Results for “High performance computer” — Page 49

DOE OSTI2025

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗

DOE OSTI2018

User-level File Systems Specialized for HPC Workloads Year-End Report

High-performance computing (HPC) clusters attract the attention of Deep Learning (DL) training users due to the clusters' powerful computation capabilities. While there have been many existing efforts to enable deep neural works to leverage the powerful CPU and GPU processors from leadership high-performance computing (HPC) systems, large-scale deep learning with larger datasets requires efficient I/O support from the underlying file and storage systems. As some current and upcoming HPC clusters have large on-node memory or are equipped with NVMe SSD on compute nodes, more distributed DL trainings are considered to leverage those storages to store dataset for efficient dataset access. Our project goal is to design a specialized DL-oriented file system to improve datasets loading performance of any DL training applications. Over the past year, the team at Florida State University has performed research activities in three different aspects, including 1) completing a specialized memory-based I/O framework (DeepIO) for improving dataset loading performance on DL applications; 2) proposing a more generalized file system (DLFS) for DL applications on node-local SSDs; 3) completing the BeeGFS performance evaluation project for Deep Neural Networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Broadband (0–4 Hz) Ground Motions for a Magnitude 7.0 Hayward Fault Earthquake With Three‐Dimensional Structure and Topography

Abstract We performed fully deterministic broadband (0–4 Hz) high‐performance computing ground motion simulations of a magnitude 7.0 scenario earthquake on the Hayward Fault (HF) in the San Francisco Bay Area of Northern California. Simulations consider average one‐dimensional (1‐D) and three‐dimensional (3‐D) anelastic structure with flat and topographic free surfaces. Ground motion intensity measures (GMIMs) for the 3‐D model display dramatic differences across the HF due to geologic heterogeneity, with low wave speeds east of the HF amplifying motions. The median GMIMs agree well with Ground Motion Prediction Equations (GMPEs); however, the 3‐D model generates more scatter than the 1‐D model. Ratios of 3‐D/1‐D GMIMs from the same source allow isolation of path and site effects for the 3‐D model. These ratios show remarkably similar trends as site‐specific factors for the GMPE predictions, suggesting that wave propagation effects in our 3‐D simulations are on average consistent with empirical data.

58 GEOSCIENCES↗

DOE OSTI2013

ADVANCED SIMULATION CAPABILITY FOR ENVIRONMENTAL MANAGEMENT- CURRENT STATUS AND PHASE II DEMONSTRATION RESULTS

The U.S. Department of Energy (USDOE) Office of Environmental Management (EM), Office of Soil and Groundwater, is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is a state-of-the-art scientific tool and approach for understanding and predicting contaminant fate and transport in natural and engineered systems. The modular and open source high-performance computing tool facilitates integrated approaches to modeling and site characterization that enable robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project continues to make significant progress in development of computer software capabilities with an emphasis on integration of capabilities in FY12. Capability development is occurring for both the Platform and Integrated Toolsets and High-Performance Computing (HPC) Multiprocess Simulator. The Platform capabilities provide the user interface and tools for end-to-end model development, starting with definition of the conceptual model, management of data for model input, model calibration and uncertainty analysis, and processing of model output, including visualization. The HPC capabilities target increased functionality of process model representations, toolsets for interaction with Platform, and verification and model confidence testing. The Platform and HPC capabilities are being tested and evaluated for EM applications in a set of demonstrations as part of Site Applications Thrust Area activities. The Phase I demonstration focusing on individual capabilities of the initial toolsets was completed in 2010. The Phase II demonstration completed in 2012 focused on showcasing integrated ASCEM capabilities. For Phase II, the Hanford Site deep vadose zone (BC Cribs) served as an application site for an end-to-end demonstration of capabilities, with emphasis on integration and linkages between the Platform and HPC components. Other demonstrations, addressing attenuation-based remedies at the Savannah River Site F Area and performance assessment for a representative waste tank, illustrate integration of linked ASCEM capabilities and initial integration efforts with tools from the Cementitious Barriers Partnership.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

ADVANCED SIMULATION CAPABILITY FOR ENVIRONMENTAL MANAGEMENT – CURRENT STATUS AND PHASE II DEMONSTRATION RESULTS

The U.S. Department of Energy (USDOE) Office of Environmental Management (EM), Office of Soil and Groundwater, is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is a state-of-the-art scientific tool and approach for understanding and predicting contaminant fate and transport in natural and engineered systems. The modular and open source high-performance computing tool facilitates integrated approaches to modeling and site characterization that enable robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project continues to make significant progress in development of computer software capabilities with an emphasis on integration of capabilities in FY12. Capability development is occurring for both the Platform and Integrated Toolsets and High-Performance Computing (HPC) Multiprocess Simulator. The Platform capabilities provide the user interface and tools for end-to-end model development, starting with definition of the conceptual model, management of data for model input, model calibration and uncertainty analysis, and processing of model output, including visualization. The HPC capabilities target increased functionality of process model representations, toolsets for interaction with Platform, and verification and model confidence testing. The Platform and HPC capabilities are being tested and evaluated for EM applications in a set of demonstrations as part of Site Applications Thrust Area activities. The Phase I demonstration focusing on individual capabilities of the initial toolsets was completed in 2010. The Phase II demonstration completed in 2012 focused on showcasing integrated ASCEM capabilities. For Phase II, the Hanford Site deep vadose zone (BC Cribs) served as an application site for an end-to-end demonstration of capabilities, with emphasis on integration and linkages between the Platform and HPC components. Other demonstrations, addressing attenuation-based remedies at the Savannah River Site F Area and performance assessment for a representative waste tank, illustrate integration of linked ASCEM capabilities and initial integration efforts with tools from the Cementitious Barriers Partnership.

DOE Environmental Management, advanced simulation,↗

NASA NTRS2006

Optics Program Modified for Multithreaded Parallel Computing

A powerful high-performance computer program for simulating and analyzing adaptive and controlled optical systems has been developed by modifying the serial version of the Modeling and Analysis for Controlled Optical Systems (MACOS) program to impart capabilities for multithreaded parallel processing on computing systems ranging from supercomputers down to Symmetric Multiprocessing (SMP) personal computers. The modifications included the incorporation of OpenMP, a portable and widely supported application interface software, that can be used to explicitly add multithreaded parallelism to an application program under a shared-memory programming model. OpenMP was applied to parallelize ray-tracing calculations, one of the major computing components in MACOS. Multithreading is also used in the diffraction propagation of light in MACOS based on pthreads [POSIX Thread, (where "POSIX" signifies a portable operating system for UNIX)]. In tests of the parallelized version of MACOS, the speedup in ray-tracing calculations was found to be linear, or proportional to the number of processors, while the speedup in diffraction calculations ranged from 50 to 60 percent, depending on the type and number of processors. The parallelized version of MACOS is portable, and, to the user, its interface is basically the same as that of the original serial version of MACOS.

Lou, John↗

DOE OSTI2023

Integrated Research Infrastructure Architecture Blueprint Activity (Final Report 2023)

The complexity of scientific pursuits is increasing rapidly with aspects that require dynamic integration of experiment, observation, theory, modeling, simulation, visualization, machine learning (ML), artificial intelligence (AI), and analysis. Research projects across the Department of Energy (DOE) are increasingly data and compute intensive. Innovative research teams are accelerating the pace of discovery by using high-performance computational and data tools in their research workflows and leveraging multiple research infrastructures. Additionally, several recent high-level U.S. government reports underscore the necessity of a new advanced computing ecosystem for international competitiveness and national security. International competitors are moving forward with major research infrastructure integration efforts that seek to capture a competitive advantage in the global innovation race. Owing to its unparalleled constellation of world-class experimental and observational facilities and high-performance and extreme-scale computational, data, and networking infrastructure, DOE is positioned to be a global leader in this new era of integrated science. However, this new integration paradigm will demand continuing evolution to ensure the U.S. remains a global leader in research and innovation. The DOE Office of Science (SC) has seized on the strategic importance of integration and has adopted a vision for Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. To respond to the evolving computational requirements of research and the competitive international innovation landscape, experimental facilities could be connected with high performance computing resources for near real-time analysis, and resources should be provided for merging enormous and diverse data for AI/ML techniques and analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

HPC Innovation Center Feasibility Assessment, CRADA No. TC02172.0 (Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and IBM TJ Watson Research Center to research, assess feasibility and develop an implementation plan for a High Performance Computing Innovation Center (HPCIC) in the Livermore Valley Open Campus (LVOC). The ultimate goal of this work was to help advance the State of California and U.S. commercial competitiveness in the arena of High Performance Computing (HPC) by accelerating the adoption of computational science solutions, consistent with recent DOE strategy directives. The desired result of this CRADA was a well-researched, carefully analyzed market evaluation that would identify those firms in core sectors of the US economy seeking to adopt or expand their use of HPC to become more competitive globally, and to define how those firms could be helped by the HPCIC with IBM as an integral partner.

East, D. R.↗

DOE OSTI2012

HPC Innovation Center Feasibility Assessment Final Report CRADA No. TC02172.0

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and IBM TJ Watson Research Center to research, assess feasibility and develop an implementation plan for a High Performance Computing Innovation Center (HPCIC) in the Livermore Valley Open Campus (LVOC). The ultimate goal of this work was to help advance the State of California and U.S. commercial competitiveness in the arena of High Performance Computing (HPC) by accelerating the adoption of computational science solutions, consistent with recent DOE strategy directives. The desired result of this CRADA was a well-researched, carefully analyzed market evaluation that would identify those firms in core sectors of the US economy seeking to adopt or expand their use of HPC to become more competitive globally, and to define how those firms could be helped by the HPCIC with IBM as an integral partner.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Adiabatic Quantum Computation Applied to Deep Learning Networks

Training deep learning networks is a difficult task due to computational complexity, and this is traditionally handled by simplifying network topology to enable parallel computation on graphical processing units (GPUs). However, the emergence of quantum devices allows reconsideration of complex topologies. We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum computation known as quantum annealing performed by a D-Wave processor. We provide a brief description of the hardware and how it solves Ising models, how we translate our data into the corresponding Ising models, and how we use available expanded topology options to explore potential performance improvements. Although we focus on the application of quantum annealing in this article, the work discussed here is just one of three approaches we explored as part of a larger project that considers alternative means for training deep learning networks. The other approaches involve using a high performance computing (HPC) environment to automatically find network topologies with good performance and using neuromorphic computing to find a low-power solution for training deep learning networks. Our results show that our quantum approach can find good network parameters in a reasonable time despite increased network topology complexity; that HPC can find good parameters for traditional, simplified network topologies; and that neuromorphic computers can use low power memristive hardware to represent complex topologies and parameters derived from other architecture choices.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

The 2018 Basic Research Needs Workshop on Extreme Heterogeneity identified five Priority Research Directions for realizing the capabilities needed to address the challenges posed in this era of rapid technological change. This report captures the outcomes of that workshop. In the context of extreme heterogeneity, it defines basic research needs and opportunities in computer science research to develop smart and trainable operating and runtime systems, programming environments, and predictive tools that will make future systems easier to adapt to scientists’ computing needs and easier for facilities to deploy securely.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Extending SST vanadis to Add SIMT Functional Units

Sandia National Laboratories is currently investigating scalable architectural simulation capabilities, with a focus on simulating and evaluating highly scalable supercomputers for high-performance computing applications. This exploration is driven by the shift toward more specialized forms of compute and the need for a more diverse set of accurate models. This project will explore the use of General-Purpose Graphical Processing Units (GPGPUs) in high-performance computing using both physical systems and new simulator models – traditional GPUs as well as tightly-coupled SIMT accelerators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

SOMA: Observability, monitoring, and in situ analytics for exascale applications

With the rise of exascale systems and large, data-centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service-based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application-specific diagnostic data and performance data addresses these needs. Furthermore, we present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi-GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Advanced Simulation Capability for Environmental Management - Current Status and Phase II Demonstration Results - 13161

The U.S. Department of Energy (US DOE) Office of Environmental Management (EM), Office of Soil and Groundwater, is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is a state-of-the-art scientific tool and approach for understanding and predicting contaminant fate and transport in natural and engineered systems. The modular and open source high-performance computing tool facilitates integrated approaches to modeling and site characterization that enable robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project continues to make significant progress in development of computer software capabilities with an emphasis on integration of capabilities in FY12. Capability development is occurring for both the Platform and Integrated Tool-sets and High-Performance Computing (HPC) Multi-process Simulator. The Platform capabilities provide the user interface and tools for end-to-end model development, starting with definition of the conceptual model, management of data for model input, model calibration and uncertainty analysis, and processing of model output, including visualization. The HPC capabilities target increased functionality of process model representations, tool-sets for interaction with Platform, and verification and model confidence testing. The Platform and HPC capabilities are being tested and evaluated for EM applications in a set of demonstrations as part of Site Applications Thrust Area activities. The Phase I demonstration focusing on individual capabilities of the initial tool-sets was completed in 2010. The Phase II demonstration completed in 2012 focused on showcasing integrated ASCEM capabilities. For Phase II, the Hanford Site deep vadose zone (BC Cribs) served as an application site for an end-to-end demonstration of capabilities, with emphasis on integration and linkages between the Platform and HPC components. Other demonstrations, addressing attenuation-based remedies at the Savannah River Site F Area and performance assessment for a representative waste tank, illustrate integration of linked ASCEM capabilities and initial integration efforts with tools from the Cementitious Barriers Partnership. (authors)

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

High performance flight computer developed for deep space applications

The development of an advanced space flight computer for real time embedded deep space applications which embodies the lessons learned on Galileo and modern computer technology is described. The requirements are listed and the design implementation that meets those requirements is described. The development of SPACE-16 (Spaceborne Advanced Computing Engine) (where 16 designates the databus width) was initiated to support the MM2 (Marine Mark 2) project. The computer is based on a radiation hardened emulation of a modern 32 bit microprocessor and its family of support devices including a high performance floating point accelerator. Additional custom devices which include a coprocessor to improve input/output capabilities, a memory interface chip, and an additional support chip that provide management of all fault tolerant features, are described. Detailed supporting analyses and rationale which justifies specific design and architectural decisions are provided. The six chip types were designed and fabricated. Testing and evaluation of a brass/board was initiated.

Bunker, Robert L.↗

DOE OSTI2023

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

High-performance finite-difference time-domain simulations of C-Mod and ITER RF antennas

Finite-difference time-domain methods have, in recent years, developed powerful capabilities for modeling realistic ICRF behavior in fusion plasmas [1, 2, 3, 4]. When coupled with the power of modern high-performance computing platforms, such techniques allow the behavior of antenna near and far fields, and the flow of RF power, to be studied in realistic experimental scenarios at previously inaccessible levels of resolution. In this talk, we present results and 3D animations from high-performance FDTD simulations on the Titan Cray XK7 supercomputer, modeling both Alcator C-Mod’s field-aligned ICRF antenna and the ITER antenna module. Much of this work focuses on scans over edge density, and tailored edge density profiles, to study dispersion and the physics of slow wave excitation in the immediate vicinity of the antenna hardware and SOL. An understanding of the role of the lower-hybrid resonance in low-density scenarios is emerging, and possible implications of this for the NSTX launcher and power balance are also discussed. In addition, we discuss ongoing work centered on using these simulations to estimate sputtering and impurity production, as driven by the self-consistent sheath potentials at antenna surfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗