AMDGPU.jl

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Sparse Arrays ​

AMDGPU.jl wraps AMD's rocSPARSE and stores sparse matrices on the device with the AMDGPU.rocSPARSE array types:

  • ROCSparseMatrixCSR — compressed sparse row (the usual default for GPU work).

  • ROCSparseMatrixCSC — compressed sparse column.

  • ROCSparseMatrixCOO — coordinate format.

  • ROCSparseVector — sparse vector.

Construct them by converting a host SparseMatrixCSC, and convert back the same way:

julia
using AMDGPU, SparseArrays
using AMDGPU.rocSPARSE

S  = sprand(Float32, 1000, 1000, 0.01)
dS = ROCSparseMatrixCSR(S)        # upload to the GPU

SparseMatrixCSC(dS)               # download back to the host

Operations ​

Sparse matrix–vector (SpMV) and matrix–matrix (SpMM) products use the standard * operator:

julia
x = AMDGPU.rand(Float32, 1000)
y = dS * x                        # SpMV

B = AMDGPU.rand(Float32, 1000, 8)
Y = dS * B                        # SpMM

Incomplete-factorization preconditioners (ic0, ilu0) and further sparse routines (conversions between formats, triangular solves, gather/scatter) are available in the AMDGPU.rocSPARSE submodule for building iterative solvers on the GPU.