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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
This commit is contained in:
Rasmus Munk Larsen
296
benchmarks/GPU/bench_gpu_solvers.cpp
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296
benchmarks/GPU/bench_gpu_solvers.cpp
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// GPU solver benchmarks: GpuLLT and GpuLU compute + solve throughput.
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//
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// Measures factorization and solve performance for the host-matrix and
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// DeviceMatrix code paths across a range of matrix sizes.
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//
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// For Nsight Systems profiling:
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// nsys profile --trace=cuda,nvtx ./bench_gpu_solvers
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//
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// For Nsight Compute kernel analysis:
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// ncu --set full -o profile ./bench_gpu_solvers --benchmark_filter=BM_GpuLLT_Compute/4096
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#include <benchmark/benchmark.h>
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#include <Eigen/Cholesky>
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#include <Eigen/GPU>
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#include <Eigen/LU>
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using namespace Eigen;
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#ifndef SCALAR
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#define SCALAR double
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#endif
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using Scalar = SCALAR;
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using Mat = Matrix<Scalar, Dynamic, Dynamic>;
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// --------------------------------------------------------------------------
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// Helpers
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// --------------------------------------------------------------------------
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static Mat make_spd(Index n) {
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Mat M = Mat::Random(n, n);
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return M.adjoint() * M + Mat::Identity(n, n) * static_cast<Scalar>(n);
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}
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// CUDA warm-up: ensure the GPU is initialized before timing.
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static void cuda_warmup() {
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static bool done = false;
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if (!done) {
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void* p;
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cudaMalloc(&p, 1);
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cudaFree(p);
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done = true;
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}
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}
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// --------------------------------------------------------------------------
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// GpuLLT benchmarks
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// --------------------------------------------------------------------------
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// Factorize from host matrix (includes H2D upload).
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static void BM_GpuLLT_Compute_Host(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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Mat A = make_spd(n);
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GpuLLT<Scalar> llt;
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for (auto _ : state) {
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llt.compute(A);
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if (llt.info() != Success) state.SkipWithError("factorization failed");
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}
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double flops = static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n) / 3.0;
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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// Factorize from DeviceMatrix (D2D copy path).
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static void BM_GpuLLT_Compute_Device(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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Mat A = make_spd(n);
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auto d_A = DeviceMatrix<Scalar>::fromHost(A);
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GpuLLT<Scalar> llt;
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for (auto _ : state) {
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llt.compute(d_A);
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if (llt.info() != Success) state.SkipWithError("factorization failed");
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}
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double flops = static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n) / 3.0;
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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// Factorize from DeviceMatrix (move path, no copy).
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static void BM_GpuLLT_Compute_DeviceMove(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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Mat A = make_spd(n);
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GpuLLT<Scalar> llt;
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for (auto _ : state) {
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auto d_A = DeviceMatrix<Scalar>::fromHost(A);
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llt.compute(std::move(d_A));
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if (llt.info() != Success) state.SkipWithError("factorization failed");
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}
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double flops = static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n) / 3.0;
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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// Solve from host matrix (H2D + potrs + D2H).
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static void BM_GpuLLT_Solve_Host(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const Index nrhs = state.range(1);
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, nrhs);
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GpuLLT<Scalar> llt(A);
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for (auto _ : state) {
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Mat X = llt.solve(B);
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benchmark::DoNotOptimize(X.data());
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}
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state.counters["n"] = n;
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state.counters["nrhs"] = nrhs;
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}
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// Solve from DeviceMatrix (D2D + potrs, async, toHost at end).
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static void BM_GpuLLT_Solve_Device(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const Index nrhs = state.range(1);
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, nrhs);
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GpuLLT<Scalar> llt(A);
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auto d_B = DeviceMatrix<Scalar>::fromHost(B);
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for (auto _ : state) {
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DeviceMatrix<Scalar> d_X = llt.solve(d_B);
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Mat X = d_X.toHost();
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benchmark::DoNotOptimize(X.data());
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}
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state.counters["n"] = n;
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state.counters["nrhs"] = nrhs;
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}
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// Solve staying entirely on device (no toHost — measures pure GPU time).
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static void BM_GpuLLT_Solve_DeviceOnly(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const Index nrhs = state.range(1);
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, nrhs);
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GpuLLT<Scalar> llt(A);
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auto d_B = DeviceMatrix<Scalar>::fromHost(B);
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for (auto _ : state) {
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DeviceMatrix<Scalar> d_X = llt.solve(d_B);
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// Force completion without D2H transfer.
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cudaStreamSynchronize(llt.stream());
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benchmark::DoNotOptimize(d_X.data());
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}
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state.counters["n"] = n;
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state.counters["nrhs"] = nrhs;
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}
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// --------------------------------------------------------------------------
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// GpuLU benchmarks
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// --------------------------------------------------------------------------
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static void BM_GpuLU_Compute_Host(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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Mat A = Mat::Random(n, n);
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GpuLU<Scalar> lu;
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for (auto _ : state) {
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lu.compute(A);
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if (lu.info() != Success) state.SkipWithError("factorization failed");
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}
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double flops = 2.0 / 3.0 * static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n);
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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static void BM_GpuLU_Compute_Device(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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Mat A = Mat::Random(n, n);
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auto d_A = DeviceMatrix<Scalar>::fromHost(A);
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GpuLU<Scalar> lu;
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for (auto _ : state) {
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lu.compute(d_A);
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if (lu.info() != Success) state.SkipWithError("factorization failed");
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}
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double flops = 2.0 / 3.0 * static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n);
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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static void BM_GpuLU_Solve_Host(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const Index nrhs = state.range(1);
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Mat A = Mat::Random(n, n);
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Mat B = Mat::Random(n, nrhs);
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GpuLU<Scalar> lu(A);
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for (auto _ : state) {
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Mat X = lu.solve(B);
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benchmark::DoNotOptimize(X.data());
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}
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state.counters["n"] = n;
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state.counters["nrhs"] = nrhs;
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}
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static void BM_GpuLU_Solve_Device(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const Index nrhs = state.range(1);
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Mat A = Mat::Random(n, n);
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Mat B = Mat::Random(n, nrhs);
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GpuLU<Scalar> lu(A);
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auto d_B = DeviceMatrix<Scalar>::fromHost(B);
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for (auto _ : state) {
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DeviceMatrix<Scalar> d_X = lu.solve(d_B);
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Mat X = d_X.toHost();
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benchmark::DoNotOptimize(X.data());
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}
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state.counters["n"] = n;
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state.counters["nrhs"] = nrhs;
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}
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// --------------------------------------------------------------------------
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// CPU baselines for comparison
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// --------------------------------------------------------------------------
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static void BM_CpuLLT_Compute(benchmark::State& state) {
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const Index n = state.range(0);
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Mat A = make_spd(n);
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LLT<Mat> llt;
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for (auto _ : state) {
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llt.compute(A);
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benchmark::DoNotOptimize(llt.matrixLLT().data());
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}
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double flops = static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n) / 3.0;
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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static void BM_CpuLU_Compute(benchmark::State& state) {
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const Index n = state.range(0);
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Mat A = Mat::Random(n, n);
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PartialPivLU<Mat> lu;
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for (auto _ : state) {
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lu.compute(A);
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benchmark::DoNotOptimize(lu.matrixLU().data());
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}
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double flops = 2.0 / 3.0 * static_cast<double>(n) * static_cast<double>(n) * static_cast<double>(n);
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state.counters["GFLOPS"] =
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benchmark::Counter(flops, benchmark::Counter::kIsIterationInvariantRate, benchmark::Counter::kIs1000);
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state.counters["n"] = n;
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}
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// --------------------------------------------------------------------------
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// Registration
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// --------------------------------------------------------------------------
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// clang-format off
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BENCHMARK(BM_GpuLLT_Compute_Host)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLLT_Compute_Device)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLLT_Compute_DeviceMove)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLLT_Solve_Host)->ArgsProduct({{64, 256, 1024, 4096}, {1, 16}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLLT_Solve_Device)->ArgsProduct({{64, 256, 1024, 4096}, {1, 16}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLLT_Solve_DeviceOnly)->ArgsProduct({{64, 256, 1024, 4096}, {1, 16}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLU_Compute_Host)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLU_Compute_Device)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLU_Solve_Host)->ArgsProduct({{64, 256, 1024, 4096}, {1, 16}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_GpuLU_Solve_Device)->ArgsProduct({{64, 256, 1024, 4096}, {1, 16}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_CpuLLT_Compute)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_CpuLU_Compute)->ArgsProduct({{64, 128, 256, 512, 1024, 2048, 4096}})->Unit(benchmark::kMicrosecond);
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// clang-format on
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