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The proposed method utilizes the shared memory which can reduce the transfer latency of simulation data between CPU and GPU.
On the other hand, the hybrid-MPI computing utilizes the shared memory among CPU cores in the CPU, and the MPI data communications were performed only for inter-CPU data exchange.
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Our method outperforms CUDASW++ 2.0 for all of the query sequences because ours can fully utilize the shared memory without the need of swapping data between shared memory and global memory.
The results showed that the GPU calculation for 5763 computational grids achieved the performance of 170 GFLOPS by utilizing the shared memory as a software-managed cache.
The proposed cloth simulation using GPU parallel computing provided around 10 34 times faster than CPU only and utilized the shared memory to communicate the cloth information between CPU and GPU.
Developing concurrent software requires developers to keep track of all the possible communication patterns that evolve from the large number of possible interleavings or concurrently overlapping executions that can occur between different execution threads through utilizing the shared memory.
Since we observed that the shared memory in each streaming multiprocessor is not fully utilized in CUDASW++, the execution flow of the Smith-Waterman algorithm was rearranged to fully utilize the shared memory for reducing the amount of slow global memory access.
Therefore, this experiment does not make use of the shared memory to reduce memory bandwidth bottleneck.
Two types of memories of CUDA, the global memory and the shared memory are utilized in the execution.
Furthermore, the shared memory is usually used for the shared data space utilized in the computation among the GPU compute threads within a specific GPU compute block.
Distributed Shared-Memory (DSM) systems are shared-memory multiprocessor architectures in which each processor node contains a partition of the shared memory.
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