gist.github.com/855e277884eb6b388cd2f00d956c2fd4>`_
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program as a toy example. Save it as ``axpy.cu``. (Clang detects that you're
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compiling CUDA code by noticing that your filename ends with ``.cu``.
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Alternatively, you can pass ``-x cuda``.)
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To build and run, run the following commands, filling in the parts in angle
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brackets as described below:
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.. code-block:: console
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$ clang++ axpy.cu -o axpy --cuda-gpu-arch=<GPU arch> \
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-L<CUDA install path>/<lib64 or lib> \
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-lcudart_static -ldl -lrt -pthread
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$ ./axpy
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y[0] = 2
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y[1] = 4
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y[2] = 6
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y[3] = 8
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On MacOS, replace `-lcudart_static` with `-lcudart`; otherwise, you may get
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"CUDA driver version is insufficient for CUDA runtime version" errors when you
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run your program.
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* ``<CUDA install path>`` -- the directory where you installed CUDA SDK.
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Typically, ``/usr/local/cuda``.
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Pass e.g. ``-L/usr/local/cuda/lib64`` if compiling in 64-bit mode; otherwise,
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pass e.g. ``-L/usr/local/cuda/lib``. (In CUDA, the device code and host code
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always have the same pointer widths, so if you're compiling 64-bit code for
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the host, you're also compiling 64-bit code for the device.) Note that as of
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v10.0 CUDA SDK `no longer supports compilation of 32-bit
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applications <https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#deprecated-features>`_.
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* ``<GPU arch>`` -- the `compute capability
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<https://developer.nvidia.com/cuda-gpus>`_ of your GPU. For example, if you
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want to run your program on a GPU with compute capability of 3.5, specify
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``--cuda-gpu-arch=sm_35``.
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Note: You cannot pass ``compute_XX`` as an argument to ``--cuda-gpu-arch``;
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only ``sm_XX`` is currently supported. However, clang always includes PTX in
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its binaries, so e.g. a binary compiled with ``--cuda-gpu-arch=sm_30`` would be
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forwards-compatible with e.g. ``sm_35`` GPUs.
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You can pass ``--cuda-gpu-arch`` multiple times to compile for multiple archs.
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The `-L` and `-l` flags only need to be passed when linking. When compiling,
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you may also need to pass ``--cuda-path=/path/to/cuda`` if you didn't install
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the CUDA SDK into ``/usr/local/cuda`` or ``/usr/local/cuda-X.Y``.
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Flags that control numerical code
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---------------------------------
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If you're using GPUs, you probably care about making numerical code run fast.
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GPU hardware allows for more control over numerical operations than most CPUs,
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but this results in more compiler options for you to juggle.
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Flags you may wish to tweak include:
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* ``-ffp-contract={on,off,fast}`` (defaults to ``fast`` on host and device when
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compiling CUDA) Controls whether the compiler emits fused multiply-add
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operations.
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* ``off``: never emit fma operations, and prevent ptxas from fusing multiply
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and add instructions.
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* ``on``: fuse multiplies and adds within a single statement, but never
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across statements (C11 semantics). Prevent ptxas from fusing other
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multiplies and adds.
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* ``fast``: fuse multiplies and adds wherever profitable, even across
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statements. Doesn't prevent ptxas from fusing additional multiplies and
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adds.
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Fused multiply-add instructions can be much faster than the unfused
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equivalents, but because the intermediate result in an fma is not rounded,
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this flag can affect numerical code.
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* ``-fcuda-flush-denormals-to-zero`` (default: off) When this is enabled,
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floating point operations may flush `denormal
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<https://en.wikipedia.org/wiki/Denormal_number>`_ inputs and/or outputs to 0.
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Operations on denormal numbers are often much slower than the same operations
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on normal numbers.
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* ``-fcuda-approx-transcendentals`` (default: off) When this is enabled, the
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compiler may emit calls to faster, approximate versions of transcendental
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functions, instead of using the slower, fully IEEE-compliant versions. For
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example, this flag allows clang to emit the ptx ``sin.approx.f32``
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instruction.
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This is implied by ``-ffast-math``.
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Standard library support
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========================
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In clang and nvcc, most of the C++ standard library is not supported on the
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device side.
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``<math.h>`` and ``<cmath>``
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----------------------------
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In clang, ``math.h`` and ``cmath`` are available and `pass
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<https://github.com/llvm/llvm-test-suite/blob/master/External/CUDA/math_h.cu>`_
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`tests
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<https://github.com/llvm/llvm-test-suite/blob/master/External/CUDA/cmath.cu>`_
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adapted from libc++'s test suite.
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In nvcc ``math.h`` and ``cmath`` are mostly available. Versions of ``::foof``
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in namespace std (e.g. ``std::sinf``) are not available, and where the standard
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calls for overloads that take integral arguments, these are usually not
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available.
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.. code-block:: c++
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#include <math.h>
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#include <cmath.h>
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// clang is OK with everything in this function.
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__device__ void test() {
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std::sin(0.); // nvcc - ok
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std::sin(0); // nvcc - error, because no std::sin(int) override is available.
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sin(0); // nvcc - same as above.
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sinf(0.); // nvcc - ok
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std::sinf(0.); // nvcc - no such function
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}
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``<std::complex>``
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------------------
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nvcc does not officially support ``std::complex``. It's an error to use
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``std::complex`` in ``__device__`` code, but it often works in ``__host__
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__device__`` code due to nvcc's interpretation of the "wrong-side rule" (see
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below). However, we have heard from implementers that it's possible to get
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into situations where nvcc will omit a call to an ``std::complex`` function,
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especially when compiling without optimizations.
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As of 2016-11-16, clang supports ``std::complex`` without these caveats. It is
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tested with libstdc++ 4.8.5 and newer, but is known to work only with libc++
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newer than 2016-11-16.
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``<algorithm>``
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---------------
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In C++14, many useful functions from ``<algorithm>`` (notably, ``std::min`` and
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``std::max``) become constexpr. You can therefore use these in device code,
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when compiling with clang.
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Detecting clang vs NVCC from code
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=================================
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Although clang's CUDA implementation is largely compatible with NVCC's, you may
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still want to detect when you're compiling CUDA code specifically with clang.
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This is tricky, because NVCC may invoke clang as part of its own compilation
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process! For example, NVCC uses the host compiler's preprocessor when
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compiling for device code, and that host compiler may in fact be clang.
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When clang is actually compiling CUDA code -- rather than being used as a
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subtool of NVCC's -- it defines the ``__CUDA__`` macro. ``__CUDA_ARCH__`` is
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defined only in device mode (but will be defined if NVCC is using clang as a
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preprocessor). So you can use the following incantations to detect clang CUDA
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compilation, in host and device modes:
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.. code-block:: c++
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#if defined(__clang__) && defined(__CUDA__) && !defined(__CUDA_ARCH__)
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// clang compiling CUDA code, host mode.
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#endif
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#if defined(__clang__) && defined(__CUDA__) && defined(__CUDA_ARCH__)
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// clang compiling CUDA code, device mode.
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#endif
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Both clang and nvcc define ``__CUDACC__`` during CUDA compilation. You can
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detect NVCC specifically by looking for ``__NVCC__``.
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Dialect Differences Between clang and nvcc
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==========================================
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There is no formal CUDA spec, and clang and nvcc speak slightly different
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dialects of the language. Below, we describe some of the differences.
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This section is painful; hopefully you can skip this section and live your life
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blissfully unaware.
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Compilation Models
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------------------
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Most of the differences between clang and nvcc stem from the different
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compilation models used by clang and nvcc. nvcc uses *split compilation*,
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which works roughly as follows:
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* Run a preprocessor over the input ``.cu`` file to split it into two source
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files: ``H``, containing source code for the host, and ``D``, containing
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source code for the device.
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* For each GPU architecture ``arch`` that we're compiling for, do:
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* Compile ``D`` using nvcc proper. The result of this is a ``ptx`` file for
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``P_arch``.
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* Optionally, invoke ``ptxas``, the PTX assembler, to generate a file,
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``S_arch``, containing GPU machine code (SASS) for ``arch``.
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* Invoke ``fatbin`` to combine all ``P_arch`` and ``S_arch`` files into a
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single "fat binary" file, ``F``.
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* Compile ``H`` using an external host compiler (gcc, clang, or whatever you
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like). ``F`` is packaged up into a header file which is force-included into
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``H``; nvcc generates code that calls into this header to e.g. launch
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kernels.
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clang uses *merged parsing*. This is similar to split compilation, except all
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of the host and device code is present and must be semantically-correct in both
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compilation steps.
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* For each GPU architecture ``arch`` that we're compiling for, do:
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* Compile the input ``.cu`` file for device, using clang. ``__host__`` code
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is parsed and must be semantically correct, even though we're not
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generating code for the host at this time.
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The output of this step is a ``ptx`` file ``P_arch``.
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* Invoke ``ptxas`` to generate a SASS file, ``S_arch``. Note that, unlike
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nvcc, clang always generates SASS code.
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* Invoke ``fatbin`` to combine all ``P_arch`` and ``S_arch`` files into a
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single fat binary file, ``F``.
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* Compile ``H`` using clang. ``__device__`` code is parsed and must be
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semantically correct, even though we're not generating code for the device
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at this time.
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``F`` is passed to this compilation, and clang includes it in a special ELF
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section, where it can be found by tools like ``cuobjdump``.
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(You may ask at this point, why does clang need to parse the input file
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multiple times? Why not parse it just once, and then use the AST to generate
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code for the host and each device architecture?
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Unfortunately this can't work because we have to define different macros during
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host compilation and during device compilation for each GPU architecture.)
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clang's approach allows it to be highly robust to C++ edge cases, as it doesn't
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need to decide at an early stage which declarations to keep and which to throw
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away. But it has some consequences you should be aware of.
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Overloading Based on ``__host__`` and ``__device__`` Attributes
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---------------------------------------------------------------
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Let "H", "D", and "HD" stand for "``__host__`` functions", "``__device__``
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functions", and "``__host__ __device__`` functions", respectively. Functions
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with no attributes behave the same as H.
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nvcc does not allow you to create H and D functions with the same signature:
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.. code-block:: c++
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// nvcc: error - function "foo" has already been defined
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__host__ void foo() {}
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__device__ void foo() {}
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However, nvcc allows you to "overload" H and D functions with different
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signatures:
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.. code-block:: c++
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// nvcc: no error
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__host__ void foo(int) {}
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__device__ void foo() {}
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In clang, the ``__host__`` and ``__device__`` attributes are part of a
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function's signature, and so it's legal to have H and D functions with
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(otherwise) the same signature:
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.. code-block:: c++
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// clang: no error
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__host__ void foo() {}
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__device__ void foo() {}
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HD functions cannot be overloaded by H or D functions with the same signature:
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.. code-block:: c++
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// nvcc: error - function "foo" has already been defined
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// clang: error - redefinition of 'foo'
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__host__ __device__ void foo() {}
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__device__ void foo() {}
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// nvcc: no error
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// clang: no error
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__host__ __device__ void bar(int) {}
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__device__ void bar() {}
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When resolving an overloaded function, clang considers the host/device
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attributes of the caller and callee. These are used as a tiebreaker during
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overload resolution. See `IdentifyCUDAPreference
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<https://clang.llvm.org/doxygen/SemaCUDA_8cpp.html>`_ for the full set of rules,
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but at a high level they are:
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* D functions prefer to call other Ds. HDs are given lower priority.
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* Similarly, H functions prefer to call other Hs, or ``__global__`` functions
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(with equal priority). HDs are given lower priority.
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* HD functions prefer to call other HDs.
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When compiling for device, HDs will call Ds with lower priority than HD, and
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will call Hs with still lower priority. If it's forced to call an H, the
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program is malformed if we emit code for this HD function. We call this the
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"wrong-side rule", see example below.
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The rules are symmetrical when compiling for host.
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Some examples:
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.. code-block:: c++
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__host__ void foo();
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__device__ void foo();
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__host__ void bar();
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__host__ __device__ void bar();
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__host__ void test_host() {
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foo(); // calls H overload
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bar(); // calls H overload
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}
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__device__ void test_device() {
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foo(); // calls D overload
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bar(); // calls HD overload
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}
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__host__ __device__ void test_hd() {
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foo(); // calls H overload when compiling for host, otherwise D overload
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bar(); // always calls HD overload
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}
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Wrong-side rule example:
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.. code-block:: c++
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__host__ void host_only();
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// We don't codegen inline functions unless they're referenced by a
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// non-inline function. inline_hd1() is called only from the host side, so
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// does not generate an error. inline_hd2() is called from the device side,
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// so it generates an error.
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inline __host__ __device__ void inline_hd1() { host_only(); } // no error
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inline __host__ __device__ void inline_hd2() { host_only(); } // error
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__host__ void host_fn() { inline_hd1(); }
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__device__ void device_fn() { inline_hd2(); }
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// This function is not inline, so it's always codegen'ed on both the host
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// and the device. Therefore, it generates an error.
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__host__ __device__ void not_inline_hd() { host_only(); }
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For the purposes of the wrong-side rule, templated functions also behave like
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``inline`` functions: They aren't codegen'ed unless they're instantiated
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(usually as part of the process of invoking them).
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clang's behavior with respect to the wrong-side rule matches nvcc's, except
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nvcc only emits a warning for ``not_inline_hd``; device code is allowed to call
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``not_inline_hd``. In its generated code, nvcc may omit ``not_inline_hd``'s
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call to ``host_only`` entirely, or it may try to generate code for
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``host_only`` on the device. What you get seems to depend on whether or not
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the compiler chooses to inline ``host_only``.
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Member functions, including constructors, may be overloaded using H and D
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attributes. However, destructors cannot be overloaded.
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Using a Different Class on Host/Device
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--------------------------------------
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Occasionally you may want to have a class with different host/device versions.
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If all of the class's members are the same on the host and device, you can just
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provide overloads for the class's member functions.
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However, if you want your class to have different members on host/device, you
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won't be able to provide working H and D overloads in both classes. In this
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case, clang is likely to be unhappy with you.
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.. code-block:: c++
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#ifdef __CUDA_ARCH__
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struct S {
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__device__ void foo() { /* use device_only */ }
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int device_only;
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};
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#else
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struct S {
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__host__ void foo() { /* use host_only */ }
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double host_only;
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};
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__device__ void test() {
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S s;
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// clang generates an error here, because during host compilation, we
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// have ifdef'ed away the __device__ overload of S::foo(). The __device__
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// overload must be present *even during host compilation*.
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S.foo();
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}
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#endif
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We posit that you don't really want to have classes with different members on H
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and D. For example, if you were to pass one of these as a parameter to a
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kernel, it would have a different layout on H and D, so would not work
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properly.
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To make code like this compatible with clang, we recommend you separate it out
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into two classes. If you need to write code that works on both host and
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device, consider writing an overloaded wrapper function that returns different
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types on host and device.
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.. code-block:: c++
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struct HostS { ... };
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struct DeviceS { ... };
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__host__ HostS MakeStruct() { return HostS(); }
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__device__ DeviceS MakeStruct() { return DeviceS(); }
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// Now host and device code can call MakeStruct().
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Unfortunately, this idiom isn't compatible with nvcc, because it doesn't allow
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you to overload based on the H/D attributes. Here's an idiom that works with
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both clang and nvcc:
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.. code-block:: c++
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struct HostS { ... };
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struct DeviceS { ... };
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#ifdef __NVCC__
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#ifndef __CUDA_ARCH__
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__host__ HostS MakeStruct() { return HostS(); }
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|
#else
|
|
__device__ DeviceS MakeStruct() { return DeviceS(); }
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|
#endif
|
|
#else
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|
__host__ HostS MakeStruct() { return HostS(); }
|
|
__device__ DeviceS MakeStruct() { return DeviceS(); }
|
|
#endif
|
|
|
|
// Now host and device code can call MakeStruct().
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|
|
|
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>`_.
|