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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Exploring the capabilities of support vector machines in detecting silent data corruptions

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

I-deas TMG to NX Space Systems Thermal Model Conversion and Computational Performance Comparison

CAD/CAE packages change on a continuous basis as the power of the tools increase to meet demands. End -users must adapt to new products as they come to market and replace legacy packages. CAE modeling has continued to evolve and is constantly becoming more detailed and complex. Though this comes at the cost of increased computing requirements Parallel processing coupled with appropriate hardware can minimize computation time. Users of Maya Thermal Model Generator (TMG) are faced with transitioning from NX I -deas to NX Space Systems Thermal (SST). It is important to understand what differences there are when changing software packages We are looking for consistency in results.

Thermal Model Generator (TMG)↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗
DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Concepts for OpenMP Target Offload Resilience

Recent reliability issues with one of the fastest supercomputers in the world, Titan at Oak Ridge National Laboratory (ORNL), demonstrated the need for resilience in large-scale heterogeneous computing. OpenMP currently does not address error and failure behavior. This paper takes a first step toward resilience for heterogeneous systems by providing the concepts for resilient OpenMP offload to devices. Using real-world error and failure observations, the paper describes the concepts and terminology for resilient OpenMP target offload, including error and failure classes and resilience strategies. It details the experienced general-purpose computing graphics processing unit (GPGPU) errors and failures in Titan. It further proposes improvements in OpenMP, including a preliminary prototype design, to support resilient offload to devices for efficient handling of errors and failures in heterogeneous high-performance computing (HPC) systems.

Engelmann, Christian↗
NASA NTRS1986

Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.

Saltz, J. H.↗
DOE OSTI2016

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data point in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.

Subasi, Omer↗
DOE OSTI2025

Deployment and Evaluation of SciStream on OLCF's Advanced Computing Ecosystem (ACE)

The growing demand for real-time analysis, experimental steering, and decision-making in scientific workflows has created a need for tightly coupled integrations between experimental facilities and high-performance computing (HPC) systems. The Department of Energy’s Integrated Research Infrastructure (IRI) initiative highlights data streaming as a key capability for enabling memory-to-memory data transfers, bypassing the limitations of traditional store-and-forward models. SciStream is a toolkit developed by researchers at Argonne National Laboratory (ANL) to support such streaming by addressing cross-domain security, delegated authentication, and application transparency. We deployed and evaluated SciStream on the Oak Ridge Leadership Computing Facility’s (OLCF) Advanced Computing Ecosystem (ACE) infrastructure, leveraging the Olivine OpenShift cluster and its high-bandwidth Data Streaming Nodes (DSNs) as gateway nodes. Our evaluation included synthetic streaming workloads derived from IRI science workflows, a streaming simulator, and integration with RabbitMQ to handle low-level messaging. This report documents the deployment process, performance evaluation, and challenges encountered, along with opportunities for future improvements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

GraphMeta: Managing HPC Rich Metadata in Graphs

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes, but also from increasingly diverse metadata, which contains data provenance and arbitrary user-defined attributes in addition to traditional POSIX metadata. This ‘rich’ metadata is becoming critical to supporting advanced data management functionality such as data auditing and validation. In our prior work, we identified a graph-based model as a promising solution to uniformly manage HPC rich metadata due to its flexibility and generality. However, at the same time, graph-based HPC rich metadata anagement also introduces significant challenges to the underlying infrastructure. In this study, we first identify the challenges on the underlying infrastructure to support scalable, high-performance rich metadata management. Based on that, we introduce GraphMeta, a graphbased engine designed for this use case. It achieves performance scalability by introducing a new graph partitioning algorithm and a write-optimal storage engine. We evaluate GraphMeta under both synthetic and real HPC metadata workloads, compare it with other approaches, and demonstrate its advantages in terms of efficiency and usability for rich metadata management in HPC systems.

Dai, Dong↗
DOE OSTI2024

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

Introduction Reconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications. Methods We observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets. Results Our experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset. Discussion By assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.

Lee, Claire Songhyun↗
DOE OSTI2025

Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation

As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh

Xcompact3D is a Fortran 90–95 open-source framework designed for fast and accurate simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the flow solver Incompact3D which was initially designed in France in the mid-90’s for serial processors to solve the incompressible Navier–Stokes equations. Incompact3D was then ported to parallel High Performance Computing (HPC) systems in the early 2010’s. Very recently the capabilities of Incompact3D have been extended so that it can now tackle more flow regimes (from incompressible flows to compressible flows at low Mach numbers), resulting in the design of a new user-friendly framework called Xcompact3D. The present manuscript presents an overview of Xcompact3D with a particular focus on its functionalities, its ready-to-run simulations and a few case studies to demonstrate its impact.

17 WIND ENERGY↗
DOE OSTI2015

Evaluating Application Resilience with XRay

The rising count and shrinking feature size of transistors within modern computers is making them increasingly vulnerable to various types of soft faults. This problem is especially acute in high-performance computing (HPC) systems used for scientific computing, because these systems include many thousands of compute cores and nodes, all of which may be utilized in a single large-scale run. The increasing vulnerability of HPC applications to errors induced by soft faults is motivating extensive work on techniques to make these applications more resilient to such faults, ranging from generic techniques such as replication or checkpoint/restart to algorithm-specific error detection and tolerance techniques. Effective use of such techniques requires a detailed understanding of how a given application is affected by soft faults to ensure that (i) efforts to improve application resilience are spent in the code regions most vulnerable to faults and (ii) the appropriate resilience technique is applied to each code region. This paper presents XRay, a tool to view the application vulnerability to soft errors, and illustrates how XRay can be used in the context of a representative application. In addition to providing actionable insights into application behavior XRay automatically selects the number of fault injection experiments required to provide an informative view of application behavior, ensuring that the information is statistically well-grounded without performing unnecessary experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

Here, we provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

LADR: low-cost application-level detector for reducing silent output corruptions

Applications running on future high performance computing (HPC) systems are more likely to experience transient faults due to technology scaling trends with respect to higher circuit density, smaller transistor size and near-threshold voltage (NTV) operations. A transient fault could corrupt application state without warning, possibly leading to incorrect application output. Such errors are called silent data corruptions (SDCs). In this paper, we present LADR, a low-cost application-level SDC detector for scientific applications. LADR protects scientific applications from SDCs by watching for data anomalies in their state variables (those of scientific interest). It employs compile-time data-flow analysis to minimize the number of monitored variables, thereby reducing runtime and memory overheads while maintaining a high level of fault coverage with low false positive rates. We evaluated LADR with 4 scientific workloads and results show that LADR achieved < 80% fault coverage with only ∼ 3% runtime overheads and ∼ 1% memory overheads. As compared to prior state-of-the-art anomaly-based detection methods, SDC achieved comparable or improved fault coverage, but reduced runtime overheads by 21% ∼ 75%, and memory overheads by 35% ∼ 55% for the evaluated workloads. We believe that such an approach with low memory and runtime overheads coupled with attractive detection precision makes LADR a viable approach for assuring the correct output from large-scale high performance simulations.

Chen, Chao↗
DOE OSTI2025

NLR HPC Eagle Jobs Data and Additional Energy Metrics

Overview: Anonymized job-level records from the Eagle high-performance computing (HPC) system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, resource utilization, CPU/GPU energy consumption, and efficiency metrics. Sensitive fields (user, account, job name) are replaced with cryptographic hashes. System & Timeframe: Eagle was a 2,000-node, 8-petaflop system operated at NLR from 2019–2024. Data covers the full operational lifetime of the system. Slurm data was processed nightly; timestamps are in Mountain Time. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.eagle.job-anon.zip — Core anonymized job records (Hive-partitioned Parquet) esif.hpc.eagle.job-anon-energy-metrics.zip — Same records with additional iLO and Ganglia energy metrics datacard.md — Full dataset documentation ~13.8 million rows, 62 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct through a pipeline: Eagle Jobs API → Redpanda → StreamSets → HPCMON API → PostgreSQL. Node-level power from iLO (HP Integrated Lights-Out); GPU power from Ganglia monitoring, joined to jobs via node lists and time ranges. Preprocessing: Anonymization of name, user, and account fields via cryptographic hashing Derived columns: queue_wait, cpu_eff, max_mem_eff Simplified job state mapping (e.g., "CANCELLED BY 12345" → "CANCELLED") QoS accounting rules (buy-in, standby, or Slurm QoS value) CPU energy estimated from TDP (200W, Intel Xeon Gold 6154, 18 cores) Timezone-aware columns (_tz) sourced from LEX accounting database to correctly handle DST transitions Key Variables: Scheduling: job_id, partition, state_simple, submit_time_tz, start_time_tz, end_time_tz, queue_waitResources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, node_energy_total_watt_hours (iLO), gpu0/1_energy_total_watt_hours (Ganglia) Partitions: bigmem, bigmem-8600, bigscratch, csc, dav, ddn, debug, gpu, haswell, long, mono, short, standard Job States: CANCELLED, COMPLETED, FAILED, NODE_FAIL, OUT_OF_MEMORY, PENDING, RUNNING, TIMEOUT QoS Levels: Unknown, normal, buy-in, debug, penalty, high, standby Important Notes: Non-_tz timestamp columns may be off by one hour across DST boundaries; use _tz columns for time difference calculations Energy fields are null for jobs without monitoring coverage Job step records and raw Slurm JSONB fields are excluded from this extract Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ultrathin Magnesium-Based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Over recent years, tantalum (Ta) has emerged as a promising candidate for transmon qubits, surpassing conventional counterparts in terms of coherence time. However, amorphous surface Ta oxide layer may introduce dielectric loss, ultimately placing a limit on the coherence time. In this study, a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer is presented. Synchrotron-based X-ray photoelectron spectroscopy studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, it is demonstrated that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Moreover, an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation is established based on computational modeling. Further, this work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, the findings pave the way for the realization of large-scale, high-performance quantum computing systems.

36 MATERIALS SCIENCE↗