Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs

• Published in: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering Vol. 14; no. 4; p. 4214
• Main Authors: Fauzan, Hermawan, Sarno, Riyanarto, Saikhu, Ahmad
• Format: Journal Article
• Language: English
• Published: 01.08.2024
• ISSN: 2088-8708; 2722-256X; 2722-2578; 2722-2578
• DOI: 10.11591/ijece.v14i4.pp4214-4227

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.