db98e2922f
These four functions all related in that they share tables and helper functions. Furthermore, the acosh and atanh builtins call log1p. As with other work in this area, these builtins are now vectorized. To enable this, there are new table accessor functions which return a vector of table values using a vector of indices. These are internally scalarized, in the absence of gather operations. Some tables which were tables of multiple entries (e.g., double2) are split into two separate "low" and "high" tables. This might affect the performance of memory operations but are hopefully mitigated by better codegen overall.
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.