cutlass/cmake/NvidiaCutlassConfig.cmake

get_filename_component(NvidiaCutlass_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)

include(CMakeFindDependencyMacro)

if(TARGET nvidia::cutlass::CUTLASS)
  return()
endif()

include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake")

# For backward compatibility with the old name
add_library(cutlass_lib ALIAS cutlass_library)
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater. 2019-11-20 08:55:34 +08:00			`get_filename_component(NvidiaCutlass_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)`

			`include(CMakeFindDependencyMacro)`

CUTLASS 3.2.1 (#1113) * Updates for 3.2.1 release. * Minor fix in gemm op profiler for raster order. * Add scheduler mapping for raster order in the kernels. 2023-09-27 05:24:26 +08:00			`if(TARGET nvidia::cutlass::CUTLASS)`
			`return()`
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater. 2019-11-20 08:55:34 +08:00			`endif()`
CUTLASS 3.2.1 (#1113) * Updates for 3.2.1 release. * Minor fix in gemm op profiler for raster order. * Add scheduler mapping for raster order in the kernels. 2023-09-27 05:24:26 +08:00
			`include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake")`

			`# For backward compatibility with the old name`
			`add_library(cutlass_lib ALIAS cutlass_library)`