In the last decade software transactional memory has become a prominent programming paradigm, which aims to improve the execution performance of concurrent programs. However, most research in the field is done in programming languages such as C, C++ and JAVA. In this paper, we present a PSTM-based architecture of DEEPSAM, a computational chemistry program written in Python language. The PSTM-based architecture aims to improve execution performance of the original computational chemistry system architecture based on evolutionary programming and to provide transactional-memory-based means for its future optimizations. The metric analysis includes system execution time and problem size scalability. The experimental results show that the new PSTM-based version gained better execution time results relative to the original version. Likewise, the results did not reveal any architectural bottleneck of the new architecture.

Keywords: Software transactional memory, Python, Diffusion equation evolutionary programming simulated annealing method (DEEPSAM), Python software transactional memory (PSTM), Evolutionary programming (EP)