Julia support for the oneAPI programming toolkit.

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oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux.

This package is under heavy development, and not ready for general use.

You need to use Julia 1.6 or higher, and it is strongly advised to use the official binaries. For now, only Linux is supported.

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

pkg> add oneAPI

This installation will take a couple of minutes to download necessary binaries, such as the oneAPI loader, several SPIR-V tools, etc. For now, the oneAPI.jl package also depends on the Intel implementation of the oneAPI spec. That means you need compatible hardware; refer to the Intel documentation for more details.

Once you have oneAPI.jl installed, you can perform a smoke-test using the low-level wrappers for the Level Zero library:

julia> using oneAPI

julia> using oneAPI.oneL0

julia> drv = first(drivers());

julia> dev = first(devices(drv))
ZeDevice(GPU, vendor 0x8086, device 0x1912): Intel(R) Gen9

To ensure other functionality works as expected, you can run the test suite from the package manager REPL mode:

pkg> test oneAPI

Test Summary: | Pass  Total
oneAPI        |   46     46
    Testing oneAPI tests passed

The functionality of oneAPI.jl is organized as follows:

• low-level wrappers for the Level Zero library
• kernel programming capabilities
• abstractions for high-level array programming

The level zero wrappers are available in the oneL0 submodule, and expose all flexibility of the underlying APIs with user-friendly wrappers:

julia> using oneAPI, oneAPI.oneL0

julia> drv = first(drivers());

julia> ctx = ZeContext(drv);

julia> dev = first(devices(drv))
ZeDevice(GPU, vendor 0x8086, device 0x1912): Intel(R) Gen9

julia> compute_properties(dev)
(maxTotalGroupSize = 256, maxGroupSizeX = 256, maxGroupSizeY = 256, maxGroupSizeZ = 256, maxGroupCountX = 4294967295, maxGroupCountY = 4294967295, maxGroupCountZ = 4294967295, maxSharedLocalMemory = 65536, subGroupSizes = (8, 16, 32))

julia> queue = ZeCommandQueue(ctx, dev);

julia> execute!(queue) do list
         append_barrier!(list)
       end

Built on top of that, are kernel programming capabilities for executing Julia code on oneAPI accelerators. For now, we reuse OpenCL intrinsics, and compile to SPIR-V using Khronos' translator:

julia> function kernel()
         barrier()
         return
       end

julia> @oneapi items=1 kernel()

Code reflection macros are available to see the generated code:

julia> @device_code_llvm @oneapi items=1 kernel()

;  @ REPL[18]:1 within `kernel'
define dso_local spir_kernel void @_Z17julia_kernel_3053() local_unnamed_addr {
top:
;  @ REPL[18]:2 within `kernel'
; ┌ @ oneAPI.jl/src/device/opencl/synchronization.jl:9 within `barrier' @ oneAPI.jl/src/device/opencl/synchronization.jl:9
; │┌ @ oneAPI.jl/src/device/opencl/utils.jl:34 within `macro expansion'
    call void @_Z7barrierj(i32 0)
; └└
;  @ REPL[18]:3 within `kernel'
  ret void
}

julia> @device_code_spirv @oneapi items=1 kernel()

; SPIR-V
; Version: 1.0
; Generator: Khronos LLVM/SPIR-V Translator; 14
; Bound: 9
; Schema: 0
               OpCapability Addresses
               OpCapability Kernel
          %1 = OpExtInstImport "OpenCL.std"
               OpMemoryModel Physical64 OpenCL
               OpEntryPoint Kernel %4 "_Z17julia_kernel_3067"
               OpSource OpenCL_C 200000
               OpName %top "top"
       %uint = OpTypeInt 32 0
     %uint_2 = OpConstant %uint 2
     %uint_0 = OpConstant %uint 0
       %void = OpTypeVoid
          %3 = OpTypeFunction %void
          %4 = OpFunction %void None %3
        %top = OpLabel
               OpControlBarrier %uint_2 %uint_2 %uint_0
               OpReturn
               OpFunctionEnd

Finally, the oneArray type makes it possible to use your oneAPI accelerator without the need to write custom kernels, thanks to Julia's high-level array abstractions:

julia> a = oneArray(rand(Float32, 2,2))
2×2 oneArray{Float32,2}:
 0.592979  0.996154
 0.874364  0.232854

julia> a .+ 1
2×2 oneArray{Float32,2}:
 1.59298  1.99615
 1.87436  1.23285

The current version of oneAPI.jl supports most of oneAPI Level Zero interface, has good kernel programming capabilties, and as a demonstration of that it fully implements 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. There is no integration with vendor libraries like oneMKL or oneDNN, and as a result certain operations (like matrix multiplication) will be unavailable or slow.