Model Parallelism for Efficient GPU Computing in Deep Learning Applications: Comprehensive Review

Abstract

Abstract—This comprehensive analysis explores the transformative impact of parallel computing techniques in deep learning. It examines the collaborative endeavors of computer scientists and domain-specific researchers, encompassing a broad spectrum of strategies ranging from conventional data parallelism to cutting-edge methodologies like pipeline and inter-operator parallelism. By democratizing access to high-performance computing resources, these innovations are redefining the landscape of artificial intelligence (AI). The study highlights the considerable enhancements in training efficiency and model accuracy while addressing challenges such as integration complexities and ethical considerations. Additionally, the research investigates the environmental implications of large-scale parallel computing, underscoring the need for sustainable, long-term solutions to minimize its impact. It emphasizes the practical importance of these advancements, particularly in critical sectors such as healthcare and education, where AI-driven innovations hold the potential to revolutionize existing practices. Emphasizing a holistic approach, the analysis advocates incorporating ethical, environmental, and societal considerations in developing AI technologies. Envisioning a future where artificial intelligence is robust but also inclusive and sustainable, this analysis serves as a roadmap for fostering a more accessible, ethical, and environmentally conscious era of artificial intelligence. As the research community continues to push boundaries, this study guides the realization of responsible and impactful AI implementation. Keywords—Artificial intelligence (AI), data parallelism, inter-operator parallelism, parallel computing