Crate baracuda_cusparse

Expand description

Safe Rust wrappers for NVIDIA cuSPARSE.

Covers the modern generic-API surface: Handle, SpMat (CSR/CSC/COO/BSR), DnMat, DnVec, and the family of op algorithms — SpMV, SpMM, SpGEMM, SpSV, SpSM, SDDMM — plus CSR↔CSC and sparse↔dense conversions, and the sparse BLAS-1 helpers (axpby, gather, scatter, rot).

All matrix/vector descriptors borrow the underlying DeviceBuffers and tie their lifetime to them so the buffers can’t be freed while cuSPARSE is still holding references.

Structs§

DnMat
DnVec
Handle: Owned cuSPARSE handle.
SpGEMMPlan: Per-plan handle for a 3-phase SpGEMM computation.
SpMat: A sparse-matrix descriptor (CSR / CSC / COO / BSR). The descriptor keeps pointers to externally-owned device buffers; the lifetime parameter ties those buffers to the descriptor.
SpSMPlan
SpSVPlan

Enums§

Csr2CscAlg
Diag
Fill
IndexBase
Op: Transpose / conjugate-transpose selector.
Order: Dense-matrix storage order.
SDDMMAlg
SpGEMMAlg
SpMMAlg
SpMVAlg
SpSMAlg
SpSVAlg

Traits§

SparseScalar: Types that cuSPARSE’s generic API accepts as element / compute type.

Functions§

axpby
csr2csc_ex2: Convert a CSR matrix to CSC format using the modern Ex2 entry point — supports algorithm selection (alg) and arbitrary value types.
csr2csc_ex2_buffer_size: Workspace size in bytes for csr2csc_ex2.
dense_to_sparse_analysis
dense_to_sparse_buffer_size
dense_to_sparse_convert
gather
rot
scatter
sddmm
sddmm_buffer_size
sddmm_preprocess: One-time preprocessing before sddmm. See spmm_preprocess for the rationale.
sparse_to_dense
sparse_to_dense_buffer_size
spgemm_compute^⚠: Phase 2: compute. Same two-step pattern for buffer2.
spgemm_copy: Phase 3: write output arrays into the pre-populated output SpMat.
spgemm_work_estimation^⚠: Phase 1: work-estimation. The caller provides buffer1 whose size is returned in size1; pass null the first time, then allocate & re-call.
spmm
spmm_buffer_size: Query buffer-size for C = alpha * op(A) * op(B) + beta * C, A sparse.
spmm_preprocess: One-time preprocessing before spmm. Pre-computes algorithm-specific state into workspace so subsequent spmm calls (with the same A sparsity pattern + dimensions) are faster. Use this when the same matrix is multiplied many times.
spmv: y = alpha * op(A) * x + beta * y.
spmv_buffer_size: Query buffer-size for y = alpha * op(A) * x + beta * y.
spsm_analysis
spsm_buffer_size
spsm_solve
spsv_analysis
spsv_buffer_size
spsv_solve

Type Aliases§

CsrMatrix: Legacy alias kept for callers from v0.1 — prefer SpMat::csr.
DenseVector: Legacy alias — prefer DnVec.
Error: Error type for cuSPARSE operations.
Result: Result alias.