- Raise CUDA minimum from 9.0 to 11.4 (sm_70/Volta).
- Raise HIP minimum to GFX906 (Vega 20/MI50) / ROCm 5.6.
- Remove EIGEN_HAS_{CUDA,HIP,GPU}_FP16 guards — FP16 is always available
on sm_70+ and GFX906+.
- Remove obsolete __HIP_ARCH_HAS_* preprocessor branches.
- C++14 cleanup: remove pre-C++14 workarounds in GPU code.
- Fix NVCC warnings (deprecated register keyword, unreachable code,
tautological comparisons).
- Fix HIP test execution on gfx1151.
- Update CI configuration for new minimum versions.
Without this flag, when compiling with nvcc, if the compute architecture of a card does
not exactly match any of those listed for `-gencode arch=compute_<arch>,code=sm_<arch>`,
then the kernel will fail to run with:
```
cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device.
```
This can happen, for example, when compiling with an older cuda version
that does not support a newer architecture (e.g. T4 is `sm_75`, but cuda
9.2 only supports up to `sm_70`).
With the `-arch=<arch>` flag, the code will compile and run at the
supplied architecture.
- Unify test/CMakeLists.txt and unsupported/test/CMakeLists.txt
- Added `EIGEN_CUDA_FLAGS` that are appended to the set of flags passed
to the cuda compiler (nvcc or clang).
The latter is to support passing custom flags (e.g. `-arch=` to nvcc,
or to disable cuda-specific warnings).
This introduces new functions:
```
// returns kernel(args...) running on the CPU.
Eigen::run_on_cpu(Kernel kernel, Args&&... args);
// returns kernel(args...) running on the GPU.
Eigen::run_on_gpu(Kernel kernel, Args&&... args);
Eigen::run_on_gpu_with_hint(size_t buffer_capacity_hint, Kernel kernel, Args&&... args);
// returns kernel(args...) running on the GPU if using
// a GPU compiler, or CPU otherwise.
Eigen::run(Kernel kernel, Args&&... args);
Eigen::run_with_hint(size_t buffer_capacity_hint, Kernel kernel, Args&&... args);
```
Running on the GPU is accomplished by:
- Serializing the kernel inputs on the CPU
- Transferring the inputs to the GPU
- Passing the kernel and serialized inputs to a GPU kernel
- Deserializing the inputs on the GPU
- Running `kernel(inputs...)` on the GPU
- Serializing all output parameters and the return value
- Transferring the serialized outputs back to the CPU
- Deserializing the outputs and return value on the CPU
- Returning the deserialized return value
All inputs must be serializable (currently POD types, `Eigen::Matrix`
and `Eigen::Array`). The kernel must also be POD (though usually
contains no actual data).
Tested on CUDA 9.1, 10.2, 11.3, with g++-6, g++-8, g++-10 respectively.
This MR depends on !622, !623, !624.
An analogue of `std::tuple` that works on device.
Context: I've tried `std::tuple` in various versions of NVCC and clang,
and although code seems to compile, it often fails to run - generating
"illegal memory access" errors, or "illegal instruction" errors.
This replacement does work on device.
The `Serializer<T>` class implements a binary serialization that
can write to (`serialize`) and read from (`deserialize`) a byte
buffer. Also added convenience routines for serializing
a list of arguments.
This will mainly be for testing, specifically to transfer data to
and from the GPU.
Removed all configurations that explicitly test or set the c++ standard
flags. The only place the standard is now configured is at the top of
the main `CMakeLists.txt` file, which can easily be updated (e.g. if
we decide to move to c++14+). This can also be set via command-line using
```
> cmake -DCMAKE_CXX_STANDARD 14
```
Kept the `EIGEN_TEST_CXX11` flag for now - that still controls whether to
build/run the `cxx11_*` tests. We will likely end up renaming these
tests and removing the `CXX11` subfolder.
