Optimizing I/O for AI Workloads in GPU Clusters

This technical whitepaper discusses the challenges and solutions related to optimizing input/output (I/O) for artificial intelligence (AI) workloads in geo-distributed GPU clusters. It addresses the complexities faced by AI/ML infrastructure teams in delivering high-performance systems that support the training and deployment of AI models. The paper outlines the critical role of GPUs in AI/ML infrastructure, emphasizing the need for effective utilization of these resources amidst budget constraints and hardware shortages. It identifies common causes of low GPU utilization, including infrastructure and code bottlenecks, and highlights the impact of data stalls on performance. The document also presents strategies for diagnosing and resolving these issues to enhance GPU utilization and improve overall AI workload efficiency. By focusing on optimizing data loading processes and addressing I/O bottlenecks, organizations can better leverage their GPU investments and accelerate the deployment of AI models.