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eigen/benchmarks/GPU/CMakeLists.txt

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GPU: Add library dispatch module (DeviceMatrix, cuBLAS, cuSOLVER) Add Eigen/GPU module: A standalone GPU library dispatch layer where DeviceMatrix<Scalar> operations map 1:1 to cuBLAS/cuSOLVER calls. CPU and GPU solvers coexist in the same binary with compatible syntax. Core infrastructure: - DeviceMatrix<Scalar>: RAII dense column-major GPU memory wrapper with async host transfer (fromHost/toHost) and CUDA event-based cross-stream synchronization. - GpuContext: Unified execution context owning a CUDA stream + cuBLAS handle + cuSOLVER handle. Thread-local default with explicit override via setThreadLocal(). Stream-borrowing constructor for integration. - DeviceBuffer: Typed RAII device allocation with move semantics. cuBLAS dispatch (expression syntax): - GEMM: d_C = d_A.adjoint() * d_B (cublasXgemm) - TRSM: d_X = d_A.triangularView<Lower>().solve(d_B) (cublasXtrsm) - SYMM/HEMM: d_C = d_A.selfadjointView<Lower>() * d_B (cublasXsymm) - SYRK/HERK: d_C = d_A * d_A.adjoint() (cublasXsyrk) cuSOLVER dispatch: - GpuLLT: Cached Cholesky factorization (cusolverDnXpotrf + Xpotrs) - GpuLU: Cached LU factorization (cusolverDnXgetrf + Xgetrs) - Solver chaining: auto x = d_A.llt().solve(d_B) - Solver expressions with .device(ctx) for explicit stream control. CI: Bump CUDA container to Ubuntu 22.04 (CMake 3.22), GCC 10->11, Clang 12->14. Bump cmake_minimum_required to 3.17 for FindCUDAToolkit. Tests: gpu_cublas.cpp, gpu_cusolver_llt.cpp, gpu_cusolver_lu.cpp, gpu_device_matrix.cpp, gpu_library_example.cu Benchmarks: bench_gpu_solvers.cpp, bench_gpu_chaining.cpp, bench_gpu_batching.cpp
2026-04-09 16:15:39 -07:00
# GPU benchmarks require CUDA runtime + cuSOLVER.
# Build separately from the main benchmark tree since they need CUDA toolchain.
#
# Usage:
# cmake -G Ninja -B build-bench-gpu -S benchmarks/GPU \
# -DCMAKE_CUDA_ARCHITECTURES=89
# cmake --build build-bench-gpu
#
# Profiling:
# nsys profile --trace=cuda ./build-bench-gpu/bench_gpu_solvers
# ncu --set full -o profile ./build-bench-gpu/bench_gpu_solvers --benchmark_filter=BM_GpuLLT_Compute/4096
cmake_minimum_required(VERSION 3.18)
project(EigenGpuBenchmarks CXX CUDA)
GPU: Add library dispatch module (DeviceMatrix, cuBLAS, cuSOLVER) Add Eigen/GPU module: A standalone GPU library dispatch layer where DeviceMatrix<Scalar> operations map 1:1 to cuBLAS/cuSOLVER calls. CPU and GPU solvers coexist in the same binary with compatible syntax. Core infrastructure: - DeviceMatrix<Scalar>: RAII dense column-major GPU memory wrapper with async host transfer (fromHost/toHost) and CUDA event-based cross-stream synchronization. - GpuContext: Unified execution context owning a CUDA stream + cuBLAS handle + cuSOLVER handle. Thread-local default with explicit override via setThreadLocal(). Stream-borrowing constructor for integration. - DeviceBuffer: Typed RAII device allocation with move semantics. cuBLAS dispatch (expression syntax): - GEMM: d_C = d_A.adjoint() * d_B (cublasXgemm) - TRSM: d_X = d_A.triangularView<Lower>().solve(d_B) (cublasXtrsm) - SYMM/HEMM: d_C = d_A.selfadjointView<Lower>() * d_B (cublasXsymm) - SYRK/HERK: d_C = d_A * d_A.adjoint() (cublasXsyrk) cuSOLVER dispatch: - GpuLLT: Cached Cholesky factorization (cusolverDnXpotrf + Xpotrs) - GpuLU: Cached LU factorization (cusolverDnXgetrf + Xgetrs) - Solver chaining: auto x = d_A.llt().solve(d_B) - Solver expressions with .device(ctx) for explicit stream control. CI: Bump CUDA container to Ubuntu 22.04 (CMake 3.22), GCC 10->11, Clang 12->14. Bump cmake_minimum_required to 3.17 for FindCUDAToolkit. Tests: gpu_cublas.cpp, gpu_cusolver_llt.cpp, gpu_cusolver_lu.cpp, gpu_device_matrix.cpp, gpu_library_example.cu Benchmarks: bench_gpu_solvers.cpp, bench_gpu_chaining.cpp, bench_gpu_batching.cpp
2026-04-09 16:15:39 -07:00
find_package(benchmark REQUIRED)
find_package(CUDAToolkit REQUIRED)
set(EIGEN_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../..")
function(eigen_add_gpu_benchmark name source)
cmake_parse_arguments(BENCH "" "" "LIBRARIES;DEFINITIONS" ${ARGN})
if(NOT IS_ABSOLUTE "${source}")
set(source "${CMAKE_CURRENT_SOURCE_DIR}/${source}")
endif()
add_executable(${name} ${source})
target_include_directories(${name} PRIVATE
${EIGEN_SOURCE_DIR}
${CUDAToolkit_INCLUDE_DIRS})
target_link_libraries(${name} PRIVATE
benchmark::benchmark benchmark::benchmark_main
CUDA::cudart CUDA::cusolver CUDA::cublas)
if(BENCH_LIBRARIES)
target_link_libraries(${name} PRIVATE ${BENCH_LIBRARIES})
endif()
target_compile_options(${name} PRIVATE -O3 -DNDEBUG)
target_compile_definitions(${name} PRIVATE EIGEN_USE_GPU)
if(BENCH_DEFINITIONS)
target_compile_definitions(${name} PRIVATE ${BENCH_DEFINITIONS})
endif()
endfunction()
# Solver benchmarks: LLT/LU compute + solve, host vs device paths, CPU baselines.
eigen_add_gpu_benchmark(bench_gpu_solvers bench_gpu_solvers.cpp)
eigen_add_gpu_benchmark(bench_gpu_solvers_float bench_gpu_solvers.cpp DEFINITIONS SCALAR=float)
# Chaining benchmarks: async pipeline efficiency, host-roundtrip vs device chain.
eigen_add_gpu_benchmark(bench_gpu_chaining bench_gpu_chaining.cpp)
eigen_add_gpu_benchmark(bench_gpu_chaining_float bench_gpu_chaining.cpp DEFINITIONS SCALAR=float)
# Batching benchmarks: multi-stream concurrency for many small systems.
eigen_add_gpu_benchmark(bench_gpu_batching bench_gpu_batching.cpp)
eigen_add_gpu_benchmark(bench_gpu_batching_float bench_gpu_batching.cpp DEFINITIONS SCALAR=float)
# FFT benchmarks: 1D/2D C2C, R2C, C2R throughput and plan reuse.
eigen_add_gpu_benchmark(bench_gpu_fft bench_gpu_fft.cpp LIBRARIES CUDA::cufft)
eigen_add_gpu_benchmark(bench_gpu_fft_double bench_gpu_fft.cpp LIBRARIES CUDA::cufft DEFINITIONS SCALAR=double)
# CG sync overhead benchmark: host vs device pointer mode for reductions.
# Uses CUDA kernels for device scalar arithmetic.
add_executable(bench_gpu_cg_sync bench_gpu_cg_sync.cu)
target_include_directories(bench_gpu_cg_sync PRIVATE
${EIGEN_SOURCE_DIR}
${CUDAToolkit_INCLUDE_DIRS})
target_link_libraries(bench_gpu_cg_sync PRIVATE
benchmark::benchmark benchmark::benchmark_main
CUDA::cudart CUDA::cusolver CUDA::cublas CUDA::cusparse CUDA::npps CUDA::nppc)
target_compile_options(bench_gpu_cg_sync PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-O3 --expt-relaxed-constexpr>)
target_compile_definitions(bench_gpu_cg_sync PRIVATE EIGEN_USE_GPU)
# GPU CG vs CPU CG comparison benchmark.
add_executable(bench_gpu_cg_vs_cpu bench_gpu_cg_vs_cpu.cu)
target_include_directories(bench_gpu_cg_vs_cpu PRIVATE
${EIGEN_SOURCE_DIR}
${CUDAToolkit_INCLUDE_DIRS})
target_link_libraries(bench_gpu_cg_vs_cpu PRIVATE
benchmark::benchmark benchmark::benchmark_main
CUDA::cudart CUDA::cusolver CUDA::cublas CUDA::cusparse CUDA::npps CUDA::nppc)
target_compile_options(bench_gpu_cg_vs_cpu PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-O3 --expt-relaxed-constexpr>)
target_compile_definitions(bench_gpu_cg_vs_cpu PRIVATE EIGEN_USE_GPU)
# Bundle Adjustment benchmark: GPU CG vs CPU CG on real BAL datasets.
add_executable(bench_gpu_ba bench_gpu_ba.cu)
target_include_directories(bench_gpu_ba PRIVATE
${EIGEN_SOURCE_DIR}
${CUDAToolkit_INCLUDE_DIRS})
target_link_libraries(bench_gpu_ba PRIVATE
benchmark::benchmark
CUDA::cudart CUDA::cusolver CUDA::cublas CUDA::cusparse CUDA::npps CUDA::nppc)
target_compile_options(bench_gpu_ba PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-O3 --expt-relaxed-constexpr>)
target_compile_definitions(bench_gpu_ba PRIVATE EIGEN_USE_GPU)