A Strategic Shift in Infrastructure Investment and Management
With rising costs and evolving needs, NAIC is pivoting from the original “factory model” approach—where centralized resources were shared among users—to a distributed model that leverages local resources. The newly acquired Olivia supercomputer, in conjunction with Sigma2 resources and the Lumi supercomputer partnership, exemplifies this shift. This model enables researchers to utilize resources closer to their location, easing pressure on central systems and reducing redundancies. To accommodate more diverse and high-demand workloads, NAIC has integrated commercial cloud services from providers like Google and Azure. This flexible, multi-cloud strategy allows the project to address specific needs as they arise, providing scalable solutions without overloading primary infrastructure.
A Refined Work Package Structure
At the heart of this restructuring are the updated work packages, which now emphasize technology assessment, ethical AI development, and sustainable infrastructure. Notably, WP9 will lead benchmarking and profiling initiatives to ensure that NAIC resources meet real-world AI demands effectively. This includes refining processes for local resource optimization, minimizing carbon footprints, and offering a robust support structure for developers and researchers alike.
Each work package aligns with NAIC’s strategic shift, blending operational support with innovative performance analysis, with WP9 specifically tasked to bridge gaps through advanced profiling and monitoring.
Benchmarking and Real-Time Profiling for Informed Decision-Making
A central focus of WP9 is on building and refining benchmarking processes. Using new dashboards like the “job analyzer,” NAIC team members can get instant feedback on performance and analyze workloads down to individual job levels. This tool offers insights into resource efficiency, pinpointing where improvements are needed. Benchmarking also extends to environmental sustainability. NAIC will integrate carbon footprint assessments and energy monitoring to align with Norway’s green commitments. This capability empowers NAIC not only to evaluate current hardware efficiency but also to project future needs responsibly.
Enhancing Workload Characterization and Performance
WP9 is actively working to characterize various workload types, focusing on factors like data volume, model complexity, and I/O demands. By identifying usage patterns and common resource bottlenecks, WP9 can streamline workload allocation, ensuring that high-demand jobs run optimally without straining system capacity. In parallel, feedback systems are being designed to guide users on efficient resource usage. This initiative includes regular updates on GPU and CPU allocation, helping researchers maximize productivity while minimizing wastage. The team also discussed the potential for ongoing user training sessions tailored for beginner and advanced users alike, making resource use as efficient as possible.
Future Outlook: Sustainable Benchmarking and Integrated Monitoring
Looking ahead, WP9 aims to establish a continuous benchmarking process that evolves alongside hardware and software developments. Automation will play a key role in maintaining these benchmarks, reducing the administrative load and ensuring data consistency. This kickoff marks a significant milestone in NAIC’s journey. By adding means to improving the infrastructure usage, investing in flexible, high-performance computing resources, and guiding users, WP9 is setting the foundation for a future where Norway’s AI resources are accessible, efficiently used, and resilient.