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[doc] [CUDA] Add sections about STL support and differences between nvcc and clang to CompileCudaWithLLVM.rst.

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git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@281573 91177308-0d34-0410-b5e6-96231b3b80d8
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Justin Lebar 2016-09-15 02:04:32 +00:00
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docs/CompileCudaWithLLVM.rst
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@ -126,6 +126,63 @@ Flags you may wish to tweak include:
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-mirror/test-suite/blob/master/External/CUDA/math_h.cu>`_
`tests
<https://github.com/llvm-mirror/test-suite/blob/master/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.
clang does not yet support ``std::complex``. Because we interpret the
"wrong-side rule" more strictly than nvcc, ``std::complex`` doesn't work in
``__device__`` or ``__host__ __device__`` code.
In the meantime, you can get limited ``std::complex`` support in clang by
building your code for C++14. In clang, all ``constexpr`` functions are always
implicitly ``__host__ __device__`` (this corresponds to nvcc's
``--relaxed-constexpr`` flag). In C++14, many ``std::complex`` functions are
``constexpr``, so you can use these with clang. (nvcc does not currently
support C++14.)
Detecting clang vs NVCC from code
=================================
@ -145,16 +202,293 @@ compilation, in host and device modes:
.. code-block:: c++
#if defined(__clang__) && defined(__CUDA__) && !defined(__CUDA_ARCH__)
// clang compiling CUDA code, host mode.
// clang compiling CUDA code, host mode.
#endif
#if defined(__clang__) && defined(__CUDA__) && defined(__CUDA_ARCH__)
// clang compiling CUDA code, device mode.
// 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
<http://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
=============