types.hpp File Reference

Fundamental CUDA-related type definitions. More...

#include "detail/optional.hpp"
#include "detail/span.hpp"
#include <builtin_types.h>
#include <cuda.h>
#include <type_traits>
#include <utility>
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <vector>
#include <stdexcept>

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Classes
struct	cuda::array::dimensions_t< NumDimensions >
	CUDA's array memory-objects are multi-dimensional; but their dimensions, or extents, are not the same as cuda::grid::dimensions_t ; they may be much larger in each axis. More...
struct	cuda::array::dimensions_t< 3 >
	Dimensions for 3D CUDA arrays. More...
struct	cuda::array::dimensions_t< 2 >
	Dimensions for 2D CUDA arrays. More...
struct	cuda::grid::dimensions_t
	A richer (kind-of-a-)wrapper for CUDA's dim3 class, used to specify dimensions for blocks (in terms of threads) and of grids(in terms of blocks, or overall). More...
struct	cuda::grid::composite_dimensions_t
	Composite dimensions for a grid - in terms of blocks, then also down into the block dimensions completing the information to the thread level. More...
struct	cuda::grid::overall_dimensions_t
	Dimensions of a grid in threads, i.e. More...

Namespaces
	cuda
	All definitions and functionality wrapping the CUDA Runtime API.
	cuda::event
	Definitions and functionality related to CUDA events (not including the event wrapper type event_t itself)
	cuda::stream
	Definitions and functionality related to CUDA streams (not including the device wrapper type stream_t itself)
	memory
	Representation, allocation and manipulation of CUDA-related memory, of different kinds.
	cuda::memory::device
	CUDA-Device-global memory on a single device (not accessible from the host)
	cuda::memory::managed
	This type of memory, also known as unified memory, appears within a unified, all-system address space - and is used with the same address range on the host and on all relevant CUDA devices on a system.
	cuda::device
	Definitions and functionality related to CUDA devices (not including the device wrapper type device_t itself)

Macros
#define	CUDA_API_WRAPPERS_COMMON_TYPES_HPP_
#define	__device__
#define	__host__
#define	CPP14_CONSTEXPR
#define	NOEXCEPT_IF_NDEBUG   noexcept(false)

Typedefs
using	cuda::status_t = CUresult
	Indicates either the result (success or error index) of a CUDA Runtime or Driver API call, or the overall status of the API (which is typically the last triggered error). More...
using	cuda::size_t = ::std::size_t
using	cuda::dimensionality_t = size_t
	The index or number of dimensions of an entity (as opposed to the extent in any dimension) - typically just 0, 1, 2 or 3.
using	cuda::array::dimension_t = size_t
using	cuda::event::handle_t = CUevent
	The CUDA Runtime API's numeric handle for events.
using	cuda::stream::handle_t = CUstream
	The CUDA API's handle for streams.
using	cuda::stream::priority_t = int
	CUDA streams have a scheduling priority, with lower values meaning higher priority. More...
using	cuda::grid::dimension_t = decltype(dim3::x)
	CUDA kernels are launched in grids of blocks of threads, in 3 dimensions. More...
using	cuda::grid::block_dimension_t = dimension_t
	CUDA kernels are launched in grids of blocks of threads, in 3 dimensions. More...
using	cuda::grid::block_dimensions_t = dimensions_t
	CUDA kernels are launched in grids of blocks of threads. More...
using	cuda::grid::overall_dimension_t = size_t
	Dimension of a grid in threads along one axis, i.e. More...
using	cuda::memory::pointer::attribute_t = CUpointer_attribute
using	cuda::memory::shared::size_t = unsigned
	Each physical core ("Symmetric Multiprocessor") on an nVIDIA GPU has a space of shared memory (see this blog entry ). More...
using	cuda::memory::managed::range_attribute_t = CUmem_range_attribute
using	cuda::device::id_t = CUdevice
	Numeric ID of a CUDA device used by the CUDA Runtime API. More...
using	cuda::device::attribute_t = CUdevice_attribute
	CUDA devices have both "attributes" and "properties". More...
using	cuda::device::attribute_value_t = int
	All CUDA device attributes (cuda::device::attribute_t) have a value of this type.
using	cuda::device::peer_to_peer::attribute_t = CUdevice_P2PAttribute
	While Individual CUDA devices have individual "attributes" (attribute_t), there are also attributes characterizing pairs; this type is used for identifying/indexing them.
using	cuda::context::handle_t = CUcontext
using	cuda::context::flags_t = unsigned
using	cuda::device::flags_t = context::flags_t
using	cuda::device::primary_context::handle_t = cuda::context::handle_t
using	cuda::device::host_thread_sync_scheduling_policy_t = context::host_thread_sync_scheduling_policy_t
using	cuda::native_word_t = unsigned
using	cuda::uuid_t = CUuuid
using	cuda::kernel::attribute_t = CUfunction_attribute
using	cuda::kernel::attribute_value_t = int
using	cuda::kernel::handle_t = CUfunction
template<typename T >
using	cuda::dynarray = ::std::vector< T >

Enumerations
enum	: priority_t { cuda::stream::default_priority = 0 }
enum	initial_visibility_t {   to_all_devices,   to_supporters_of_concurrent_managed_access }
enum	cuda::multiprocessor_cache_preference_t : ::std::underlying_type< CUfunc_cache_enum >::type {   cuda::multiprocessor_cache_preference_t::no_preference = CU_FUNC_CACHE_PREFER_NONE,   cuda::multiprocessor_cache_preference_t::equal_l1_and_shared_memory = CU_FUNC_CACHE_PREFER_EQUAL,   cuda::multiprocessor_cache_preference_t::prefer_shared_memory_over_l1 = CU_FUNC_CACHE_PREFER_SHARED,   cuda::multiprocessor_cache_preference_t::prefer_l1_over_shared_memory = CU_FUNC_CACHE_PREFER_L1,   none = no_preference,   equal = equal_l1_and_shared_memory,   prefer_shared = prefer_shared_memory_over_l1,   prefer_l1 = prefer_l1_over_shared_memory }
	L1-vs-shared-memory balance option. More...
enum	cuda::multiprocessor_shared_memory_bank_size_option_t : ::std::underlying_type< CUsharedconfig >::type {   device_default = CU_SHARED_MEM_CONFIG_DEFAULT_BANK_SIZE,   four_bytes_per_bank = CU_SHARED_MEM_CONFIG_FOUR_BYTE_BANK_SIZE,   eight_bytes_per_bank = CU_SHARED_MEM_CONFIG_EIGHT_BYTE_BANK_SIZE }
	A physical core (SM)'s shared memory has multiple "banks"; at most one datum per bank may be accessed simultaneously, while data in different banks can be accessed in parallel. More...
enum	cuda::context::host_thread_sync_scheduling_policy_t : unsigned int {   cuda::context::heuristic = CU_CTX_SCHED_AUTO,   cuda::context::default_ = heuristic,   cuda::context::spin = CU_CTX_SCHED_SPIN,   cuda::context::block = CU_CTX_SCHED_BLOCKING_SYNC,   cuda::context::yield = CU_CTX_SCHED_YIELD,   cuda::context::automatic = heuristic }
	Scheduling policies the Runtime API may use when the host-side thread it is running in needs to wait for results from a certain device. More...

Functions
constexpr bool	cuda::grid::operator== (composite_dimensions_t lhs, composite_dimensions_t rhs) noexcept
constexpr bool	cuda::grid::operator!= (composite_dimensions_t lhs, composite_dimensions_t rhs) noexcept
constexpr bool	cuda::grid::operator== (overall_dimensions_t lhs, overall_dimensions_t rhs) noexcept
constexpr bool	cuda::grid::operator!= (overall_dimensions_t lhs, overall_dimensions_t rhs) noexcept
constexpr overall_dimensions_t	cuda::grid::operator* (dimensions_t grid_dims, block_dimensions_t block_dims) noexcept
address_t	cuda::memory::device::address (const void *device_ptr) noexcept
	Return a pointers address as a numeric value of the type appropriate for device. More...
void *	cuda::memory::as_pointer (device::address_t address) noexcept

Detailed Description

Fundamental CUDA-related type definitions.

This is a common file for all definitions of fundamental CUDA-related types, some shared by different APIs.

Note

In this file you'll find several numeric or opaque handle types, e.g. for devices, streams and events. These are mostly to be ignored; they appear here to make interaction with the unwrapped API easier and to break dependencies in the code. Instead, this library offers wrapper classes for them, in separate header files. For example: stream.hpp contains a stream_t class with its unique stream handle. Those are the ones you will want to use - they are more convenient and safer.

Typedef Documentation

block_dimension_t

using cuda::grid::block_dimension_t = typedef dimension_t

CUDA kernels are launched in grids of blocks of threads, in 3 dimensions.

In each of these, the number of threads per block is specified in this type.

Note

Theoretically, CUDA could split the type for blocks per grid and threads per block, but for now they're the same.

At the time of writing, a grid dimension value cannot exceed 2^31 on any axis (even lower on the y and z axes), so signed 32-bit integers can may be safely narrowing-cast into from this type.

block_dimensions_t

using cuda::grid::block_dimensions_t = typedef dimensions_t

CUDA kernels are launched in grids of blocks of threads.

This expresses the dimensions of a block within such a grid, in terms of threads.

Todo:

Consider having both grid and block dims inhert from the same dimensions_t structure, but be incompatible, to prevent mis-casting one as the other.

dimension_t

using cuda::grid::dimension_t = typedef decltype(dim3::x)

CUDA kernels are launched in grids of blocks of threads, in 3 dimensions.

In each of these, the numbers of blocks per grid is specified in this type.

Note

Theoretically, CUDA could split the type for blocks per grid and threads per block, but for now they're the same.

overall_dimension_t

using cuda::grid::overall_dimension_t = typedef size_t

Dimension of a grid in threads along one axis, i.e.

a multiplication of a grid's block dimension and the grid's dimension in blocks, on some axis.

size_t

using cuda::memory::shared::size_t = typedef unsigned

Each physical core ("Symmetric Multiprocessor") on an nVIDIA GPU has a space of shared memory (see this blog entry ).

This type is large enough to hold its size.

Note

actually, uint16_t is usually large enough to hold the shared memory size (as of Volta/Turing architectures), but there are exceptions to this rule, so we have to go with the next smallest.

Todo:

consider using uint32_t.

Enumeration Type Documentation

host_thread_sync_scheduling_policy_t

enum cuda::context::host_thread_sync_scheduling_policy_t : unsigned int

Scheduling policies the Runtime API may use when the host-side thread it is running in needs to wait for results from a certain device.

Enumerator
heuristic	Default behavior; yield or spin based on a heuristic. The default value if the flags parameter is zero, uses a heuristic based on the number of active CUDA contexts in the process C and the number of logical processors in the system P. If C > P, then CUDA will yield to other OS threads when waiting for the device, otherwise CUDA will not yield while waiting for results and actively spin on the processor.
default_	Alias for the default behavior; see heuristic .
spin	Keep control and spin-check for result availability. Instruct CUDA to actively spin when waiting for results from the device. This can decrease latency when waiting for the device, but may lower the performance of CPU threads if they are performing work in parallel with the CUDA thread.
block	Block the thread until results are available. Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the device to finish work.
yield	Yield control while waiting for results. Instruct CUDA to yield its thread when waiting for results from the device. This can increase latency when waiting for the device, but can increase the performance of CPU threads performing work in parallel with the device.
automatic	see heuristic