llvm-project/llvm/docs/CompileCudaWithLLVM.rst

=========================
Compiling CUDA with clang
=========================

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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 in llvm 3.9, but it's still in active development, so we
recommend you `compile clang/LLVM from HEAD
<http://llvm.org/docs/GettingStarted.html>`_.

Before you build CUDA code, you'll need to have installed the appropriate
driver for your nvidia GPU and 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 `does not support
<https://llvm.org/bugs/show_bug.cgi?id=26966>`_ the CUDA toolkit as installed
by many Linux package managers; you probably need to install nvidia's package.

You will need CUDA 7.0 or 7.5 to compile with clang.  CUDA 8 support is in the
works.

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

* ``<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.)

* ``<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``, ``/usr/local/cuda-7.0``, or
``/usr/local/cuda-7.5``.

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``.

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__``.

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
  <http://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
  <http://llvm.org/doxygen/NVPTXInferAddressSpaces_8cpp_source.html>`_ --
  In PTX, we can operate on pointers that are in a paricular "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
  <http://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 unrooling 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
  <http://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.com/docs/gpucc-talk.pdf>`_
|
| `Tutorial given at CGO <http://wujingyue.com/docs/gpucc-tutorial.pdf>`_

Obtaining Help
==============

To obtain help on LLVM in general and its CUDA support, see `the LLVM
community <http://llvm.org/docs/#mailing-lists>`_.