API

@kernel function f(args) end

Takes a function definition and generates a Kernel constructor from it. The enclosed function is allowed to contain kernel language constructs. In order to call it the kernel has first to be specialized on the backend and then invoked on the arguments.

Kernel language

• @Const
• @index
• @groupsize
• @ndrange
• @localmem
• @private
• @uniform
• @synchronize
• @print

Kernel constructor

After defining a kernel function f, call f(backend[, workgroupsize[, ndrange]]) to obtain a Kernel specialized for that backend. Workgroup size and ndrange can be fixed at construction time (enabling size-specific compile-time optimizations and fewer runtime checks, at the cost of recompilation when the sizes change) or supplied at launch:

f(backend)                    # dynamic workgroup size and ndrange
f(backend, 64)                # static workgroup size of 64
f(backend, 64, 1024)          # static workgroup size and ndrange
f(backend, 64, (128, 128))    # multi-dimensional ndrange

Example

using KernelAbstractions

@kernel function vecadd(A, @Const(B))
    I = @index(Global)
    @inbounds A[I] += B[I]
end

dev = CPU()
A = ones(1024)
B = rand(1024)
vecadd(dev, 64)(A, B, ndrange=length(A))
synchronize(dev)

@kernel config function f(args) end

This allows for two different configurations:

1. cpu={true, false}: Disables code-generation of the CPU function. This relaxes semantics such that KernelAbstractions primitives can be used in non-kernel functions.
2. inbounds={false, true}: Enables a forced @inbounds macro around the function definition in the case the user is using too many @inbounds already in their kernel. Note that this can lead to incorrect results, crashes, etc and is fundamentally unsafe. Be careful!
3. unsafe_indices={false, true}: Disables the implicit validation of indices, users must avoid @index(Global).

• @context

@Const(A)

@Const is an argument annotiation that asserts that the memory reference by A is both not written to as part of the kernel and that it does not alias any other memory in the kernel.

@index

The @index macro can be used to give you the index of a workitem within a kernel function. It supports both the production of a linear index or a cartesian index. A cartesian index is a general N-dimensional index that is derived from the iteration space.

Index granularity

• Global: Used to access global memory.
• Group: The index of the workgroup.
• Local: The within workgroup index.

Index kind

• Linear: Produces an Int64 that can be used to linearly index into memory.
• Cartesian: Produces a CartesianIndex{N} that can be used to index into memory.
• NTuple: Produces a NTuple{N} that can be used to index into memory.

If the index kind is not provided it defaults to Linear, this is subject to change.

Examples

@index(Global, Linear)
@index(Global, Cartesian)
@index(Local, Cartesian)
@index(Group, Linear)
@index(Local, NTuple)
@index(Global)

@localmem T dims

Declare storage that is local to a workgroup.

@private T dims

Declare storage that is local to each item in the workgroup. This can be safely used across @synchronize statements. On a CPU, this will allocate additional implicit dimensions to ensure correct localization.

For storage that only persists between @synchronize statements, an MArray can be used instead.

See also @uniform.

@private mem = 1

Creates a private local of mem per item in the workgroup. This can be safely used across @synchronize statements.

@synchronize()

After a @synchronize statement all read and writes to global and local memory from each thread in the workgroup are visible in from all other threads in the workgroup.

@synchronize(cond)

After a @synchronize statement all read and writes to global and local memory from each thread in the workgroup are visible in from all other threads in the workgroup. cond is not allowed to have any visible sideffects.

Platform differences

• GPU: This synchronization will only occur if the cond evaluates.
• CPU: This synchronization will always occur.

@print(items...)

This is a unified print statement.

Platform differences

• GPU: This will reorganize the items to print via @cuprintf
• CPU: This will call print(items...)

@uniform expr

expr is evaluated outside the workitem scope. This is useful for variable declarations that span workitems, or are reused across @synchronize statements.

@groupsize()

Query the workgroupsize on the backend. This function returns a tuple corresponding to kernel configuration. In order to get the total size you can use prod(@groupsize()).

@ndrange()

Query the ndrange on the backend. This function returns a tuple corresponding to kernel configuration.

Backend

Abstract supertype for all KernelAbstractions backends.

Concrete backends (for example CUDABackend from CUDA.jl or CPU from this package) determine where arrays are allocated and where kernels execute. Use get_backend to obtain the backend for an array and allocate to create storage on a backend.

Example

backend = get_backend(A)
kernel = my_kernel(backend, 256)
kernel(A, ndrange=length(A))
synchronize(backend)

Abstract type for all GPU based KernelAbstractions backends.

CPU

Type alias for POCLBackend, the CPU execution backend.

Construct with CPU() (equivalent to POCLBackend()). Kernels run on the host via POCL/OpenCL using the same programming model as GPU backends, which is useful for debugging and for running kernel code without a GPU.

Example

A = ones(Float32, 1024)
mul2_kernel(CPU(), 64)(A, ndrange=length(A))
synchronize(CPU())

POCLBackend()

CPU backend that compiles kernels to OpenCL via POCL and executes them on the host. This is the concrete type behind the CPU alias.

get_backend(A::AbstractArray)::Backend

Get a Backend instance suitable for array A.

allocate(::Backend, Type, dims...; unified=false)::AbstractArray

Allocate a storage array appropriate for the computational backend. unified=true allocates an array using unified memory if the backend supports it and throws otherwise. Use supports_unified to determine whether it is supported by a backend.

zeros(::Backend, Type, dims...; unified=false)::AbstractArray

Allocate a storage array appropriate for the computational backend filled with zeros. unified=true allocates an array using unified memory if the backend supports it and throws otherwise.

ones(::Backend, Type, dims...; unified=false)::AbstractArray

Allocate a storage array appropriate for the computational backend filled with ones. unified=true allocates an array using unified memory if the backend supports it and throws otherwise.

copyto!(::Backend, dest::AbstractArray, src::AbstractArray)

Perform an asynchronous copyto! operation that is execution ordered with respect to the back-end.

For most users, Base.copyto! should suffice, performance a simple, synchronous copy. Only when you know you need asynchronicity w.r.t. the host, you should consider using this asynchronous version, which requires additional lifetime guarantees as documented below.

arr = zeros(64)
GC.@preserve arr begin
    copyto!(backend, arr, ...)
    # other operations
    synchronize(backend)
end

pagelock!(::Backend, dest::AbstractArray)

Pagelock (pin) a host memory buffer for a backend device. This may be necessary for copyto! to perform asynchronously w.r.t to the host/

This function should return nothing; or missing if not implemented.

unsafe_free!(x::AbstractArray)

Release the memory of an array for reuse by future allocations and reduce pressure on the allocator. After releasing the memory of an array, it should no longer be accessed.

functional(::Backend)

Queries if the provided backend is functional. This may mean different things for different backends, but generally should mean that the necessary drivers and a compute device are available.

This function should return a Bool or missing if not implemented.

versioninfo(io::IO=stdout, backend::Backend)::Nothing

Print information about backend to io. It is up to the backends to determine what is relevant.

supports_unified(::Backend)::Bool

Returns whether unified memory arrays are supported by the backend.

supports_atomics(::Backend)::Bool

Returns whether @atomic operations are supported by the backend.

supports_float64(::Backend)::Bool

Returns whether Float64 values are supported by the backend.

synchronize(::Backend)

Synchronize the current backend.

device(backend::Backend)::Int

Return the 1-based index of the currently active device for backend.

ndevices(backend::Backend)::Int

Return the number of devices available to backend.

device!(backend::Backend, id::Int)

Select the active device for backend. id is a 1-based device index and must satisfy 1 <= id <= ndevices(backend).

Example

device!(CUDABackend(), 2)  # use the second CUDA device

priority!(::Backend, prio::Symbol)

Set the priority for the backend stream/queue. This is an optional feature that backends may or may not implement. If a backend shall support priorities it must accept :high, :normal, :low. Where :normal is the default.

Kernel{Backend, WorkgroupSize, NDRange, Func}

Host-side handle for a kernel specialized on a backend, workgroup size, and ndrange.

Kernels are created by calling a @kernel function on a backend, for example my_kernel(CUDABackend(), 256). The returned object is callable:

kernel = my_kernel(backend, 64)
kernel(A, B, ndrange=length(A))   # launch asynchronously
synchronize(backend)

Use workgroupsize, ndrange, and backend to inspect a kernel's static configuration.

(kernel::Kernel{<:NewBackend})(args...; ndrange=nothing, workgroupsize=nothing)

workgroupsize(kernel::Kernel)

Return the static workgroup size type parameter of kernel (StaticSize or DynamicSize).

ndrange(ctx)

Return the launch ndrange as a tuple.

backend(kernel::Kernel)

Return the Backend that kernel was constructed for.

@ka_code_typed [kwargs...] kernel(args...; ndrange=..., workgroupsize=...)

Return the typed IR for a kernel's device function, similar to InteractiveUtils.code_typed.

Pass interactive=true to descend into the IR with Cthulhu (must be loaded in the session). If ndrange is fixed at kernel construction time, it can be omitted at the call site.

Examples

@ka_code_typed my_kernel(backend)(A, ndrange=length(A))
@ka_code_typed my_kernel(backend, 64)(A, ndrange=length(A))
@ka_code_typed optimize=false my_kernel(backend)(A, ndrange=length(A))
@ka_code_typed interactive=true my_kernel(CPU())(A, ndrange=length(A))

@ka_code_llvm [kwargs...] kernel(args...; ndrange=..., workgroupsize=...)

Return the LLVM IR for a kernel's device function, similar to InteractiveUtils.code_llvm.

Only supported on the CPU backend. GPU kernels will throw an error.

Examples

@ka_code_llvm my_kernel(CPU())(A, ndrange=length(A))
@ka_code_llvm my_kernel(CPU(), 64)(A, ndrange=length(A))

partition(kernel, ndrange, workgroupsize)

Partition the iteration space of kernel into workgroups.

Returns the blocked iteration space and whether dynamic bounds-checking is required for the last (possibly partial) workgroup. Primarily used by backend implementations and tests.

@context()

Access the hidden context object used by KernelAbstractions.

function f(@context, a)
    I = @index(Global, Linear)
    a[I]
end

@kernel cpu=false function my_kernel(a)
    f(@context, a)
end

argconvert(kernel::Kernel, arg)

Convert arg to the device-side representation expected by kernel's backend.

Backend implementations define methods for their array and scalar types. This is called automatically when a kernel is launched.

DynamicSize

Marker type indicating that a kernel's workgroup size or ndrange is chosen at launch time.

StaticSize{S}

Marker type encoding a compile-time workgroup size or ndrange as a tuple S.