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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
73 lines
2.6 KiB
C++
73 lines
2.6 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_TEST_GPU_CONTEXT_H
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#define EIGEN_TEST_GPU_CONTEXT_H
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// RAII context for GPU tests that use NVIDIA library APIs (cuBLAS, cuSOLVER, etc.).
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// Owns a non-default CUDA stream. Library handles (cuBLAS, cuSOLVER, etc.) are added
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// here by each integration phase as needed; each handle is bound to the owned stream.
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//
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// Usage:
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// GpuContext ctx;
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// auto buf = gpu_copy_to_device(ctx.stream, A);
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// // ... call NVIDIA library APIs using ctx.stream / ctx.cusolver ...
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// ctx.synchronize();
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#include "gpu_test_helper.h"
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#ifdef EIGEN_USE_GPU
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#include <cusolverDn.h>
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// Checks cuSOLVER return codes, aborts on failure.
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#define CUSOLVER_CHECK(expr) \
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do { \
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cusolverStatus_t _status = (expr); \
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if (_status != CUSOLVER_STATUS_SUCCESS) { \
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printf("cuSOLVER error %d at %s:%d\n", static_cast<int>(_status), __FILE__, __LINE__); \
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gpu_assert(false); \
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} \
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} while (0)
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struct GpuContext {
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cudaStream_t stream = nullptr;
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cusolverDnHandle_t cusolver = nullptr;
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GpuContext() {
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GPU_CHECK(gpuGetDevice(&device_));
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GPU_CHECK(gpuGetDeviceProperties(&device_props_, device_));
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GPU_CHECK(cudaStreamCreate(&stream));
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CUSOLVER_CHECK(cusolverDnCreate(&cusolver));
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CUSOLVER_CHECK(cusolverDnSetStream(cusolver, stream));
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}
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~GpuContext() {
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if (cusolver) CUSOLVER_CHECK(cusolverDnDestroy(cusolver));
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if (stream) GPU_CHECK(cudaStreamDestroy(stream));
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}
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int device() const { return device_; }
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const gpuDeviceProp_t& deviceProperties() const { return device_props_; }
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// Wait for all work submitted on this context's stream to complete.
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void synchronize() { GPU_CHECK(cudaStreamSynchronize(stream)); }
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// Non-copyable, non-movable.
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GpuContext(const GpuContext&) = delete;
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GpuContext& operator=(const GpuContext&) = delete;
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private:
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int device_ = 0;
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gpuDeviceProp_t device_props_;
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};
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#endif // EIGEN_USE_GPU
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#endif // EIGEN_TEST_GPU_CONTEXT_H
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