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National Center for High-Performance Computing

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A view of the CNCAR main building.

The National Center for High-Performance Computing (Martino: Centro Nacional de Computación de Alto Rendimiento, CNCAR), also known as the National Supercomputer (Martino: Superordenaror Nacional), is a flagship research institute dedicated to advancing computational science, and providing economic modeling, mathematical simulation, and generative AI. It is located in Cárdenas, the capital of Nouvelle Alexandrie. It serves as the nation’s primary hub for high-performance computing resources, providing unparalleled computational power to academia, industry, and government agencies.

History

The CNCAR was established in 1695 AN as part of the New Prosperity Plan, in order to centralise and democratise access to high-performance computing. Initially conceived as a distributed network of smaller clusters, the project consolidated into a single, purpose-built campus in Cárdenas by 1710 AN. The center’s early work focused on economic simulation, climate and disaster modeling, and automation research.

In 1738 AN, the Frontera Program allocated over €4.2 billion towards the expansion and upgrading of CNCAR. This funding enabled the onboarding of the very first domestic Artificial Intelligence program, AlexAI, which came online in 1740 AN and immediately positioned Nouvelle Alexandrie as a leader in AI research.

Purpose

The CNCAR provides high-computational resource capacity for a wide variety of both scientific and economic research, including:

  • Computational Science for physics, chemistry, and engineering
  • Economic Modeling for large-scale simulations for economic trends, market analysis, finance, and international trade.
  • Mathematical Simulations for advanced algorithms to solve complex equations in theoretical and applied mathematics.
  • Generative AI through the development of large-scale language models, neural networks, and AI-driven creative tools.
  • Data Science & Analytics for big data processing for public health, urban planning, and social sciences.

In general, CNCAR services over 1.2 million node-hours per year across its high-performance computing resources, with Helix serving as the primary workhorse. In 1740 AN, allocations were distributed as follows:

  • Computational Science accounted for 39% of all requests.
  • Economic Modeling accounted for 21%.
  • Mathematical Simulation accounted for 17%.
  • Data Science and Analytics accounted for 15%.
  • Generative AI accounted for 8%.

Helix

The Helix Supercomputer is the CNCAR's core facility. It provides unparalleled processing power through a hybrid architecture, utilising both CPU, GPU, and TPUs units. Peak performance was measured in 1742 AN at 12.8 PetaFLOPS. Memory systems utilise a mix of DDR5 RAM and NVMe SSD, with a total of 1.2 RAM PB + 48 SSD PB. Storage exceeds over 200 PB, using a parallel file system. The system uses 200 Gbit/s HDR connectivity to maintain low-latency, high-throughput readiness.

  • 12,288 2x 64-Core CPUs provide bulk processing power; while:
    • 49,152 H100 Tensor Core GPUs are dedicated to deep learning workloads; and
    • 1,024 TPU v8p Pods are dedicated to generative AI and large language models.

Cooling is provided by liquid immersion cooling for CPU/GPU nodes; with additional air cooling for auxiliary systems. This keeps power consumption remarkable low at 24 MW during peak use, with an intricate system of residual heating utilisation throughout Cárdenas.

Organisation

The CNCAR is directly overseen by the Department of Research and Development, and is led by a Director appointed by the Secretary. The institute is furthermore divided into three service departments and an executive office:

  • Director of Research
    • Department of Administration
    • Department of Public Engagement
    • Department of Software & Hardware Development

The CNCAR also maintains one advisory board, the Computing Advisory Council, which advises both on allocation for private and public companies, ethical use, and responsible AI development.

See also