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Thread is the basic running unit in CUDA programming model and it has a three-level hierarchy: grid, block, thread.
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After the calculation step may be a second stage of synchronization of the block threads before writing to the GPU global memory.
In the context of a thread block, the scheme indexes intra-block threads along two dimensions to deal with data with different scale factors.
(Detailed definition and relation of blocks, threads, and memory in GPU kernel was described in Additional file 1).
If there are 256 threads in a block, each thread can have 64 bytes, that is, 16 words.
For example, by assigning different cells of a diagonal to a block of threads, each thread has to access a different row and different column.
The computational results showed that the proposed method significantly improved Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.
The results suggested that the proposed method may improve Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.
The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.
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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