Scalability and Distributed Computing in NET for Large-Scale AI Workloads

Abstract

The rapid advancement of artificial intelligence (AI) has led to the development of increasingly complex and resource-intensive algorithms, demanding scalable and distributed computing solutions. This abstract introduces a comprehensive exploration of scalability and distributed computing in the context of large-scale AI workloads, with a focus on the .NET framework. As AI models grow in complexity and size, traditional computing architectures often struggle to meet the computational demands. This research delves into the challenges associated with scaling AI workloads and explores how the .NET framework, with its rich ecosystem and robust features, can be leveraged to address these challenges effectively. The study begins by establishing the key factors influencing the scalability of AI workloads, such as model size, data volume, and training complexity. It then examines the limitations of traditional computing approaches and highlights the need for distributed computing solutions to harness the power of parallelism and accelerate AI computations. The .NET framework, known for its versatility and cross-platform capabilities, provides a promising platform for developing scalable and distributed AI applications. The research also discusses potential challenges and considerations in adopting distributed computing for large-scale AI workloads in the .NET ecosystem. This includes issues related to data consistency, fault tolerance, and efficient resource utilization.