CUDA driver

CuError(code)
CuError(code, meta)

Create a CUDA error object with error code code. The optional meta parameter indicates whether extra information, such as error logs, is known.

name(err::CuError)

Gets the string representation of an error code.

This name can often be used as a symbol in source code to get an instance of this error. For example:

julia> using CUDAdrv

julia> err = CuError(1)
CuError(1, ERROR_INVALID_VALUE)

julia> name(err)
"ERROR_INVALID_VALUE"

julia> CUDAdrv.ERROR_INVALID_VALUE
CuError(1, ERROR_INVALID_VALUE)

description(err::CuError)

Gets the string description of an error code.

version()

Returns the CUDA version as reported by the driver.

CuDevice(i::Integer)

Get a handle to a compute device.

devices()

Get an iterator for the compute devices.

name(dev::CuDevice)

Returns an identifier string for the device.

totalmem(dev::CuDevice)

Returns the total amount of memory (in bytes) on the device.

attribute(dev::CuDevice, code)

Returns information about the device.

attribute(X, ptr::Union{Ptr,CuPtr}, attr)

Returns attribute attr about pointer ptr. The type of the returned value depends on the attribute, and as such must be passed as the X parameter.

capability(dev::CuDevice)

Returns the compute capability of the device.

warpsize(dev::CuDevice)

Returns the warp size (in threads) of the device.

CuContext(dev::CuDevice, flags=CTX_SCHED_AUTO)
CuContext(f::Function, ...)

Create a CUDA context for device. A context on the GPU is analogous to a process on the CPU, with its own distinct address space and allocated resources. When a context is destroyed, the system cleans up the resources allocated to it.

When you are done using the context, call unsafe_destroy! to mark it for deletion, or use do-block syntax with this constructor.

CuCurrentContext()

Return the current context, or nothing if there is no active context.

activate(ctx::CuContext)

Binds the specified CUDA context to the calling CPU thread.

synchronize()

Block for the current context's tasks to complete.

device()
device(ctx::CuContext)

Returns the device for a context.

CuPrimaryContext(dev::CuDevice)

Create a primary CUDA context for a given device.

Each primary context is unique per device and is shared with CUDA runtime API. It is meant for interoperability with (applications using) the runtime API.

CuContext(pctx::CuPrimaryContext)

Retain the primary context on the GPU, returning a context compatible with the driver API. The primary context will be released when the returned driver context is finalized.

As these contexts are refcounted by CUDA, you should not call unsafe_destroy! on them but use unsafe_release! instead (available with do-block syntax as well).

isactive(pctx::CuPrimaryContext)

Query whether a primary context is active.

flags(pctx::CuPrimaryContext)

Query the flags of a primary context.

setflags!(pctx::CuPrimaryContext)

Set the flags of a primary context.

CuModule(data, options::Dict{CUjit_option,Any})
CuModuleFile(path, options::Dict{CUjit_option,Any})

Create a CUDA module from a data, or a file containing data. The data may be PTX code, a CUBIN, or a FATBIN.

The options is an optional dictionary of JIT options and their respective value.

CuFunction(mod::CuModule, name::String)

Acquires a function handle from a named function in a module.

CuGlobal{T}(mod::CuModule, name::String)

Acquires a typed global variable handle from a named global in a module.

eltype(var::CuGlobal)

Return the element type of a global variable object.

getindex(type[, elements...])

Construct a 1-d array of the specified type. This is usually called with the syntax Type[]. Element values can be specified using Type[a,b,c,...].

Examples

julia> Int8[1, 2, 3]
3-element Array{Int8,1}:
 1
 2
 3

julia> getindex(Int8, 1, 2, 3)
3-element Array{Int8,1}:
 1
 2
 3

Base.getindex(var::CuGlobal)

Return the current value of a global variable.

Base.setindex(var::CuGlobal{T}, val::T)

Set the value of a global variable to val

CuLink()

Creates a pending JIT linker invocation.

add_data!(link::CuLink, name::String, code::String)

Add PTX code to a pending link operation.

add_data!(link::CuLink, name::String, data::Vector{UInt8}, type::CUjitInputType)

Add object code to a pending link operation.

add_file!(link::CuLink, path::String, typ::CUjitInputType)

Add data from a file to a link operation. The argument typ indicates the type of the contained data.

The result of a linking operation.

This object keeps its parent linker object alive, as destroying a linker destroys linked images too.

complete(link::CuLink)

Complete a pending linker invocation, returning an output image.

CuModule(img::CuLinkImage, ...)

Create a CUDA module from a completed linking operation. Options from CuModule apply.

Mem.DeviceBuffer
Mem.Device

A buffer of device memory residing on the GPU.

Mem.alloc(DeviceBuffer, bytesize::Integer)

Allocate bytesize bytes of memory on the device. This memory is only accessible on the GPU, and requires explicit calls to unsafe_copyto!, which wraps cuMemcpy, for access on the CPU.

Mem.HostBuffer
Mem.Host

A buffer of pinned memory on the CPU, possible accessible on the GPU.

Mem.alloc(HostBuffer, bytesize::Integer, [flags])

Allocate bytesize bytes of page-locked memory on the host. This memory is accessible from the CPU, and makes it possible to perform faster memory copies to the GPU. Furthermore, if flags is set to HOSTALLOC_DEVICEMAP the memory is also accessible from the GPU. These accesses are direct, and go through the PCI bus. If flags is set to HOSTALLOC_PORTABLE, the memory is considered mapped by all CUDA contexts, not just the one that created the memory, which is useful if the memory needs to be accessed from multiple devices. Multiple flags can be set at one time using a bytewise OR:

flags = HOSTALLOC_PORTABLE | HOSTALLOC_DEVICEMAP

Mem.register(HostBuffer, ptr::Ptr, bytesize::Integer, [flags])

Page-lock the host memory pointed to by ptr. Subsequent transfers to and from devices will be faster, and can be executed asynchronously. If the HOSTREGISTER_DEVICEMAP flag is specified, the buffer will also be accessible directly from the GPU. These accesses are direct, and go through the PCI bus. If the HOSTREGISTER_PORTABLE flag is specified, any CUDA context can access the memory.

Mem.unregister(HostBuffer)

Unregisters a memory range that was registered with Mem.register.

Mem.UnifiedBuffer
Mem.Unified

A managed buffer that is accessible on both the CPU and GPU.

Mem.alloc(UnifiedBuffer, bytesize::Integer, [flags::CUmemAttach_flags])

Allocate bytesize bytes of unified memory. This memory is accessible from both the CPU and GPU, with the CUDA driver automatically copying upon first access.

prefetch(::UnifiedBuffer, [bytes::Integer]; [device::CuDevice], [stream::CuStream])

Prefetches memory to the specified destination device.

advise(::UnifiedBuffer, advice::CUDAdrv.CUmem_advise, [bytes::Integer]; [device::CuDevice])

Advise about the usage of a given memory range.

available_memory()

Returns the available_memory amount of memory (in bytes), available for allocation by the CUDA context.

total_memory()

Returns the total amount of memory (in bytes), available for allocation by the CUDA context.

CuStream(; flags=STREAM_DEFAULT, priority=nothing)

Create a CUDA stream.

CuDefaultStream()

Return the default stream.

synchronize(s::CuStream)

Wait until a stream's tasks are completed.

CuEvent()

Create a new CUDA event.

record(e::CuEvent, stream=CuDefaultStream())

Record an event on a stream.

synchronize(e::CuEvent)

Waits for an event to complete.

elapsed(start::CuEvent, stop::CuEvent)

Computes the elapsed time between two events (in seconds).

@elapsed stream ex
@elapsed ex

A macro to evaluate an expression, discarding the resulting value, instead returning the number of seconds it took to execute on the GPU, as a floating-point number.

CuDim3(x)

CuDim3((x,))
CuDim3((x, y))
CuDim3((x, y, x))

A type used to specify dimensions, consisting of 3 integers for respectively the x, y and z dimension. Unspecified dimensions default to 1.

Often accepted as argument through the CuDim type alias, eg. in the case of cudacall or launch, allowing to pass dimensions as a plain integer or a tuple without having to construct an explicit CuDim3 object.

cudacall(f::CuFunction, types, values...; blocks::CuDim, threads::CuDim,
         cooperative=false, shmem=0, stream=CuDefaultStream())

ccall-like interface for launching a CUDA function f on a GPU.

For example:

vadd = CuFunction(md, "vadd")
a = rand(Float32, 10)
b = rand(Float32, 10)
ad = Mem.alloc(DeviceBuffer, 10*sizeof(Float32))
unsafe_copyto!(ad, convert(Ptr{Cvoid}, a), 10*sizeof(Float32)))
bd = Mem.alloc(DeviceBuffer, 10*sizeof(Float32))
unsafe_copyto!(bd, convert(Ptr{Cvoid}, b), 10*sizeof(Float32)))
c = zeros(Float32, 10)
cd = Mem.alloc(DeviceBuffer, 10*sizeof(Float32))

cudacall(vadd, (CuPtr{Cfloat},CuPtr{Cfloat},CuPtr{Cfloat}), ad, bd, cd; threads=10)
unsafe_copyto!(convert(Ptr{Cvoid}, c), cd, 10*sizeof(Float32)))

The blocks and threads arguments control the launch configuration, and should both consist of either an integer, or a tuple of 1 to 3 integers (omitted dimensions default to 1). The types argument can contain both a tuple of types, and a tuple type, the latter being slightly faster.

launch(f::CuFunction; args...; blocks::CuDim=1, threads::CuDim=1,
       cooperative=false, shmem=0, stream=CuDefaultStream())

Low-level call to launch a CUDA function f on the GPU, using blocks and threads as respectively the grid and block configuration. Dynamic shared memory is allocated according to shmem, and the kernel is launched on stream stream.

Arguments to a kernel should either be bitstype, in which case they will be copied to the internal kernel parameter buffer, or a pointer to device memory.

This is a low-level call, prefer to use cudacall instead.

@profile ex

Run expressions while activating the CUDA profiler.

Note that this API is used to programmatically control the profiling granularity by allowing profiling to be done only on selective pieces of code. It does not perform any profiling on itself, you need external tools for that.

start()

Enables profile collection by the active profiling tool for the current context. If profiling is already enabled, then this call has no effect.

stop()

Disables profile collection by the active profiling tool for the current context. If profiling is already disabled, then this call has no effect.