Abstract
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.
Highlights
Stochastic automata are probabilistic automata with input/output behavior
The Compute Unified Device Architecture (CUDA) programming model introduced by NVIDIA provides extension to the C language and supports the CPU/Graphics Processing Units (GPUs) execution
The high computational complexity limits the usage of stochastic automata
Summary
Stochastic automata are probabilistic automata with input/output behavior. Stochastic automata emits an output symbol and moves into another state after reading input. A stochastic automaton can have all transitions between states [5] This can lead to high computational complexity for real world problems. Performance of stochastic automata algorithms can be enhanced with the help of modern high performance computing. The Compute Unified Device Architecture (CUDA) programming model introduced by NVIDIA provides extension to the C language and supports the CPU/GPU execution. Many threads can be executed in parallel to process different parts of data [6], [7]. The Forward algorithm finds the path with time complexity O(m2)n. The Forward algorithm is partitioned into data independent and dependent parts.
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