Julia support for the oneAPI programming toolkit.
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.
The current version of oneAPI.jl supports most of the 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. The integration with vendor libraries like oneMKL or oneDNN is still in development, and as result certain array operations may be unavailable or slow.
You need to use Julia 1.8 or higher, and it is strongly advised to use the official binaries. 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:
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, perform a smoke test by calling the versioninfo() function:
julia> using oneAPI
julia> oneAPI.versioninfo()
Binary dependencies:
- NEO_jll: 22.43.24595+0
- libigc_jll: 1.0.12504+0
- gmmlib_jll: 22.3.0+0
- SPIRV_LLVM_Translator_unified_jll: 0.2.0+0
- SPIRV_Tools_jll: 2022.1.0+0
Toolchain:
- Julia: 1.8.5
- LLVM: 13.0.1
1 driver:
- 00000000-0000-0000-173d-d94201036013 (v1.3.24595, API v1.3.0)
2 devices:
- Intel(R) Graphics [0x56a0]
- Intel(R) HD Graphics P630 [0x591d]If you have multiple compatible drivers or devices, use the driver! and device!
functions to configure which one to use in the current task:
julia> devices()
ZeDevice iterator for 2 devices:
1. Intel(R) Graphics [0x56a0]
2. Intel(R) HD Graphics P630 [0x591d]
julia> device()
ZeDevice(GPU, vendor 0x8086, device 0x56a0): Intel(R) Graphics [0x56a0]
julia> device!(2)
ZeDevice(GPU, vendor 0x8086, device 0x591d): Intel(R) HD Graphics P630 [0x591d]To ensure other functionality works as expected, you can run the test suite from the package manager REPL mode. Note that this will pull and run the test suite for GPUArrays, which takes quite some time:
pkg> test oneAPI
...
Testing finished in 16 minutes, 27 seconds, 506 milliseconds
Test Summary: | Pass Total Time
Overall | 4945 4945
SUCCESS
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)
endBuilt 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 1.23285Not all oneAPI GPUs support Float64 datatypes. You can test if your GPU does using the following code:
julia> using oneAPI
julia> oneL0.module_properties(device()).fp64flags & oneL0.ZE_DEVICE_MODULE_FLAG_FP64 == oneL0.ZE_DEVICE_MODULE_FLAG_FP64
falseIf your GPU doesn't, executing code that relies on Float64 values will result in an error:
julia> oneArray([1.]) .+ 1
┌ Error: Module compilation failed:
│
│ error: Double type is not supported on this platform.To work on oneAPI.jl, you just need to dev the package. In addition, you may need to
build the binary support library that's used to interface with oneMKL and other C++
vendor libraries. This library is normally provided by the oneAPI_Support_jll.jl package,
however, we only guarantee to update this package when releasing oneAPI.jl. You can build
this library yourself by simply executing deps/build_local.jl.
To facilitate development, there are other things you may want to configure:
The oneAPI Level Zero libraries feature a so-called validation layer, which validates the arguments to API calls. This can be useful to spot potential isssues, and can be enabled by setting the following environment variables:
ZE_ENABLE_VALIDATION_LAYER=1ZE_ENABLE_PARAMETER_VALIDATION=1EnableDebugBreak=0(this is needed to work around intel/compute-runtime#639)
If you're experiencing an issue with the underlying toolchain (NEO, IGC, etc), you may
want to use a debug build of these components, which also perform additional
validation. This can be done simply by calling oneAPI.set_debug!(true) and restarting
your Julia session. This sets a preference used by the respective JLL packages.
To further debug the toolchain, you may need a custom build and point oneAPI.jl towards it.
This can also be done using preferences, overriding the paths to resources provided by the
various JLLs that oneAPI.jl uses. A helpful script to automate this is provided in the
res folder of this repository:
$ julia res/local.jl
Trying to find local IGC...
- found libigc at /usr/local/lib/libigc.so
- found libiga64 at /usr/local/lib/libiga64.so
- found libigdfcl at /usr/local/lib/libigdfcl.so
- found libopencl-clang at /usr/local/lib/libopencl-clang.so.11
Trying to find local gmmlib...
- found libigdgmm at /usr/local/lib/libigdgmm.so
Trying to find local NEO...
- found libze_intel_gpu.so.1 at /usr/local/lib/libze_intel_gpu.so.1
- found libigdrcl at /usr/local/lib/intel-opencl/libigdrcl.so
Trying to find local oneAPI loader...
- found libze_loader at /lib/x86_64-linux-gnu/libze_loader.so
- found libze_validation_layer at /lib/x86_64-linux-gnu/libze_validation_layer.so
Writing preferences...
The discovered paths will be written to a global file with preferences, typically
$HOME/.julia/environments/vX.Y/LocalPreferences.toml (where vX.Y refers to the Julia
version you are using). You can modify this file, or remove it when you want to revert to
default set of binaries.
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