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This is the release of the CUDA 13.3 samples, which include additions for CUDA Tile C++, and updated CCCL and Python samples.
tileSpMV
Description
This sample demonstrates sparse matrix-vector multiplication (SpMV)
y = A * x using CUDA Tile C++.
The matrix is built directly on the host in Sliced ELLPACK (SELL)
format — the format the Tile kernel actually reads. SELL is the
same idea as ELLPACK applied per-slice: rows are grouped into
slices of ROWS consecutive rows (sorted by length to minimize
padding within a slice) and stored column-major so that the k-th
nonzero of every row in the slice occupies a contiguous span of
ROWS elements in memory.
Each CTA processes one slice using a 2D tile of shape<ROWS, COLS>:
- Dimension 0 (
ROWS): the rows of the slice (one tile row per matrix row in the slice) - Dimension 1 (
COLS): the nextCOLSnonzeros of every row in the slice, processed simultaneously
The kernel computes partial products against the x-vector (an
irreducible gather), accumulates into a 2D tile, reduces along the
column dimension with cuda::tiles::sum(acc, 1_ic) to produce one
sum per row, and scatters the per-row sums to y using the slice
permutation array.
The sample generates a single random sparse matrix and verifies the Tile kernel's output against a CPU reference SpMV.
Expected Output
Random sparse matrix: rows=100000, cols=100000, nnz=..., avg nnz/row=...
Tile configuration: ROWS=64, COLS=16 (... slices)
Success! Tile SpMV matches the CPU reference.
Prerequisites
- CUDA Toolkit version 13.3 or later.
- CUDA Driver version 580 or later.
- Host compiler with C++20 support.