This paper introduces the AI-Enhanced Cluster Configurator, a tool that uses artificial intelligence to streamline the configuration of high-performance computing (HPC) clusters. This tool offers a sophisticated recommendation system that assists users in selecting optimal server and storage solutions. It provides two operational modes: an Assisted Mode, which simplifies the configuration process for novice users through AI-generated recommendations, and an Expert Mode, which offers in-depth customization options for experienced users. This study delves into the AI methodologies employed, underscoring how they substantially enhance and simplify the design of HPC clusters for a broader range of users, thereby supporting the solution architecture of essential technology for cutting-edge research and development across various domains.

Managing High-Performance Computing (HPC) workloads in the cloud requires balancing performance, cost efficiency, and automation. Traditional HPC deployment on cloud is manual, complex, and error-prone leading to delays, misconfigurations. These workloads demand powerful and expensive compute, network, and storage resources that remain billable even during setup, further increasing financial overhead. The need for automated, scalable, and cost-optimized HPC environments has never been greater. To address these challenges, we introduce RaceHPC, a cloud-agnostic, fully automated platform designed to host HPC competitions, training programs, and benchmarking initiatives on Cloud with minimal manual effort. By leveraging DevOps, Continuous Integration / Continuous Delivery (CI/CD), and Kubernetes orchestration, it eliminates manual intervention by automatically provisioning all required resources and configurations using Infrastructure as Code (IaC) and containerized environments integrating idempotent and self-scaling deployment mechanisms and ensuring that each competitor receives a secure, isolated, and fully preloaded HPC workspace with the necessary dependencies and tools accessible via a web browser. Beyond automation, RaceHPC prioritizes cost efficiency by incorporating FinOps strategies, automatically scaling resources based on demand and decommissioning infrastructure at competition deadlines to eliminate unnecessary expenses.

The European Extremely Large Telescope (E-ELT) is one of today's most challenging projects in ground-based astronomy. Addressing one of the key science cases for the E-ELT, the study of the early Universe, requires the implementation of multi-object adaptive optics (MOAO), a dedicated concept relying on turbulence tomography. We use a novel pseudo-analytical approach to simulate the performance of tomographic reconstruction of the atmospheric turbulence in an MOAO system on real datasets. We simulate simultaneously 4K galaxies in a common field of view on massively parallel supercomputers during a single night of observations. We are able to generate a first-ever high-resolution galaxy map at almost a real-time throughput. This simulation scale opens new research horizons in numerical methods for experimental astronomy, some core components of the pipeline standing as pathfinders toward actual operations and future astronomic discoveries on the E-ELT.

Abstract The Abstract Data and Communication Library (ADCL) is an adaptive communication library optimizing application level group communication operations at runtime. The library provides for a given communication pattern a large number of implementations and incorporates a runtime selection logic in order to choose the implementation leading to the highest performance. In this paper, we demonstrate how an application utilizing ADCL is deployed on a wide range of HPC architectures, including an IBM Blue Gene/L, an NEC SX‐8, an IBM Power PC cluster using an IBM Federation Switch, an AMD Opteron cluster utilizing a 4xInfiniBand and a Gigabit Ethernet network, and an Intel EM64T cluster using a hierarchical Gigabit Ethernet network with reduced uplink bandwidth. We demonstrate, how different implementations for the three‐dimensional neighborhood communication lead to the minimal execution time of the application on different architectures. ADCL gives the user the advantage of having to maintain only a single version of the source code and still have the ability to achieve close to optimal performance for the application on all architectures. Copyright © 2010 John Wiley & Sons, Ltd.