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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An empirical study of I/O separation for burst buffers in HPC systems

To meet the exascale I/O requirements for the High-Performance Computing (HPC), a new I/O subsystem, Burst Buffer, based on solid state drives (SSD), has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation that requires costly garbage collection (GC) and increases the number of bytes written to the SSD. To address this data fragmentation challenge, a new multi-stream feature has been developed for SSDs. In this work, we develop an I/O Separation scheme called BIOS to leverage this multi-stream feature to group the I/O streams based on the user IDs. We propose a stream-aware scheduling policy based on burst buffer pools in the workload manager, and integrate the BIOS with the workload manager to optimize the I/O separation scheme in burst buffer. We evaluate the proposed framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. Experimental results show that the BIOS could improve the performance by 1.44x on average and reduce the Write Amplification Factor (WAF) by up to 1.20x. Finally, these demonstrate the potential benefits of the I/O separation scheme for solid state storage systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development and Commercialization of an IDAES-Based Power Plant Performance Monitoring and Optimization System

DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Clinical benefits of new immobilization system for hypofractionated radiotherapy of intrahepatic hepatocellular carcinoma by helical tomotherapy

Objective: A comprehensive clinical evaluation was conducted, assessing the Body Pro-Lok immobilization and positioning system to facilitate hypofractionated radiotherapy of intrahepatic hepatocellular carcinoma (HCC), using helical tomotherapy to improve treatment precision. Methods: Clinical applications of the Body Pro-Lok system were investigated (as above) in terms of interfractional and intrafractional setup errors and compressive abdominal breath control. To assess interfractional setup errors, a total of 42 patients who were given 5 to 20 fractions of helical tomotherapy for intrahepatic HCC were analyzed. Overall, 15 patients were immobilized using simple vacuum cushion (group A), and the Body Pro-Lok system was used in 27 patients (group B), performing megavoltage computed tomography (MVCT) scans 196 times and 435 times, respectively. Pretreatment MVCT scans were registered to the planning kilovoltage computed tomography (KVCT) for error determination, and group comparisons were made. To establish intrafractional setup errors, 17 patients with intrahepatic HCC were selected at random for immobilization by Body Pro-Lok system, undergoing MVCT scans after helical tomotherapy every week. A total of 46 MVCT re-scans were analyzed for this purpose. In researching breath control, 12 patients, randomly selected, were immobilized by Body Pro-Lok system and subjected to 2-phase 4-dimensional CT (4DCT) scans, with compressive abdominal control or in freely breathing states, respectively. Respiratory-induced liver motion was then compared. Results: Mean interfractional setup errors were as follows: (1) group A: X, 2.97 ± 2.47 mm; Y, 4.85 ± 4.04 mm; and Z, 3.77 ± 3.21 mm; pitch, 0.66 ± 0.62°; roll, 1.09 ± 1.06°; and yaw, 0.85 ± 0.82°; and (2) group B: X, 2.23 ± 1.79 mm; Y, 4.10 ± 3.36 mm; and Z, 1.67 ± 1.91 mm; pitch, 0.45 ± 0.38°; roll, 0.77 ± 0.63°; and yaw, 0.52 ± 0.49°. Between-group differences were statistically significant in 6 directions (p < 0.05). Mean intrafractional setup errors with use of the Body Pro-Lok system were as follows: X, 0.41 ± 0.46 mm; Y, 0.86 ± 0.80 mm; Z, 0.33 ± 0.44 mm; and roll, 0.12 ± 0.19°. Mean liver-induced respiratory motion determinations were as follows: (1) abdominal compression: X, 2.33 ± 1.22 mm; Y, 5.11 ± 2.05 mm; Z, 2.13 ± 1.05 mm; and 3D vector, 6.22 ± 1.94 mm; and (2) free breathing: X, 3.48 ± 1.14 mm; Y, 9.83 ± 3.00 mm; Z, 3.38 ± 1.59 mm; and 3D vector, 11.07 ± 3.16 mm. Between-group differences were statistically different in 4 directions (p < 0.05). Conclusions: The Body Pro-Lok system is capable of improving interfractional and intrafractional setup accuracy and minimizing tumor movement owing to respirations in patients with intrahepatic HCC during hypofractionated helical tomotherapy.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)

This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

HPC at NREL: FY 2019

Brochure featuring Fiscal Year 2019 accomplishments at the NREL High-Performance Computing User Facility and Data Center at the Energy Systems Integration Facility, including information on the facility's HPC systems as well as highlights from several modeling and simulation projects across the spectrum of energy efficiency and renewable energy technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO 2 interferometer on DIII-D

Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the C O 2 phase data (simple and crosspower spectrograms). All C O 2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical C O 2 interferometer chord passing near the center is better for ML-based detection of AEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Costa - Introduction to 2015 Annual Report

In parallel with Sandia National Laboratories having two major locations (NM and CA), along with a number of smaller facilities across the nation, so too is the distribution of scientific, engineering and computing resources. As a part of Sandia’s Institutional Computing Program, CA site-based Sandia computer scientists and engineers have been providing mission and research staff with local CA resident expertise on computing options while also focusing on two growing high performance computing research problems. The first is how to increase system resilience to failure, as machines grow larger, more complex and heterogeneous. The second is how to ensure that computer hardware and configurations are optimized for specialized data analytical mission needs within the overall Sandia computing environment, including the HPC subenvironment. All of these activities support the larger Sandia effort in accelerating development and integration of high performance computing into national security missions. Sandia continues to both promote national R&D objectives, including the recent Presidential Executive Order establishing the National Strategic Computing Initiative and work to ensure that the full range of computing services and capabilities are available for all mission responsibilities, from national security to energy to homeland defense.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Highly interactive, steered scientific workflows on HPC systems: optimizing design solutions

Scientific workflows are becoming increasingly important in high performance computing (HPC) settings, as the feasibility and appeal of many simultaneous heterogeneous tasks increases with increasing hardware capabilities. Currently no HPC-based workflow platform supports a dynamically adaptable workflow with interactive steering and analysis at run-time. Furthermore, for most workflow programs, compute resources are fixed for a given instance, resulting in a possible waste of expensive allocation resources when tasks are spawned and killed. Here we describe the design and testing of a run-time-interactive, adaptable, steered workflow tool capable of executing thousands of parallel tasks without an MPI programming model, using a database management system to facilitate task management through multiple live connections. We find that on the Oak Ridge Leadership Computing Facility pre-exascale Summit supercomputer it is possible to launch and interactively steer workflows with thousands of simultaneous tasks with negligible latency. For the case of particle simulation and analysis tasks that run for minutes to hours, this paradigm offers the prospect of a robust and efficient means to perform simulation-space exploration with on-the-fly analysis and adaptation.

Ossyra, John R↗
DOE OSTI2025

QEpsilon v0.1.0

QEpsilon is a Python package designed to minimize the effort required to build a data-driven quantum master equation of an open quantum system and to perform time evolution of the master equation. Applications of QEpsilon span from quantum computing to condensed matter systems.

Xie, Pinchen [Lawrence Berkeley National Laborator↗
DOE OSTI2013

HPCG Benchmark Technical Specification

The High Performance Conjugate Gradient (HPCG) benchmark [cite SNL, UTK reports] is a tool for ranking computer systems based on a simple additive Schwarz, symmetric Gauss-Seidel preconditioned conjugate gradient solver. HPCG is similar to the High Performance Linpack (HPL), or Top 500, benchmark [1] in its purpose, but HPCG is intended to better represent how today’s applications perform. In this paper we describe the technical details of HPCG: how it is designed and implemented, what code transformations are permitted and how to interpret and report results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Planning for Pre-Exascale Platform Environment (Fiscal Year 2015 Level 2 Milestone 5216)

This Plan for ASC Pre-Exascale Platform Environments document constitutes the deliverable for the fiscal year 2015 (FY15) Advanced Simulation and Computing (ASC) Program Level 2 milestone Planning for Pre-Exascale Platform Environment. It acknowledges and quantifies challenges and recognized gaps for moving the ASC Program towards effective use of exascale platforms and recommends strategies to address these gaps. This document also presents an update to the concerns, strategies, and plans presented in the FY08 predecessor document that dealt with the upcoming (at the time) petascale high performance computing (HPC) platforms. With the looming push towards exascale systems, a review of the earlier document was appropriate in light of the myriad architectural choices currently under consideration. The ASC Program believes the platforms to be fielded in the 2020s will be fundamentally different systems that stress ASC’s ability to modify codes to take full advantage of new or unique features. In addition, the scale of components will increase the difficulty of maintaining an errorfree system, thus driving new approaches to resilience and error detection/correction. The code revamps of the past, from serial- to vector-centric code to distributed memory to threaded implementations, will be revisited as codes adapt to a new message passing interface (MPI) plus “x” or more advanced and dynamic programming models based on architectural specifics. Development efforts are already underway in some cases, and more difficult or uncertain aspects of the new architectures will require research and analysis that may inform future directions for program choices. In addition, the potential diversity of system architectures may require parallel if not duplicative efforts to analyze and modify environments, codes, subsystems, libraries, debugging tools, and performance analysis techniques as well as exploring new monitoring methodologies. It is difficult if not impossible to selectively eliminate some of these activities until more information is available through simulations of potential architectures, analysis of systems designs, and informed study of commodity technologies that will be the constituent parts of future platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Memory access optimization for particle operations in computational fluid dynamics-discrete element method simulations

Computational Fluid Dynamics - Discrete Element Method is used to model gas-solid systems in several applications in energy, pharmaceutical and petrochemical industries. Computational performance bottlenecks often limit the problem sizes that can be simulated at industrial scale. The data structures used to store several millions of particles in such large-scale simulations have a large memory footprint that does not fit into the processor cache hierarchies on current high-performance-computing platforms, leading to reduced computational performance. This paper specifically addresses this aspect of memory access bottlenecks in industrial scale simulations. The use of space-filling curves to improve memory access patterns is described and their impact on computational performance is quantified in both shared and distributed memory parallelization paradigms. The Morton space filling curve applied to uniform grids and k-dimensional tree partitions are used to reorder the particle data-structure thus improving spatial and temporal locality in memory. The performance impact of these techniques when applied to two benchmark problems, namely the homogeneous-cooling-system and a fluidized-bed, are presented. We report these optimization techniques lead to approximately two-fold performance improvement in particle focused operations such as neighbor-list creation and data-exchange, with ~ 1.5 times overall improvement in a fluidization simulation with 1.27 million particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Simulations of Quantum Approximate Optimization Algorithm on HPC-QC Integrated Systems

The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising tool for accelerating optimization processes in the Noisy Intermediate-Scale Quantum (NISQ) era. Compared to classical methods, QAOA efficiently solves optimization problems, often formulated as Quadratic Unconstrained Binary Optimization (QUBO) problems. Classical quantum simulators are crucial for evaluating quantum algorithms due to limited quantum resources. However, QAOA's performance can vary with different simulation methods. This study analyzes QAOA's performance using various quantum simulators (e.g., density _matrix, statevector, and matrix_product_state) and demonstrates the benefits of HPC-QC integrated systems in solving QUBO problems on an active learning workflow. By simulating QAOA on dense, large-matrix QUBO problems, we evaluate accuracy and problem-solving time. We also assess QAOA's performance on local computers and HPC-QC inte-grated systems, using Oak Ridge Leadership Computing Facility (OLCF)'s Frontier supercomputer with local Qiskit Aer and remote IBM Quantum simulators.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
DOE OSTI2019

Balar: A SST GPU Component for Performance Modeling and Profiling

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SST-GPU: A Scalable SST GPU Component for Performance Modeling and Profiling

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of unprecedented amounts of data have created a space for massively parallel accelerators capable of maintaining context for thousands of concurrent threads resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. One path for the design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. The SST framework has been proven to scale up to run simulations containing tens of thousands of nodes. A previous report described the initial integration of the open-source, execution-driven GPU simulator, GPGPU-Sim, into the SST framework. This report discusses the results of the integration and how to use the new GPU component in SST. It also provides examples of what it can be used to analyze and a correlation study showing how closely the execution matches that of a Nvidia V100 GPU when running kernels and mini-apps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in an Earth system model. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO 2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO 2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model based on a 9-day lag had significantly fewer nodes than a graph based on monthly means. Furthermore, this demonstrates our method’s ability to perform dimension reduction while still uncovering source-to-impact pathways.

Changepoint detection↗