RMACC 2026

Discover how Google Cloud works with High Performance Computing by integrating specialized infrastructure and managed storage with powerful, cloud-native orchestration tools. This session highlights how these advancements accelerate scientific discovery and engineering design, providing the fastest time-to-insight for mission-critical research.

Python dependency management getting you burned? Tired of slow pip installs and juggling virtual environments on HPC systems? This session introduces UV, a lightning-fast Python package manager designed for speed, simplicity, and reproducibility. 
We’ll explore UV’s core features including uv pip, uv venv, uv tool and demonstrate how they streamline package installation, environment creation, and tool management. We’ll also compare UV with traditional solutions such as pip and conda, highlighting where it excels in performance and usability, particularly in high-performance computing (HPC) environments. 

AI is creating clear winners and losers across economies and regions. Sovereign AI Factories offer a powerful economic fulcrum for states seeking to attract talent, investment, and sustainable growth. By pooling resources at a state level, Sovereign AI Factories unite universities, K–12 systems, corporations, research institutes, and economic development agencies around a shared AI infrastructure that no single organization could afford on its own.
This presentation will outline the three pillars of a successful Sovereign AI Factory:
Defining a Sovereign AI Factory

• Setting goals
• Building the coalition
• Promoting the benefits

Defining the Hardware Architecture

• Scaling compute, storage, and network resources
• Addressing the need for direct liquid cooling
• Key data center considerations

Defining the User Experience

• Delivering a self-service cloud experience
• Ensuring user and resource security
• Supporting AI and HPC workloads

Meetup with members of the RMACC SysAdmin group for an informal discussion.

This session will cover ASU’s experience with the recent Intel Gaudi system donation, including the planning and buildout of new data center space to support the hardware. I will discuss the technical and operational challenges involved in bringing the systems online, lessons learned during deployment, the current status of the environment, and how we plan to integrate Gaudi into our broader research computing ecosystem to support AI and other high performance workloads.

Benchmarking is a core part of HPC operations — whether you're validating a new cluster, justifying a procurement, or hunting down a performance regression. But running benchmarks well on heterogeneous hardware introduces challenges that the documentation doesn't always prepare you for. This presentation shares our experience running the NVIDIA HPC benchmark container on A100 GPUs and submitting results to the Top500 and Green500 lists, covering the practical details of tuning HPL parameters, navigating the submission process, and the surprises we encountered along the way. We also attempted runs across mixed A100 and H100 nodes, which raised questions about how GPU generational differences affect Linpack scaling, interconnect saturation, and whether heterogeneous submissions are even meaningful. Beyond Linpack, we'll discuss our use of the OSU Micro-Benchmarks (OMB) suite as a cluster diagnostics tool, using pairwise node communication tests to identify fabric bottlenecks, misconfigured adapters, and inconsistent latency across the cluster, and more.