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Parallel and distributed computing models on a graphics processing unit to accelerate simulation of membrane systems

机译:图形处理单元上的并行和分布式计算模型,以加速膜系统的仿真

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Membrane systems are parallel distributed computing models that are used in a wide variety of areas. Use of a sequential machine to simulate membrane systems loses the advantage of parallelism in Membrane Computing. In this paper, an innovative classification algorithm based on a weighted network is introduced. Two new algorithms have been proposed for simulating membrane systems models on a Graphics Processing Unit (GPU). Communication and synchronization between threads and thread blocks in a GPU are time-consuming processes. In previous studies, dependent objects were assigned to different threads. This increases the need for communication between threads, and as a result, performance decreases, In previous studies, dependent membranes have also been assigned to different thread blocks, requiring inter-block communications and decreasing performance. The speedup of the proposed algorithm on a GPU that classifies dependent objects using a sequential approach, for example with 512 objects per membrane, was 82×, while for the previous approach (Algorithm 1), it was 8.2×. For a membrane system with high dependency among membranes, the speedup of the second proposed algorithm (Algorithm 3) was 12×, while for the previous approach (Algorithm 1) and the first proposed algorithm (Algorithm 2) that assign each membrane to one thread block, it was 1.8×.
机译:膜系统是广泛用于各个领域的并行分布式计算模型。使用顺序机器来模拟膜系统会失去膜计算中并行性的优势。本文介绍了一种基于加权网络的创新分类算法。已经提出了两种新算法来模拟图形处理单元(GPU)上的膜系统模型。 GPU中线程与线程块之间的通信和同步是耗时的过程。在以前的研究中,从属对象被分配给不同的线程。这增加了线程之间通信的需求,结果导致性能下降。在以前的研究中,从属膜也已分配给不同的线程块,需要块间通信并降低性能。提出的算法在使用顺序方法(例如,每个膜具有512个对象)的相关对象进行分类的GPU上的加速为82倍,而对于先前的方法(算法1)为8.2倍。对于膜之间具有高度依赖性的膜系统,第二种建议算法(算法3)的加速为12倍,而对于先前方法(算法1)和第一种建议算法(算法2),将每个膜分配给一个线程块,是1.8×。

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