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Nov 25, 2025
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Update README.md #551

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26 changes: 19 additions & 7 deletions README.md
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Expand Up @@ -29,20 +29,20 @@ good kernel programming capabilties, and as a demonstration of that it fully imp
the GPUArrays.jl array interfaces. This results in a full-featured GPU array type.

However, the package has not been extensively tested, and performance issues might be
present. The integration with vendor libraries like oneMKL or oneDNN is still in
development, and as result certain array operations may be unavailable or slow.
present. The integration with vendor libraries like oneMKL has been extended with support
for sparse linear algebra operations. Some operations may still be unavailable or slow.


## Quick start

You need to use Julia 1.8 or higher, and it is strongly advised to use [the official
You need to use Julia 1.10 or higher, and it is strongly advised to use [the official
binaries](https://julialang.org/downloads/). For now, only Linux is supported.
On Windows, you need to use the second generation Windows Subsystem for Linux (WSL2).
**If you're using Intel Arc GPUs (A580, A750, A770, etc), you need to use at least
Linux 6.2.** For other hardware, any recent Linux distribution should work.

Once you have installed Julia, proceed by entering the package manager REPL mode by pressing
`]` and adding theoneAPI package:
`]` and adding the oneAPI package:

```
pkg> add oneAPI
Expand All @@ -60,11 +60,12 @@ julia> using oneAPI

julia> oneAPI.versioninfo()
Binary dependencies:
- NEO: 24.26.30049+0
- NEO: 25.35.35096
- libigc: 1.0.17193+0
- gmmlib: 22.3.20+0
- SPIRV_LLVM_Translator: 20.1.0+1
- SPIRV_Tools: 2025.1.0+1
- SPIRV_LLVM_Translator: 21
- SPIRV_Tools: 2025.4.0
- oneAPI_Support: 0.9.2 (oneMKL v2025.2.0)

Toolchain:
- Julia: 1.11.5
Expand Down Expand Up @@ -219,6 +220,17 @@ julia> a .+ 1
1.87436 1.23285
```

The oneMKL integration provides extended support for linear algebra operations, including sparse
matrix operations that integrate with Julia's standard LinearAlgebra interface:

```julia
julia> using oneAPI, oneAPI.oneMKL, SparseArrays, LinearAlgebra
julia> A = sprand(100, 100, 0.1)
julia> dA = oneMKL.oneSparseMatrixCSC(A)
julia> x = oneArray(rand(100))
julia> y = dA * x # Matrix-vector multiplication via LinearAlgebra
```

### `Float64` support

Not all oneAPI GPUs support Float64 datatypes. You can test if your GPU does using
Expand Down