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A kernel was launched with the number of blocks of threads as a multiple of the amount of blocks executed concurrently in each SM.
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The computation is executed by sets of blocks of threads.
Since a SM can process one or more blocks of threads simultaneously, we avoid blocks of threads staying in the scheduling queue.
Each kernel executes as a group of threads, which are logically organized in the hierarchical form of grids and blocks of threads.
As we can see in Figure 2 the grid (shaded in gray) could be smaller than the blocks of threads.
This was implemented in CUDA at the level of blocks of threads.
First, we set the number of the blocks as 64 and change the number of threads, as shown in Figure 6.
In CUDA, the thread hierarchy within a kernel is organized by the thread, the block of threads and the grid of blocks as shown in Figure 1, where different thread hierarchies have different memory scopes.
As a consequence, each block of threads stay persistent and can operate on more than one part of the input, when this input is larger than the grid of blocks of the kernel.
A separate block of threads was run for each fragment, while 256 threads were run within a block.
Using CUDA, a kernel is executed on threads organized in blocks; each thread is responsible for a portion of data, and each block of threads shares a local memory (called shared).
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