oneMKL Integration

oneAPI.jl provides bindings to the Intel oneMKL library, enabling high-performance linear algebra operations on Intel GPUs.

Dense Linear Algebra (BLAS/LAPACK)

Standard BLAS and LAPACK operations are automatically accelerated when using oneArray.

using oneAPI, LinearAlgebra

A = oneArray(rand(Float32, 100, 100))
B = oneArray(rand(Float32, 100, 100))

# Matrix multiplication (GEMM)
C = A * B

# Linear solve (AX = B)
X = A \ B

Sparse Linear Algebra

oneAPI.jl supports sparse matrix operations via oneMKL's sparse BLAS functionality. These integrate with Julia's SparseArrays standard library.

using oneAPI, oneAPI.oneMKL, SparseArrays, LinearAlgebra

# Create a sparse matrix on CPU
A = sprand(Float32, 100, 100, 0.1)

# Move to GPU (converts to oneMKL format)
dA = oneMKL.oneSparseMatrixCSR(A)

# Create a dense vector
x = oneArray(rand(Float32, 100))

# Sparse matrix-vector multiplication
y = dA * x

Three storage formats are available: oneSparseMatrixCSR, oneSparseMatrixCSC and oneSparseMatrixCOO. oneMKL's sparse back-end is CSR-based, and a oneSparseMatrixCSC is therefore stored as the CSR representation of its transpose. As a consequence the triangular operations (sparse_trmv!, sparse_trsv!, sparse_trsm!) cannot be expressed for CSC matrices and throw an ArgumentError. Prefer CSR when you have the choice.

FFTs

Fast Fourier Transforms are supported through AbstractFFTs.jl interface integration with oneMKL DFTs. oneAPI.jl depends on AbstractFFTs.jl, so no separate FFT package is required.

using oneAPI, AbstractFFTs

a = oneArray(rand(ComplexF32, 1024))

# Forward FFT
b = fft(a)

# Inverse FFT
c = ifft(b)