Heterogeneous target attributes overloading in Clang CUDA (__CUDA_ARCH__ considered harmful)

Bryce_Lelbach2 · October 27, 2018, 1:04am

Today, CUDA C++ has a macro that can be used to distinguish which architecture
(either the host architecture, a specific device architecture, or any device
architecture) code is currently being compiled for.

When CUDA code is compiled for the host, CUDA_ARCH is not defined. When it
is compiled for the device, it is defined to a value that indicates the SM architecture.

At face value, this seems like a useful way to customize how heterogeneous code
is implemented on a particular architecture:

host device
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
#if CUDA_ARCH >= 200
asm (“vadd.u32.u32.u32.add %0, %1, %2, %3;” : “=r”(x) : “r”(x), “r”(y), “r”(z));
#else
x = x + y + z;
#endif
return x;
}

However, CUDA_ARCH is only well suited to a split compilation CUDA compiler,
like NVCC, which uses a separate host compiler (GCC, Clang, MSVC, etc) and device
compiler, preprocessing and compiling your code once for each target architecture
(once for the host, and one time for each target device architecture).

CUDA_ARCH has some caveats, however. The NVCC compiler has to see all kernel
function declarations (e.g. global functions) during both host and device
compilation, to generate the host side launch stubs and the actual device side
kernel code. Otherwise, NVCC may not compile the device side kernel code, either
because it believes it is unused or because it is never instantiated (in the case
of a template kernel function). This, regretably, will not fail at compile time,
but instead fails at runtime when you attempt to launch the (non-existant) kernel.

Consider the following code. It unconditionally calls parallel::reduce_n_impl
on the host, which instantiates some (unseen) template kernel functions during
host compilation. However, in device code, if THRUST_HAS_CUDART is false,
parallel::reduce_n_impl is never instantiated and the actual device code for
the kernel functions are never compiled.

#if !defined(CUDA_ARCH) || (CUDA_ARCH>= 350 && defined(CUDACC_RDC))
// We’re either not compiling as device code, or we are compiling as device
// code and we can launch kernels from device code (SM 3.5 and higher +
// relocatable device code is required for the device side runtime which is
// needed to do device side launches).

define THRUST_HAS_CUDART 1

#else

define THRUST_HAS_CUDART 0

#endif

namespace thrust {

#pragma nv_exec_check_disable
template <typename Derived,
typename InputIt,
typename Size,
typename T,
typename BinaryOp>
host device
T reduce_n(execution_policy& policy,
InputIt first,
Size num_items,
T init,
BinaryOp binary_op)
{
// Broken version:
#if THRUST_HAS_CUDART
return system::cuda::reduce_n_impl(policy, first, num_items, init, binary_op);
#else
// We are running on the device and there is no device side runtime, so we
// can’t launch a kernel to do the reduction in parallel. Instead, we just
// do a sequential reduction in the calling thread.
return system::sequential::reduce_n_impl(first, num_items, init, binary_op);
#endif
}

} // namespace thrust

Instead, we end up using the rather odd pattern of adding a (non-constexpr) if
statement whose condition is known at compile time. This ensures the kernel function
is instantiated during device compilation, even though it is not actually used.
Fortunately, while NVCC can as-if optimize away the if statement, it cannot treat
the instantiation as unused.

#pragma nv_exec_check_disable
template <typename Derived,
typename InputIt,
typename Size,
typename T,
typename BinaryOp>
host device
T reduce_n(execution_policy& policy,
InputIt first,
Size num_items,
T init,
BinaryOp binary_op)
{
if (THRUST_HAS_CUDART)
return parallel::reduce_n_impl(policy, first, num_items, init, binary_op);

#if !THRUST_HAS_CUDART
// We are running on the device and there is no device side runtime, so we
// can’t launch a kernel to do the reduction in parallel. Instead, we just
// do a sequential reduction in the calling thread.
return sequential::reduce_n_impl(first, num_items, init, binary_op);
#endif
}

For more background, see:

https://github.com/NVlabs/cub/issues/30

For a merged parse CUDA compiler, like Clang CUDA, CUDA_ARCH is a poor fit,
because as a textual macro it can be used to completely change the code that
the compiler consumes during host and device compilation, essentially forcing
separate preprocessing and parsing.

Clang CUDA offers one alternative today, host / device overloading,
which is better suited to a merged parse model:

device
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
asm (“vadd.u32.u32.u32.add %0, %1, %2, %3;” : “=r”(x) : “r”(x), “r”(y), “r”(z));
return x;
}

host
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
return x + y + z;
}

However, this approach does not allow us to customize code for specific device
architectures. Note that the above code will not compile on SM 1.0 devices, as
the inline assembly contains instructions unavailable on those platforms.

Tuning for specific device architectures is critical for high performance CUDA
libraries, like Thrust. We need to be able to select different algorithms and
use architecture specific facilities to get speed of light performance.

Fortunately, there is some useful prior art. Clang (and GCC) has a related feature,
attribute((target(“…”))), which can be used to define a function “overloaded”
on the architecture it is compiled for. One common use case for this feature is
implementing functions that utilize micro-architecture specific CPU SIMD
instructions:

using double4 = double attribute((vector_size(32)));

attribute((target(“sse”)))
double4 fma(double4d x, double4 y, double4 z);

attribute((target(“avx”)))
double4 fma(double4d x, double4 y, double4 z);

attribute((target(“default”)))
double4 fma(double4d x, double4 y, double4 z); // “Fallback” implementation.

This attribute can also be used to target specific architectures:

attribute((target(“arch=atom”)))
void foo(); // Will be called on ‘atom’ processors.

attribute((target(“default”)))
void foo(); // Will be called on any other processors.

This could easily be extended for heterogeneous compilation:

attribute((target(“host:arch=skylake”)))
void foo();

attribute((target(“arch=atom”)))
void foo(); // Implicitly “host:arch=atom”.

attribute((target(“host:default”)))
void foo();

attribute((target(“device:arch=sm_20”)))
void foo();

attribute((target(“device:arch=sm_60”)))
void foo();

attribute((target(“device:default”)))
void foo();

Or, perhaps more concisely, we could introduce this shorthand:

host(“arch=skylake”)
void foo();

host
void foo(); // Implicitly “host:default”.

device(“arch=sm_20”)
void foo();

device(“arch=sm_60”)
void foo();

device // Implicitly “device:default”.
void foo();

Another place that we use CUDA_ARCH_ today in Thrust and CUB is in
metaprogramming code that selects the correct “strategies” that should be
used to implement a particular algorithm:

enum arch {
host,
sm_30, sm_32, sm_35, // Kepler
sm_50, sm_52, sm_53, // Maxwell
sm_60, sm_61, sm_62, // Pascal
sm_70, // Volta
sm_72, sm_75 // Turing
};

host device
constexpr arch select_arch()
{
switch (CUDA_ARCH)
{
// …
};
}

template <class T, arch Arch = select_arch()>
struct radix_sort_tuning;

template
struct radix_sort_tuning<T, sm_35>
{
constexpr size_t INPUT_SIZE = sizeof(T);

constexpr size_t NOMINAL_4B_ITEMS_PER_THREAD = 11;
constexpr size_t ITEMS_PER_THREAD
= std::min(NOMIMAL_4B_ITEMS_PER_THREAD,
std::max(1, (NOMIMAL_4B_ITEMS_PER_THREAD * 4 / INPUT_SIZE)));

constexpr size_t BLOCK_THREADS = 256;
constexpr auto BLOCK_LOAD_STRATEGY = BLOCK_LOAD_WARP_TRANSPOSE;
constexpr auto CACHE_LOAD_STRATEGY = LOAD_LDG;
constexpr auto BLOCK_STORE_STRATEGY = BLOCK_STORE_WARP_TRANSPOSE;
};

template
struct radix_sort_tuning<T, sm_50> { /* … */ };

// …

With heterogeneous target attributes, we could implement select_arch like
so:

host
constexpr arch select_arch() { return host; }

device(“arch=sm_30”)
constexpr arch select_arch() { return sm_30; }

device(“arch=sm_35”)
constexpr arch select_arch() { return sm_35; }

// …

You could also potentially use this with if constexpr:

void foo()
{
// Moral equivalent of #if CUDA_ARCH
if constexpr (host != select_arch())
// …
else
// …
}

This feature would also make it much easier to port some of the more tricky parts
of libc++ to GPUs, such as iostreams and concurrency primitives.

It would be awesome if we could take host / device overloading a step
further and make it a full fledged replacement for CUDA_ARCH. It would provide
a possible future migration path away from CUDA_ARCH, which would enable us to
move to true merged parsing for heterogeneous C++: preprocess once, parse once,
perform platform-agnostic optimizations once, code gen multiple times.

So, questions:

Can target attributes go on constexpr functions today?
Does anyone have suggestions for how this approach could be improved? Alternatives?
Is there interest in this in Clang CUDA?

Artem-B · November 7, 2018, 7:47pm

Hi, Bryce,

Today, CUDA C++ has a macro that can be used to distinguish which architecture
(either the host architecture, a specific device architecture, or any device
architecture) code is currently being compiled for.

When CUDA code is compiled for the host, CUDA_ARCH is not defined. When it
is compiled for the device, it is defined to a value that indicates the SM architecture.

At face value, this seems like a useful way to customize how heterogeneous code
is implemented on a particular architecture:

host device
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
#if CUDA_ARCH >= 200
asm (“vadd.u32.u32.u32.add %0, %1, %2, %3;” : “=r”(x) : “r”(x), “r”(y), “r”(z));
#else
x = x + y + z;
#endif
return x;
}

However, CUDA_ARCH is only well suited to a split compilation CUDA compiler,
like NVCC, which uses a separate host compiler (GCC, Clang, MSVC, etc) and device
compiler, preprocessing and compiling your code once for each target architecture
(once for the host, and one time for each target device architecture).

CUDA_ARCH has some caveats, however. The NVCC compiler has to see all kernel
function declarations (e.g. global functions) during both host and device
compilation, to generate the host side launch stubs and the actual device side
kernel code. Otherwise, NVCC may not compile the device side kernel code, either
because it believes it is unused or because it is never instantiated (in the case
of a template kernel function). This, regretably, will not fail at compile time,
but instead fails at runtime when you attempt to launch the (non-existant) kernel.

Consider the following code. It unconditionally calls parallel::reduce_n_impl
on the host, which instantiates some (unseen) template kernel functions during
host compilation. However, in device code, if THRUST_HAS_CUDART is false,
parallel::reduce_n_impl is never instantiated and the actual device code for
the kernel functions are never compiled.

#if !defined(CUDA_ARCH) || (CUDA_ARCH>= 350 && defined(CUDACC_RDC))
// We’re either not compiling as device code, or we are compiling as device
// code and we can launch kernels from device code (SM 3.5 and higher +
// relocatable device code is required for the device side runtime which is
// needed to do device side launches).

define THRUST_HAS_CUDART 1

#else

define THRUST_HAS_CUDART 0

#endif

namespace thrust {

#pragma nv_exec_check_disable
template <typename Derived,
typename InputIt,
typename Size,
typename T,
typename BinaryOp>
host device
T reduce_n(execution_policy& policy,
InputIt first,
Size num_items,
T init,
BinaryOp binary_op)
{
// Broken version:
#if THRUST_HAS_CUDART
return system::cuda::reduce_n_impl(policy, first, num_items, init, binary_op);
#else
// We are running on the device and there is no device side runtime, so we
// can’t launch a kernel to do the reduction in parallel. Instead, we just
// do a sequential reduction in the calling thread.
return system::sequential::reduce_n_impl(first, num_items, init, binary_op);
#endif
}

} // namespace thrust

CUDA programming guide says “don’t do it”:

https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-arch-macro

“If a function template is instantiated and launched from the host, then the function template must be instantiated with the same template arguments irrespective of whether is defined and regardless of the value of .”

So, yes, you shouldn’t do it with nvcc.

Instead, we end up using the rather odd pattern of adding a (non-constexpr) if
statement whose condition is known at compile time. This ensures the kernel function
is instantiated during device compilation, even though it is not actually used.
Fortunately, while NVCC can as-if optimize away the if statement, it cannot treat
the instantiation as unused.

#pragma nv_exec_check_disable
template <typename Derived,
typename InputIt,
typename Size,
typename T,
typename BinaryOp>
host device
T reduce_n(execution_policy& policy,
InputIt first,
Size num_items,
T init,
BinaryOp binary_op)
{
if (THRUST_HAS_CUDART)
return parallel::reduce_n_impl(policy, first, num_items, init, binary_op);

#if !THRUST_HAS_CUDART
// We are running on the device and there is no device side runtime, so we
// can’t launch a kernel to do the reduction in parallel. Instead, we just
// do a sequential reduction in the calling thread.
return sequential::reduce_n_impl(first, num_items, init, binary_op);
#endif
}

For more background, see:

https://github.com/NVlabs/cub/issues/30
https://stackoverflow.com/questions/51248770/cuda-arch-flag-with-thrust-execution-policy

For a merged parse CUDA compiler, like Clang CUDA, CUDA_ARCH is a poor fit,
because as a textual macro it can be used to completely change the code that
the compiler consumes during host and device compilation, essentially forcing
separate preprocessing and parsing.

Yes. One of the ideas I had was to abandon it altogether, parse the source once and then codegen host and device IR from the same AST. Alas, the goal was to compile the existing CUDA code, so we has to keep CUDA_ARCH working and because of that we also have to parse the sources for each host and device compilation.

If CUDA_ARCH is gone, it would make it possible to do more neat things.

Clang CUDA offers one alternative today, host / device overloading,
which is better suited to a merged parse model:

device
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
asm (“vadd.u32.u32.u32.add %0, %1, %2, %3;” : “=r”(x) : “r”(x), “r”(y), “r”(z));
return x;
}

host
uint32_t iadd3(uint32_t x, uint32_t y, uint32_t z) {
return x + y + z;
}

However, this approach does not allow us to customize code for specific device
architectures. Note that the above code will not compile on SM 1.0 devices, as
the inline assembly contains instructions unavailable on those platforms.

Correct. But now that we have ability for the source for two different targets (host&device) to co-exist in principle, it should be relatively easy to extend it to (host + N devices).

Tuning for specific device architectures is critical for high performance CUDA
libraries, like Thrust. We need to be able to select different algorithms and
use architecture specific facilities to get speed of light performance.

Fortunately, there is some useful prior art. Clang (and GCC) has a related feature,
attribute((target(“…”))), which can be used to define a function “overloaded”
on the architecture it is compiled for. One common use case for this feature is
implementing functions that utilize micro-architecture specific CPU SIMD
instructions:

using double4 = double attribute((vector_size(32)));

attribute((target(“sse”)))
double4 fma(double4d x, double4 y, double4 z);

attribute((target(“avx”)))
double4 fma(double4d x, double4 y, double4 z);

attribute((target(“default”)))
double4 fma(double4d x, double4 y, double4 z); // “Fallback” implementation.

This attribute can also be used to target specific architectures:

attribute((target(“arch=atom”)))
void foo(); // Will be called on ‘atom’ processors.

attribute((target(“default”)))
void foo(); // Will be called on any other processors.

This could easily be extended for heterogeneous compilation:

attribute((target(“host:arch=skylake”)))
void foo();

attribute((target(“arch=atom”)))
void foo(); // Implicitly “host:arch=atom”.

attribute((target(“host:default”)))
void foo();

attribute((target(“device:arch=sm_20”)))
void foo();

attribute((target(“device:arch=sm_60”)))
void foo();

attribute((target(“device:default”)))
void foo();

Or, perhaps more concisely, we could introduce this shorthand:

host(“arch=skylake”)
void foo();

host
void foo(); // Implicitly “host:default”.

device(“arch=sm_20”)
void foo();

device(“arch=sm_60”)
void foo();

device // Implicitly “device:default”.
void foo();

This could be one way to do it, though attribute((target)) has existing semantics that does not quite match the use case you’re proposing. I.e. currently the intent is to generate the code for a different variant of the architecture we’re compiling for and that it’s possible to incorporate the generated code for all the variants in the TU into the same object file. In PTX, we can’t do the same. I.e. in your example above, we can’t incorporate sm_60 PTX/SASS into the same object file with sm_20.

If I understand your proposal correctly, instead of generating code for multiple different targets, you want to skip the targets you can’t generate code during particular compilation. I.e. it still implies one compilation pass per device. If that’s the case, then it does not change the current overloading scheme much as any given compilation will only deal with host + 1 device, and the code targeting incompatible devices would be ignored.

So, Extending device attribute and specializing it for particular target seems plausible, but it probably should not be based on attribute((target))

Another place that we use CUDA_ARCH_ today in Thrust and CUB is in
metaprogramming code that selects the correct “strategies” that should be
used to implement a particular algorithm:

enum arch {
host,
sm_30, sm_32, sm_35, // Kepler
sm_50, sm_52, sm_53, // Maxwell
sm_60, sm_61, sm_62, // Pascal
sm_70, // Volta
sm_72, sm_75 // Turing
};

host device
constexpr arch select_arch()
{
switch (CUDA_ARCH)
{
// …
};
}

template <class T, arch Arch = select_arch()>
struct radix_sort_tuning;

template
struct radix_sort_tuning<T, sm_35>
{
constexpr size_t INPUT_SIZE = sizeof(T);

constexpr size_t NOMINAL_4B_ITEMS_PER_THREAD = 11;
constexpr size_t ITEMS_PER_THREAD
= std::min(NOMIMAL_4B_ITEMS_PER_THREAD,
std::max(1, (NOMIMAL_4B_ITEMS_PER_THREAD * 4 / INPUT_SIZE)));

constexpr size_t BLOCK_THREADS = 256;
constexpr auto BLOCK_LOAD_STRATEGY = BLOCK_LOAD_WARP_TRANSPOSE;
constexpr auto CACHE_LOAD_STRATEGY = LOAD_LDG;
constexpr auto BLOCK_STORE_STRATEGY = BLOCK_STORE_WARP_TRANSPOSE;
};

template
struct radix_sort_tuning<T, sm_50> { /* … */ };

// …

With heterogeneous target attributes, we could implement select_arch like
so:

host
constexpr arch select_arch() { return host; }

device(“arch=sm_30”)
constexpr arch select_arch() { return sm_30; }

device(“arch=sm_35”)
constexpr arch select_arch() { return sm_35; }

// …

You could also potentially use this with if constexpr:

void foo()
{
// Moral equivalent of #if CUDA_ARCH
if constexpr (host != select_arch())
// …
else
// …
}

We could probably make select_arch() a constexpr builtin function returning current compilation target.

This feature would also make it much easier to port some of the more tricky parts
of libc++ to GPUs, such as iostreams and concurrency primitives.

It would be awesome if we could take host / device overloading a step
further and make it a full fledged replacement for CUDA_ARCH. It would provide
a possible future migration path away from CUDA_ARCH, which would enable us to
move to true merged parsing for heterogeneous C++: preprocess once, parse once,
perform platform-agnostic optimizations once, code gen multiple times.

+100.

So, questions:

Can target attributes go on constexpr functions today?

Godbolt says yes, but the function is no longer behaves as a constexpr as it must be dispatched as ifunc: https://godbolt.org/z/64I_SJ
In any case, I don’t think target attributes are a good match for this. We should probably extend ‘device’ instead.

Does anyone have suggestions for how this approach could be improved? Alternatives?

Perhaps we could consider using SFINAE to specialize device-side functions.
E.g. something along the lines of
enable_if_t<current_arch()==350, void> foo(){sm_35 code;}
enable_if_t<current_arch()==600, void> foo(){sm_60 code;}

Is there interest in this in Clang CUDA?

This is a very good question.
My (anecdotal) observation is that most of the GPU cycles these days are spent in the code written by NVIDIA itself. Everybody seems to be writing wrappers over cuDNN/cuBLAS/cuFFT, so interest in writing howe-grown CUDA code has diminished a lot – what’s the point if NVIDIA’s libraries are faster and beating it is unlikely.

If we had a GPU back-end which would be capable of generating native GPU code that can achieve peak performance, that could help resurrecting interest in writing CUDA code again.

Ironically, it may be AMD that may make this happen. There’s been a lot of progress lately in getting clang to compile HIP (~CUDA) for AMD GPUs and making it work with TensorFlow.

–Artem