AI Systems Adoption of Unified Research Data Management on Accelerator Computing

Abstract

Research data is expected to grow exponentially with the adoption of artificial intelligence (AI) and machine learning (ML). Robust data management practices are crucial for ensuring data integrity, provenance tracking, and adherence to ethical and regulatory standards, which is essential for building trustworthy AI systems. This paper explores the adoption of oneAPI, an open standards-based programming model, for streamlining research data management across diverse AI systems. It also explores containerization to ensure consistent execution across heterogeneous Cloud-based environments while providing security over sensitive data-based systems. By leveraging oneAPI's cross-architecture capabilities, including Data Parallel C++ (DPC++) and the other AI toolkits based on oneAPI, researchers can develop secure and performant AI solutions that seamlessly process and analyze sensitive data across heterogeneous computing environments. This unified approach proposes a framework for consistent data handling and reproducibility of research computing results where data confidentiality, security and integrity are concerns notably in the Cloud. Through a case study example, this paper discusses the benefits of adopting oneAPI for AI research data management (RDM), highlighting its potential to accelerate scientific discoveries while maintaining robust security and privacy standards.