Device Memory Spaces

CUDA devices use several memory spaces, which have different characteristics that reflect their distinct usages in CUDA applications. These memory spaces include global,
local, shared, texture, and registers, as shown in Figure 2.

Of these different memory spaces, global memory is the most plentiful; see Features and Technical Specifications of the CUDA C Programming Guide for the amounts of memory available in each memory space at each compute capability level. Global, local, and texture memory have the greatest access latency, followed by constant memory, shared memory, and the register file.The various principal traits of the memory types are shown in Table 1.

writing to its underlying global memory array in the same kernel launch should be avoided because the texture caches are read-only and are not invalidated when the associated global memory is modified.