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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
268
benchmarks/GPU/bench_gpu_batching.cpp
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268
benchmarks/GPU/bench_gpu_batching.cpp
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// GPU batching benchmarks: multi-stream concurrency for many small solves.
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//
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// Each GpuLLT/GpuLU owns its own CUDA stream. This benchmark measures how
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// well multiple solver instances overlap on the GPU, which is critical for
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// workloads like robotics (many small systems) and SLAM (batched poses).
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//
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// Compares:
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// 1. Sequential: one solver handles all systems one by one
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// 2. Batched: N solvers on N streams, all launched before any sync
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// 3. CPU baseline: Eigen LLT on host
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//
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// For Nsight Systems: batched mode should show overlapping kernels on
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// different streams in the timeline view.
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//
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// nsys profile --trace=cuda ./bench_gpu_batching
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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 <memory>
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#include <vector>
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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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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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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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// Sequential: one solver, N systems solved one after another
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// --------------------------------------------------------------------------
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static void BM_Batch_Sequential(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const int batch_size = static_cast<int>(state.range(1));
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// Pre-generate all SPD matrices and RHS vectors.
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std::vector<Mat> As(batch_size);
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std::vector<Mat> Bs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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As[i] = make_spd(n);
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Bs[i] = Mat::Random(n, 1);
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}
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GpuLLT<Scalar> llt;
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for (auto _ : state) {
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std::vector<Mat> results(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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llt.compute(As[i]);
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results[i] = llt.solve(Bs[i]);
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}
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benchmark::DoNotOptimize(results.back().data());
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}
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state.counters["n"] = n;
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state.counters["batch"] = batch_size;
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state.counters["total_solves"] = batch_size;
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}
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// --------------------------------------------------------------------------
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// Sequential with DeviceMatrix (avoid re-upload of A each iteration)
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// --------------------------------------------------------------------------
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static void BM_Batch_Sequential_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 int batch_size = static_cast<int>(state.range(1));
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std::vector<Mat> As(batch_size);
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std::vector<Mat> Bs(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_As(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_Bs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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As[i] = make_spd(n);
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Bs[i] = Mat::Random(n, 1);
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d_As[i] = DeviceMatrix<Scalar>::fromHost(As[i]);
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d_Bs[i] = DeviceMatrix<Scalar>::fromHost(Bs[i]);
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}
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GpuLLT<Scalar> llt;
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for (auto _ : state) {
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std::vector<Mat> results(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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llt.compute(d_As[i]);
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DeviceMatrix<Scalar> d_X = llt.solve(d_Bs[i]);
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results[i] = d_X.toHost();
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}
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benchmark::DoNotOptimize(results.back().data());
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}
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state.counters["n"] = n;
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state.counters["batch"] = batch_size;
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state.counters["total_solves"] = batch_size;
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}
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// --------------------------------------------------------------------------
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// Batched: N solvers on N streams, overlapping execution
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// --------------------------------------------------------------------------
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static void BM_Batch_MultiStream(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const int batch_size = static_cast<int>(state.range(1));
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std::vector<Mat> As(batch_size);
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std::vector<Mat> Bs(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_As(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_Bs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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As[i] = make_spd(n);
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Bs[i] = Mat::Random(n, 1);
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d_As[i] = DeviceMatrix<Scalar>::fromHost(As[i]);
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d_Bs[i] = DeviceMatrix<Scalar>::fromHost(Bs[i]);
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}
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// N solvers = N independent CUDA streams.
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std::vector<std::unique_ptr<GpuLLT<Scalar>>> solvers(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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solvers[i] = std::make_unique<GpuLLT<Scalar>>();
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}
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for (auto _ : state) {
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// Phase 1: launch all factorizations (async, different streams).
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for (int i = 0; i < batch_size; ++i) {
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solvers[i]->compute(d_As[i]);
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}
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// Phase 2: launch all solves (async, different streams).
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std::vector<DeviceMatrix<Scalar>> d_Xs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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d_Xs[i] = solvers[i]->solve(d_Bs[i]);
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}
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// Phase 3: download all results.
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std::vector<Mat> results(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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results[i] = d_Xs[i].toHost();
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}
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benchmark::DoNotOptimize(results.back().data());
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}
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state.counters["n"] = n;
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state.counters["batch"] = batch_size;
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state.counters["streams"] = batch_size;
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state.counters["total_solves"] = batch_size;
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}
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// --------------------------------------------------------------------------
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// Batched with async download (overlap D2H with computation)
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// --------------------------------------------------------------------------
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static void BM_Batch_MultiStream_AsyncDownload(benchmark::State& state) {
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cuda_warmup();
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const Index n = state.range(0);
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const int batch_size = static_cast<int>(state.range(1));
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std::vector<Mat> As(batch_size);
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std::vector<Mat> Bs(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_As(batch_size);
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std::vector<DeviceMatrix<Scalar>> d_Bs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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As[i] = make_spd(n);
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Bs[i] = Mat::Random(n, 1);
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d_As[i] = DeviceMatrix<Scalar>::fromHost(As[i]);
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d_Bs[i] = DeviceMatrix<Scalar>::fromHost(Bs[i]);
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}
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std::vector<std::unique_ptr<GpuLLT<Scalar>>> solvers(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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solvers[i] = std::make_unique<GpuLLT<Scalar>>();
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}
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for (auto _ : state) {
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// Launch all compute + solve.
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std::vector<DeviceMatrix<Scalar>> d_Xs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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solvers[i]->compute(d_As[i]);
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d_Xs[i] = solvers[i]->solve(d_Bs[i]);
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}
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// Enqueue all async downloads.
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std::vector<HostTransfer<Scalar>> transfers;
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transfers.reserve(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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transfers.push_back(d_Xs[i].toHostAsync());
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}
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// Collect all results.
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for (int i = 0; i < batch_size; ++i) {
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benchmark::DoNotOptimize(transfers[i].get().data());
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}
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}
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state.counters["n"] = n;
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state.counters["batch"] = batch_size;
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state.counters["streams"] = batch_size;
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state.counters["total_solves"] = batch_size;
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}
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// --------------------------------------------------------------------------
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// CPU baseline: Eigen LLT on host, sequential
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// --------------------------------------------------------------------------
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static void BM_Batch_CPU(benchmark::State& state) {
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const Index n = state.range(0);
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const int batch_size = static_cast<int>(state.range(1));
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std::vector<Mat> As(batch_size);
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std::vector<Mat> Bs(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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As[i] = make_spd(n);
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Bs[i] = Mat::Random(n, 1);
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}
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for (auto _ : state) {
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std::vector<Mat> results(batch_size);
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for (int i = 0; i < batch_size; ++i) {
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LLT<Mat> llt(As[i]);
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results[i] = llt.solve(Bs[i]);
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}
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benchmark::DoNotOptimize(results.back().data());
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}
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state.counters["n"] = n;
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state.counters["batch"] = batch_size;
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state.counters["total_solves"] = batch_size;
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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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// Args: {matrix_size, batch_size}
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// Small matrices with large batches are the interesting case for multi-stream.
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BENCHMARK(BM_Batch_Sequential)->ArgsProduct({{16, 32, 64, 128, 256, 512}, {1, 4, 16, 64}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Batch_Sequential_Device)->ArgsProduct({{16, 32, 64, 128, 256, 512}, {1, 4, 16, 64}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Batch_MultiStream)->ArgsProduct({{16, 32, 64, 128, 256, 512}, {1, 4, 16, 64}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Batch_MultiStream_AsyncDownload)->ArgsProduct({{16, 32, 64, 128, 256, 512}, {1, 4, 16, 64}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Batch_CPU)->ArgsProduct({{16, 32, 64, 128, 256, 512}, {1, 4, 16, 64}})->Unit(benchmark::kMicrosecond);
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// Also run larger sizes with moderate batching.
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BENCHMARK(BM_Batch_MultiStream)->ArgsProduct({{512, 1024, 2048}, {1, 4, 8}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Batch_MultiStream_AsyncDownload)->ArgsProduct({{512, 1024, 2048}, {1, 4, 8}})->Unit(benchmark::kMicrosecond);
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// clang-format on
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