@kernel config function f(args) endThis allows for two different configurations:
cpu={true, false}: Disables code-generation of the CPU function. This relaxes semantics such that KernelAbstractions primitives can be used in non-kernel functions.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!This is an experimental feature.
KernelAbstractions.@Const — Macro@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.
Violating those constraints will lead to arbitrary behaviour.
As an example given a kernel signature kernel(A, @Const(B)), you are not allowed to call the kernel with kernel(A, A) or kernel(A, view(A, :)).
KernelAbstractions.@index — Macro@indexThe @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)
+synchronize(backend)@kernel config function f(args) endThis allows for two different configurations:
cpu={true, false}: Disables code-generation of the CPU function. This relaxes semantics such that KernelAbstractions primitives can be used in non-kernel functions.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!This is an experimental feature.
KernelAbstractions.@Const — Macro@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.
Violating those constraints will lead to arbitrary behaviour.
As an example given a kernel signature kernel(A, @Const(B)), you are not allowed to call the kernel with kernel(A, A) or kernel(A, view(A, :)).
KernelAbstractions.@index — Macro@indexThe @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)KernelAbstractions.@localmem — Macro@localmem T dimsDeclare storage that is local to a workgroup.
KernelAbstractions.@private — Macro@private T dimsDeclare 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 = 1Creates a private local of mem per item in the workgroup. This can be safely used across @synchronize statements.
KernelAbstractions.@synchronize — Macro@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.KernelAbstractions.@print — Macro@print(items...)This is a unified print statement.
Platform differences
GPU: This will reorganize the items to print via @cuprintfCPU: This will call print(items...)KernelAbstractions.@uniform — Macro@uniform exprexpr is evaluated outside the workitem scope. This is useful for variable declarations that span workitems, or are reused across @synchronize statements.
KernelAbstractions.@groupsize — Macro@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()).
KernelAbstractions.@ndrange — Macro@ndrange()Query the ndrange on the backend. This function returns a tuple corresponding to kernel configuration.
KernelAbstractions.synchronize — Functionsynchronize(::Backend)Synchronize the current backend.
Backend implementations must implement this function.
KernelAbstractions.allocate — Functionallocate(::Backend, Type, dims...)::AbstractArrayAllocate a storage array appropriate for the computational backend.
Backend implementations must implement allocate(::NewBackend, T, dims::Tuple)
KernelAbstractions.zeros — Functionzeros(::Backend, Type, dims...)::AbstractArrayAllocate a storage array appropriate for the computational backend filled with zeros.
KernelAbstractions.Kernel — TypeKernel{Backend, WorkgroupSize, NDRange, Func}Kernel closure struct that is used to represent the backend kernel on the host. WorkgroupSize is the number of workitems in a workgroup.
Backend implementations must implement:
(kernel::Kernel{<:NewBackend})(args...; ndrange=nothing, workgroupsize=nothing)As well as the on-device functionality.
KernelAbstractions.partition — FunctionPartition a kernel for the given ndrange and workgroupsize.
KernelAbstractions.@context — Macro@context()Access the hidden context object used by KernelAbstractions.
Only valid to be used from a kernel with cpu=false.
function f(@context, a)
+@index(Global)KernelAbstractions.@localmem — Macro@localmem T dimsDeclare storage that is local to a workgroup.
KernelAbstractions.@private — Macro@private T dimsDeclare 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 = 1Creates a private local of mem per item in the workgroup. This can be safely used across @synchronize statements.
KernelAbstractions.@synchronize — Macro@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.KernelAbstractions.@print — Macro@print(items...)This is a unified print statement.
Platform differences
GPU: This will reorganize the items to print via @cuprintfCPU: This will call print(items...)KernelAbstractions.@uniform — Macro@uniform exprexpr is evaluated outside the workitem scope. This is useful for variable declarations that span workitems, or are reused across @synchronize statements.
KernelAbstractions.@groupsize — Macro@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()).
KernelAbstractions.@ndrange — Macro@ndrange()Query the ndrange on the backend. This function returns a tuple corresponding to kernel configuration.
KernelAbstractions.synchronize — Functionsynchronize(::Backend)Synchronize the current backend.
Backend implementations must implement this function.
KernelAbstractions.allocate — Functionallocate(::Backend, Type, dims...)::AbstractArrayAllocate a storage array appropriate for the computational backend.
Backend implementations must implement allocate(::NewBackend, T, dims::Tuple)
KernelAbstractions.zeros — Functionzeros(::Backend, Type, dims...)::AbstractArrayAllocate a storage array appropriate for the computational backend filled with zeros.
KernelAbstractions.Kernel — TypeKernel{Backend, WorkgroupSize, NDRange, Func}Kernel closure struct that is used to represent the backend kernel on the host. WorkgroupSize is the number of workitems in a workgroup.
Backend implementations must implement:
(kernel::Kernel{<:NewBackend})(args...; ndrange=nothing, workgroupsize=nothing)As well as the on-device functionality.
KernelAbstractions.partition — FunctionPartition a kernel for the given ndrange and workgroupsize.
KernelAbstractions.@context — Macro@context()Access the hidden context object used by KernelAbstractions.
Only valid to be used from a kernel with cpu=false.
function f(@context, a)
I = @index(Global, Linear)
a[I]
end
@kernel cpu=false function my_kernel(a)
f(@context, a)
-endSettings
This document was generated with Documenter.jl version 1.8.0 on Wednesday 8 January 2025. Using Julia version 1.11.2.