Co-design in the Exascale Computing Project

Germann, Timothy C.

doi:10.1177/10943420211059380

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…High Performance Computing systems have been steadily rising in size and complexity in the last years, as revealed by the exponential increase of the worldwide supercomputer installation 3 . HPC systems are typically composed by replicating a large number of components, usually, in the order of thousands of computing nodes, each of them constituted of a collection of smaller functional parts, such as CPUs, RAM, interconnections, storage, etc.…”

Section: Introductionmentioning

confidence: 99%

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Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Molan

Borghesi

Benini

et al. 2022

Euro-Par 2022: Parallel Processing

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Anomaly detection systems are vital in ensuring the availability of modern High-Performance Computing (HPC) systems, where many components can fail or behave wrongly. Building a data-driven representation of the computing nodes can help with predictive maintenance and facility management. Luckily, most of the current supercomputers are endowed with monitoring frameworks that can build such representations in conjunction with Deep Learning (DL) models. In this work, we propose a novel semi-supervised DL approach based on autoencoder networks and clustering algorithms (applied to the latent representation) to build a digital twin of the computing nodes of the system. The DL model projects the node features into a lower-dimensional space. Then, clustering is applied to capture and reveal underlying, non-trivial correlations between the features. The extracted information provides valuable insights for system administrators and managers, such as anomaly detection and node classification based on their behaviour and operative conditions. We validated the approach on 240 nodes from the Marconi 100 system, a Tier-0 supercomputer located in CINECA (Italy), considering a 10-month period.

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Section: Introductionmentioning

confidence: 99%

“…The sheer size and complexity of supercomputers create huge challenges in terms of optimal management of the IT components and their significant energy footprint [1]. The race towards Exascale 4 continues to make these challenges ever more pressing [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Molan

Borghesi

Benini

et al. 2022

Euro-Par 2022: Parallel Processing

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ECP libraries and tools: An overview

Heroux,

McInnes,

Ahrens

et al. 2024

The International Journal of High Performance Computing Applica

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The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. Subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

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Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations

Degen,

Caviedes Voullième,

Buiter

et al. 2023

Geosci. Model Dev.

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Abstract. An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

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Co-design in the Exascale Computing Project

Abstract: We provide an overview of the six co-design centers within the U.S. Department of Energy’s Exascale Computing Project, each of which is described in more detail in a separate paper in this special issue. We also give a perspective on the evolution of computational co-design.

Cited by 3 publications

References 12 publications

Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models

ECP libraries and tools: An overview

Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations

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