localworksize

localworksize refers to a concept in parallel computing, particularly within the context of graphics processing units (GPUs) and their programming models like OpenCL and CUDA. It defines the number of threads that are grouped together to execute a kernel concurrently on a processing unit. This group of threads is known as a work-group.

When a kernel is launched on a GPU, it is typically executed by a grid of work-groups, and each work-group is composed of a fixed number of threads, which is the local work size. The local work size is a crucial parameter that can significantly impact the performance of a GPU computation. Choosing an appropriate local work size is essential for efficient hardware utilization, as it affects how threads are mapped to the GPU's streaming multiprocessors (SMs) and how efficiently resources like shared memory and registers are managed.

Factors influencing the optimal local work size include the specific GPU architecture, the kernel's computational patterns, and memory access characteristics. For instance, a local work size that is a multiple of the warp size (a group of threads executed in lockstep by an SM) often leads to better performance. Developers experiment with different local work sizes to find the sweet spot that maximizes throughput and minimizes latency for their particular application. The global work size, on the other hand, defines the total number of threads executing the kernel across the entire grid, irrespective of work-group boundaries.