586cacdbdd
With this commit, the CLC fmin/fmax builtins use clang's __builtin_elementwise_(min|max)imumnum which helps us generate LLVM minimumnum/maximumnum intrinsics directly. These intrinsics uniformly select the non-NaN input over the (quiet or signalling) NaN input, which corresponds to what the OpenCL CTS tests. These intrinsics maintain the vector types, as opposed to scalarizing, which was previously happening. This commit therefore helps to optimize codegen for those targets. Note that there is ongoing discussion regarding how these builtins should handle signalling NaNs in the OpenCL specification and whether they should be able to return a quiet NaN as per the IEEE behaviour. If the specification and/or CTS is ever updated to allow or mandate returning a qNAN, these builtins could/should be updated to use __builtin_elementwise_(min|max)num instead which would lower to LLVM minnum/maxnum intrinsics. The SPIR-V targets maintain the old implementations, as the LLVM -> SPIR-V translator can't currently handle the LLVM intrinsics. The implementation has been simplifies to consistently use clang builtins, as opposed to before where the half version was explicitly defined. [1] https://github.com/KhronosGroup/OpenCL-CTS/pull/2285
libclc
libclc is an open source implementation of the library requirements of the OpenCL C programming language, as specified by the OpenCL 1.1 Specification. The following sections of the specification impose library requirements:
- 6.1: Supported Data Types
- 6.2.3: Explicit Conversions
- 6.2.4.2: Reinterpreting Types Using as_type() and as_typen()
- 6.9: Preprocessor Directives and Macros
- 6.11: Built-in Functions
- 9.3: Double Precision Floating-Point
- 9.4: 64-bit Atomics
- 9.5: Writing to 3D image memory objects
- 9.6: Half Precision Floating-Point
libclc is intended to be used with the Clang compiler's OpenCL frontend.
libclc is designed to be portable and extensible. To this end, it provides generic implementations of most library requirements, allowing the target to override the generic implementation at the granularity of individual functions.
libclc currently supports PTX, AMDGPU, SPIRV and CLSPV targets, but support for more targets is welcome.
Compiling and installing
(in the following instructions you can use make or ninja)
For an in-tree build, Clang must also be built at the same time:
$ cmake <path-to>/llvm-project/llvm/CMakeLists.txt -DLLVM_ENABLE_PROJECTS="libclc;clang" \
-DCMAKE_BUILD_TYPE=Release -G Ninja
$ ninja
Then install:
$ ninja install
Note you can use the DESTDIR Makefile variable to do staged installs.
$ DESTDIR=/path/for/staged/install ninja install
To build out of tree, or in other words, against an existing LLVM build or install:
$ cmake <path-to>/llvm-project/libclc/CMakeLists.txt -DCMAKE_BUILD_TYPE=Release \
-G Ninja -DLLVM_DIR=$(<path-to>/llvm-config --cmakedir)
$ ninja
Then install as before.
In both cases this will include all supported targets. You can choose which
targets are enabled by passing -DLIBCLC_TARGETS_TO_BUILD to CMake. The default
is all.
In both cases, the LLVM used must include the targets you want libclc support for
(AMDGPU and NVPTX are enabled in LLVM by default). Apart from SPIRV where you do
not need an LLVM target but you do need the
llvm-spirv tool available.
Either build this in-tree, or place it in the directory pointed to by
LLVM_TOOLS_BINARY_DIR.