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Add the operator interface needed for GPU iterative solvers: - BLAS Level-1 on DeviceMatrix: dot(), norm(), squaredNorm(), setZero(), noalias(), operator+=/-=/\*= dispatching to cuBLAS axpy/scal/dot/nrm2. - DeviceScalar<Scalar>: device-resident scalar returned by reductions. Defers host sync until value is read (implicit conversion). Device-side division via NPP for real types. - GpuContext: stream-borrowing constructor, setThreadLocal(), cublasLtHandle(), cusparseHandle(). - GEMM upgraded from cublasGemmEx to cublasLtMatmul with heuristic algorithm selection and plan caching. - GpuSparseContext: GpuContext& constructor for same-stream execution, deviceView() returning DeviceSparseView with operator* for device-resident SpMV (d_y = d_A * d_x). - geam expressions: d_C = d_A + alpha * d_B via cublasXgeam. - GpuSVD::matrixV() convenience wrapper. These additions make DeviceMatrix usable as a VectorType in Eigen algorithm templates. Conjugate gradient is the motivating example and is tested against CPU ConjugateGradient for correctness. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
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