[libclc] Use builtin_convertvector to convert between vector types (#115865)

This keeps values in vectors, rather than scalarizing them and then
reconstituting the vector. The builtin is identical to performing a
C-style cast on each element, which is what we were doing by recursively
splitting the vector down to calling the "base" conversion function on
each element.

GitOrigin-RevId: 0d2ef7af1956b463b87a09500bd87bd4147616d4
1 file changed
tree: 820056b2e8fbd7ae1022f83b5fbe703f7cbd95eb
  1. amdgcn/
  2. amdgcn-amdhsa/
  3. amdgpu/
  4. clc/
  5. clspv/
  6. cmake/
  7. generic/
  8. ptx/
  9. ptx-nvidiacl/
  10. r600/
  11. spirv/
  12. spirv64/
  13. test/
  14. utils/
  15. www/
  16. .gitignore
  17. check_external_calls.sh
  18. CMakeLists.txt
  19. compile-test.sh
  20. CREDITS.TXT
  21. libclc.pc.in
  22. LICENSE.TXT
  23. README.md
README.md

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.

Website

https://libclc.llvm.org/