Frontiers of Artificial Intelligence Through Accelerated Model Training and Optimization Techniques

The need to improve training procedures has become more apparent in the fast developing area of High-Performance Computing (HPC) powered artificial intelligence models. In this research, we provide a unique method, the Parallelized Stochastic Gradient Descent (SGD) algorithm, which takes use of high-performance computing (HPC) capabilities to speed up the training of AI models. Adagrad, Adam, L-BFGS, NAG, RMSprop, and SGD are six classic optimization methods that are compared to the suggested approach. We want to demonstrate the efficacy of our approach in terms of convergence speed, computing economy, and model correctness by conducting comprehensive experiments and evaluations. The results of our trials show that the suggested approach greatly outperforms the state-of-the-art methods, achieving convergence to a smaller loss in a shorter amount of time. Additionally, shorter training periods and smaller model sizes demonstrate the computational efficiency of our method. These discoveries are of tremendous relevance for different AI applications, from natural language processing to computer vision, where training big and sophisticated models is a computational problem. Our findings have ramifications beyond the AI field since saving energy and money via more effective use of HPC resources makes AI more accessible and sustainable for more people. The optimization of deep learning algorithms in the high-performance computing age is a topic of continuing discussion, to which our results add. This research shows how HPC-driven AI models may disrupt businesses by facilitating the quick creation of high-performing models and encouraging creativity across disciplines.