Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing Units

被引：0

作者：

Sarwar, Muhammad Umer ^{[1
]}

Hanif, Muhammad Kashif ^{[1
]}

Talib, Ramzan ^{[1
]}

Aziz, Muhammad Haris ^{[2
]}

机构：

[1] Govt Coll Univ, Dept Comp Sci, Faisalabad 38000, Pakistan

[2] Univ Engn & Technol, Dept Ind Engn, Taxila 47050, Pakistan

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Automata; Stochastic processes; Graphics processing units; Heuristic algorithms; Learning automata; Inference algorithms; Kernel; Stochastic automata; CUDA; GPU; forward algorithm; parallelization; SYSTEMS; GPUS;

D O I：

10.1109/ACCESS.2020.2973741

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A stochastic automaton is a non-deterministic automata with input and output behavior which works serially and synchronously. Stochastic automata is being used in different application areas. For large state space and sequence lengths, performance of stochastic automata is a major concern. For this purpose, graphics processing units can be employed to improve the performance. In this study, a parallel version of inference algorithm for stochastic automata is designed. The parallel version is mapped to graphics processing unit using the dynamic parallelism. The performance of parallel version is compared with different realizations and parameters. Parallel implementation of inference algorithm achieved approximately speedup factor of 50 for 256 states.

引用

页码：32270 / 32279

页数：10

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