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I’m nominating Shannon Blair from Los Alamos National Laboratory’s (LANL) Sustainability Program and Michael Moss from LANL’s Pollution Prevention Program for the Phil Wirdzek Leadership Award for their efforts in reducing sulfur hexafluoride (SF 6 ) emissions at LANL and creating a system of accounting for improved tracking of SF 6 emissions in the future.
This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for the calendar year (CY) 2022. In CY 2022, ALCF operated Theta, an Intel-based Cray XC40 system (11.7 petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9 petaflops), that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform; and Polaris, a 44-petaflop AMD and NVIDIA-based HPE Apollo 6500 Gen10+ system that provides a powerful new platform to prepare applications and workloads for Aurora, Argonne National Laboratory’s (Argonne’s) upcoming Intel-Hewlett Packard Enterprise (HPE) exascale supercomputer.
This presentation was submitted to the Society for Mining, Metallurgy, and Exploration (SME) MineXchange annual conference in Salt Lake City, Utah. This abstract and subsequent presentation provide an overview of the Critical Minerals Leadership Academy (CMLA) that took place in Laramie, Wyoming in August of 2025. The presentation includes information regarding the development of the CMLA, a review of the event, and information regarding participants, their backgrounds and future intentions.
DOE scientists, today, have access to high performance computing (HPC) facilities with very powerful systems that enable them to execute their computations faster, more efficiently, and at greater scales than ever before. To further their knowledge and produce new discoveries, scientists rely on workflows - sometimes very complex - that provide them with an easy way to automate, reproduce and verify their computations. However, historically, creating workflow submission environments in large HPC facilities has been cumbersome, requires expertise and many man-hours of effort due to the peculiarities, policies, and the restrictions that these systems present. In this paper we discuss the approach a large DOE facility (OLCF) is taking in order to provide containers as a service to its users. This capability is used to create Pegasus workflow management system submit nodes as a service (WSaaS) at the Oak Ridge Leadership Computing Facilities (OLCF), targeting the Summit supercomputer. This deployment builds upon the Kubernetes/Openshift cluster (Slate) that exists within OLCF’s DMZ and its automation triggers. Additionally, we evaluate our approach’s overhead and effort to deploy the solution as compared to previous solutions, such as setting up a Pegasus submission environment on OLCF’s login nodes or submitting jobs remotely via the rvGAHP.
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Many scientific applications that support the mission of the US Department of Energy (US-DoE) require modeling complex engineering and/or physical systems. Here, examples of such complex systems arise from: (a) materials science to develop new compounds with exceptional mechanical and thermodynamical properties (e.g., resistance to mechanical stresses and high temperatures), (b) structural and nuclear engineering to model the temporal evolution of the structural damage of concrete shields exposed to continuous neutron and gamma radiations emitted by the nuclear reactor core, (c) urban sciences (e.g., transportation and smart buildings), and (d) power grid systems.
It is an honor to dedicate this special issue to Dr. Scott A. Chambers, who has had a rewarding and impactful career in surface science, spectroscopy, and thin film synthesis. His research career, spanning from his graduate work in the 1970’s to the present day, was built upon pioneering early work in precision thin film synthesis and spectroscopic characterization that occurred beginning in the 1960’s. Notably, this includes the contributions of both Art Gossard to precision film synthesis by molecular beam epitaxy (MBE) and Chuck Fadley to photoelectron spectroscopy; both Art and Chuck were recently honored with JVSTA commemorative issues of their own. Yet Scott is no mere copycat; he extended and expanded their contributions to further advance the field of surface science, and he applied the same scientific rigor to the emerging field of precision epitaxial oxide synthesis. This rigor was perhaps not always appreciated by the more “enthusiastic” members of the community who tended to draw exciting conclusions from limited data. He was once referred to, fondly, by a collaborator as a “spoilsport” for his penchant for using careful, defensible synthesis and characterization to prove that popular models and assumptions of the day did not stand up to scrutiny.
Monitoring and analyzing a wide range of I/O activities in an HPC cluster is important in maintaining mission-critical performance in a large-scale, multi-user, parallel storage system. Center-wide I/O traces can provide high-level information and fine-grained activities per application or per user running in the system. Studying such large-scale traces can provide helpful insights into the system. It can be used to develop predictive methods for making predictive decisions, adjusting scheduling policies, or providing decisions for the design of next-generation systems. However, sharing real-world I/O traces to expedite such research efforts leaves a few concerns; i) the cost of sharing the large traces is expensive due to this large size, and ii) privacy concern is an issue.We address such issues by building an end-to-end machine learn- ing (ML) workflow that can generate I/O traces for large-scale HPC applications. We leverage ML based feature selection and gener- ative models for I/O trace generation. The generative models are trained on I/O traces collected by the darshan I/O characterization tool over a period of one year. We present a two-step generation process consisting of two deep-learning models, called the feature generator and the trace generator. The combination of two-step generative models provides robustness by reducing the bias of the model and accounting for the stochastic nature of the I/O traces across different runs of an application. We evaluate the performance of the generative models and show that the two-step model can generate time-series I/O traces with less than 20% root mean square error.
I/O constitutes a significant portion of most of the application run-time. Spawning many such applications concurrently on an HPC system leads to severe I/O contention. Thus, understanding and subsequently reducing I/O contention induced by such multi-tenancy is critical for the efficient and reliable performance of the HPC system. In this study, we demonstrate that an application’s performance is influenced by the command line arguments passed to the job submission. We model an application’s I/O behavior based on two factors: past I/O behavior within a time window and user-configured I/O settings via command-line arguments. We conclude that I/O patterns for well-known HPC applications like E3SM and LAMMP are predictable, with an average uncertainty below 0.25 (A probability of 80%) and near zero (A probability of 100%) within a day. However, I/O pattern variance increases as the study time window lengthens. Additionally, we show that for 38 users and at least 50 applications constituting approximately 93000 job submissions, there is a high correlation between a submitted command line and the past command lines made within 1 to 10 days submitted by the user. We claim the length of this time window is unique per user.
Andes is a (704)-node commodity-type Linux® cluster. Each of Andes’s 704 nodes contain two 16-core 3.0 GHz AMD EPYC 7302 processors with AMD’s Simultaneous Multithreading (SMT) Technology and 256GB of main memory. Andes also has nine large memory GPU nodes. These nodes each have 1TB of main memory and two NVIDIA K80 GPUs with two 14-core 2.30 GHz Intel Xeon processors with HT Technology.
Abstract not provided.
This annual report from the Better Buildings Initiative contains information about partner projects, energy and cost savings, and new program initiatives. The 2021 Progress Report also recognizes the latest group of Better Buildings Goal Achievers - organizations who have met their energy, water, and/or financing commitments under the Better Buildings or Better Plants Challenge.
Simulation and Computation plays a pivotal role in maintaining confidence in the stockpile as the approach to underwrite that confidence has changed. The need for resolution and fidelity at scale drives our need for increased computing capability. DOE/NNSA Advanced Simulation and Computing (ASC) program provides the computational surrogate for testing. ASC provides simulation-based confidence in the U.S. stockpile.
ASC strategy is focused on three principal goals: Provide capabilities enabling a resilient deterrent and responsive complex; Provide capabilities needed to confidently assess and qualify the performance of nuclear weapons; Build computing technologies for solving national security problems that are intractable today.
Mission drivers at the national level are evolving to accommodate a broadening landscape. The U.S. faces a rapidly evolving, threat-based environment with an associated expansion of national-level priorities. Priorities continue to include all aspects of sustaining the existing, aging stockpile. Now, however, we must also include consideration of options for a future deterrent, including efficacy of the deterrent against new threats (threat assessment and response), capability needs associated with new materials and modern manufacturing and production processes, and efficiency requirements for a modern workflow for weapons design. Executing this complex strategy will come with challenges. We must execute our current mission, while we prepare for future missions and an increasingly uncertain future.
Materials Dynamics focuses on deterministic linkages between microstructure-properties-performance of materials at high strain rates and pressures. Understanding process-structure-propertiesperformance (PSPP) is increasingly important for new material qualification, accepting manufacturing process changes, and new materials development.
Abstract not provided.
Abstract not provided.