To ensure that the grid size is large enough to provide work for all processors. Many threads simultaneously, but they must be from the same thread block. Thread blocks are then grouped into grids, whichĬan also be one-, two-, or three-dimensional.ĬUDA devices often contain multiple separate processors. With multi-dimensional data such as images. Thread blocks can be one-, two-, or three-dimensional, which is helpful when working These threads canīe grouped into thread blocks, which share an area of fast hardware memory known as shared Grids, Blocks and Threads:ĬUDA devices typically execute kernel functions on many threads in parallel.
Submitted, and may interleave with work from other streams. Work within a single stream will execute sequentially in the order that it was Kernels and the two terms will be used interchangeably.Ī Stream is akin to a thread - asynchronous work such as kernel execution can be queued into a One might load a function from a shared-object file and call it. Functions can be loaded from modules and launched on a device as Each context is associated with a single device.Ī Module is similar to a shared-object library - it is a piece of compiled code which exportsįunctions and global values. Contexts, Modules, Streams and Functions:Ī CUDA context is akin to a process on the host - it contains all of the state for working withĪ device, all memory allocations, etc. Must be transferred from host memory to device memory before the device can use it forĬomputations, and the results must then be transferred back to host memory. The host is the CPU and its associated memory space. A device refers to a CUDA-capable GPU or similar device and itsĪssociated external memory space. This crate and its documentation uses the terms “device” and “host” frequently, so it’s worthĮxplaining them in more detail.
#DIM3 CUDA SAMPLE CODE DRIVER#
This crate provides a safe, user-friendly wrapper around the CUDA Driver API.
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