Work_dim in NDRange

I can not understand what work_dim is 开发者_运维百科for in clEnqueueNDRangeKernel()?

So, what is the difference between work_dim=1 and work_dim=2? And why work items are grouped into work groups? A work item or a work group is a thread running on the device (or neither)?

Thanks ahead!

work_dim is the number of dimensions for the clEnqueueNDRangeKernel() execution.

If you specify work_dim = 1, then the global and local work sizes are unidimensional. Thus, inside the kernels you can only access info in the first dimension, e.g. get_global_id(0), etc.

If you specify work_dim = 2 or 3, then you must also specify 2 or 3 dimensional global and local worksizes; in such case, you can access info inside the kernels in 2 or 3 dimensions, e.g. get_global_id(1), or get_group_id(2).

In practice you can do everything in 1D, but for dealing with 2D or 3D data, it maybe simpler to directly use 2/3 dimensional kernels; for example, in the case of 2D data, such as an image, if each thread/work-item is to deal with a single pixel, each thread/work-item could deal with the pixel at coordinates (x,y), with x = get_global_id(0) and y = get_global_id(1).

A work-item is a thread, while work-groups are groups of work-items/threads.

I believe the division work-groups / work-items is related with the hardware architecture of GPUs and other accelerators (e.g. Cell/BE); you can map the execution of work-groups to GPU Stream Multiprocessors (in NVIDIA talk) or SPUs (in IBM/Cell talk), while the corresponding work-itens would run inside the execution units of the Stream MultiProcessors and/or SPUs. It's not uncommon to have work group size = 1 if you are executing kernels in a CPU (e.g. for a quad-core, you would have 4 work groups, each one with one work item - though in my experience it's usually better to have more workgroups than CPU cores).

Check the OpenCL reference manual, as well as the OpenCl manual for whichever device your are programming. The quick reference card is also very helpful.