CUDA-BASED PARALLELIZATION OF GRADIENT BOOSTING AND BAGGING ALGORITHM FOR DIAGNOSING DIABETES (Computer systems and information technologies, 2023)
Data, its volume, structure, and form of presentation are among the most significant problems in working in the medical field. The probability of error is very high without innovative high-tech data analysis tools. It is easy to miss an important factor that is critical but lost among other, less important information. This work aims to study the proposed parallel gradient boosting algorithm in combination with the Bagging algorithm in the classification of diabetes to achieve greater stability and higher accuracy, reduce computational complexity and improve performance in medicine. The methods of parallelization of the Gradient Boosting algorithm in combination with the Bagging algorithm are investigated in the paper. Performance scores were obtained: approximately 7 using ThreadPoolExecutor and an eight-core computer system and 9.5 based on CUDA technology. Performance indicators that go to the unit are calculated. This, in turn, confirms the effectiveness of the proposed parallel algorithm. Another significant result of the study is improving algorithm accuracy by increasing the number of algorithms in the composition. The problem of diagnosing a patient's diabetes based on specific measurements included in the data set is considered. Detailed analysis and pre-processing of the selected dataset were performed. The parallelization of the proposed algorithm is implemented using the multi-core architecture of modern computers and CUDA technology. The process of learning models and training samples was parallelized. The theoretical estimation of the computational complexity of the offered parallel algorithm is given. A comparison of serial and parallel algorithm execution time using ThreadPoolExecutor when varying the number of threads and algorithms in the composition is presented. And also, the comparative analysis of time expenses at consecutive and parallel execution based on CPU and GPU is carried out.
Keywords: ensemble of models; acceleration, graphic processor; CUDA technology; machine learning; classification problem