.png){kind=icon}
.png){kind=icon}
Return
Accelerating Forward Algorithm for Stochastic Automata on Graphics Processing Units
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2973741.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Automata
Stochastic processes
Graphics processing units
Heuristic algorithms
Learning automata
Inference algorithms
Kernel
Stochastic automata
CUDA
GPU
forward algorithm
parallelization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W