What are CUDA streams?

A stream in CUDA is a sequence of operations that execute on the device in the order in which they are issued by the host code. While operations within a stream are guaranteed to execute in the prescribed order, operations in different streams can be interleaved and, when possible, they can even run concurrently.

How is GPU occupancy measured?

Achieved occupancy is measured on each warp scheduler using hardware performance counters to count the number of active warps on that scheduler every clock cycle. These counts are then summed across all warp schedulers on each SM and divided by the clock cycles the SM is active to find the average active warps per SM.

How many warps can run simultaneously inside a multiprocessor?

Now these warps (presuming they have no relation to each other) can be ran in parallel as well? Yes, warps can run in parallel. Each Fermi SM has 2 warps schedulers. Each warp scheduler can dispatch instruction(s) for 1 warp each cycle.

What is CUDA stream synchronize?

In CUDA, we can run multiple kernels on different streams concurrently. There are two types of stream synchronization in CUDA. A programmer can place the synchronization barrier explicitly, to synchronize tasks such as memory operations.

Are CUDA kernels blocking?

CUDA kernels are subdivided into blocks. A group of threads is called a CUDA block. CUDA blocks are grouped into a grid.

How many warps are in a block?

Blocks per SM For example, on a GPU that supports 16 active blocks and 64 active warps per SM, blocks with 32 threads (1 warp per block) result in at most 16 active warps (25\% theoretical occupancy), because only 16 blocks can be active, and each block has only one warp.

What is a block in CUDA?

CUDA kernels are subdivided into blocks. A group of threads is called a CUDA block. CUDA blocks are grouped into a grid. A kernel is executed as a grid of blocks of threads (Figure 2). Each kernel is executed on one device and CUDA supports running multiple kernels on a device at one time.