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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
216
benchmarks/GPU/bench_gpu_chaining.cpp
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216
benchmarks/GPU/bench_gpu_chaining.cpp
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// GPU chaining benchmarks: measure async pipeline efficiency.
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//
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// Compares:
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// 1. Host round-trip per solve (baseline)
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// 2. DeviceMatrix chaining (no host round-trip between solves)
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// 3. Varying chain lengths (1, 2, 4, 8 consecutive solves)
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//
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// For Nsight Systems: look for gaps between kernel launches in the timeline.
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// Host round-trip creates visible idle gaps; chaining should show back-to-back kernels.
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//
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// nsys profile --trace=cuda,nvtx ./bench_gpu_chaining
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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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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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// Baseline: host round-trip between every solve
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// --------------------------------------------------------------------------
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static void BM_Chain_HostRoundtrip(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 chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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GpuLLT<Scalar> llt(A);
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for (auto _ : state) {
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Mat X = B;
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for (int i = 0; i < chain_len; ++i) {
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X = llt.solve(X); // host → device → host each time
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}
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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["chain"] = chain_len;
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state.counters["solves/iter"] = chain_len;
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}
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// --------------------------------------------------------------------------
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// DeviceMatrix chaining: no host round-trip between solves
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// --------------------------------------------------------------------------
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static void BM_Chain_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 chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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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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for (int i = 1; i < chain_len; ++i) {
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d_X = llt.solve(d_X); // device → device, fully async
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}
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Mat X = d_X.toHost(); // single sync at end
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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["chain"] = chain_len;
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state.counters["solves/iter"] = chain_len;
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}
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// --------------------------------------------------------------------------
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// DeviceMatrix chaining with async download (overlap D2H with next iteration)
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// --------------------------------------------------------------------------
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static void BM_Chain_DeviceAsync(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 chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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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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for (int i = 1; i < chain_len; ++i) {
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d_X = llt.solve(d_X);
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}
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auto transfer = d_X.toHostAsync();
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Mat X = transfer.get();
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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["chain"] = chain_len;
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state.counters["solves/iter"] = chain_len;
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}
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// --------------------------------------------------------------------------
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// Pure GPU chain (no download — measures kernel-only throughput)
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// --------------------------------------------------------------------------
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static void BM_Chain_DeviceNoDownload(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 chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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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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for (int i = 1; i < chain_len; ++i) {
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d_X = llt.solve(d_X);
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}
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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["chain"] = chain_len;
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state.counters["solves/iter"] = chain_len;
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}
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// --------------------------------------------------------------------------
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// Compute + solve chain (full pipeline: factorize, then chain solves)
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// --------------------------------------------------------------------------
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static void BM_FullPipeline_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 int chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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for (auto _ : state) {
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GpuLLT<Scalar> llt(A);
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Mat X = B;
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for (int i = 0; i < chain_len; ++i) {
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X = llt.solve(X);
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}
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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["chain"] = chain_len;
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}
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static void BM_FullPipeline_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 chain_len = static_cast<int>(state.range(1));
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Mat A = make_spd(n);
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Mat B = Mat::Random(n, 1);
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for (auto _ : state) {
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auto d_A = DeviceMatrix<Scalar>::fromHost(A);
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auto d_B = DeviceMatrix<Scalar>::fromHost(B);
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GpuLLT<Scalar> llt;
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llt.compute(d_A);
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DeviceMatrix<Scalar> d_X = llt.solve(d_B);
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for (int i = 1; i < chain_len; ++i) {
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d_X = llt.solve(d_X);
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}
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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["chain"] = chain_len;
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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, chain_length}
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BENCHMARK(BM_Chain_HostRoundtrip)->ArgsProduct({{64, 256, 1024, 4096}, {1, 2, 4, 8}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Chain_Device)->ArgsProduct({{64, 256, 1024, 4096}, {1, 2, 4, 8}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Chain_DeviceAsync)->ArgsProduct({{64, 256, 1024, 4096}, {1, 2, 4, 8}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_Chain_DeviceNoDownload)->ArgsProduct({{64, 256, 1024, 4096}, {1, 2, 4, 8}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_FullPipeline_Host)->ArgsProduct({{256, 1024, 4096}, {1, 4}})->Unit(benchmark::kMicrosecond);
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BENCHMARK(BM_FullPipeline_Device)->ArgsProduct({{256, 1024, 4096}, {1, 4}})->Unit(benchmark::kMicrosecond);
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// clang-format on
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