Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing Units

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...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.32270-32279
Main Authors: Sarwar, Muhammad Umer, Hanif, Muhammad Kashif, Talib, Ramzan, Aziz, Muhammad Haris
Format: Article
Language:English
Subjects:
Algorithms
Automata
Automata theory
CUDA
forward algorithm
GPU
Graphics processing units
Heuristic algorithms
Inference
Inference algorithms
Kernel
Learning automata
parallelization
Performance enhancement
Stochastic automata
Stochastic processes
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2973741