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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Energy 101: Energy Efficient Data Centers

Data centers provide mission-critical computing functions vital to the daily operation of top U.S. economic, scientific, and technological organizations. These data centers consume large amounts of energy to run and maintain their computer systems, servers, and associated high-performance components—up to 3% of all U.S. electricity powers data centers. And as more information comes online, data centers will consume even more energy. Data centers can become more energy efficient by incorporating features like power-saving "stand-by" modes, energy monitoring software, and efficient cooling systems instead of energy-intensive air conditioners. These and other efficiency improvements to data centers can produce significant energy savings, reduce the load on the electric grid, and help protect the nation by increasing the reliability of critical computer operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Cardiac Motion During Deep-Inspiration Breath-Hold: Implications for Breast Cancer Radiotherapy

Purpose: Many patients with left-sided breast cancer receive adjuvant radiotherapy during deep-inspiration breath hold (DIBH) to minimize radiation exposure to the heart. We measured the displacement of the left anterior descending artery (LAD) and heart owing to cardiac motion during DIBH, relative to the standard tangential fields for left breast cancer radiotherapy. Methods and Materials: A total of 20 patients who had undergone computed tomography-based coronary angiography with retrospective electrocardiographic gating were randomly selected for the present study. The patients underwent scanning during DIBH to control the influence of respiration on cardiac motion. Standard medial and lateral tangential fields were placed, and the LADs were contoured on the systolic- and diastolic-phase computed tomography data sets by the clinicians. Displacement of the LAD during cardiac contractions was calculated in three directions: toward the posterior edge of the treatment fields, left-right, and anteroposterior. Displacement of the entire heart was measured on the maximal and minimal intensity projection computed tomography images. Results: The mean displacement of the LAD from cardiac contraction without the influence of respiration for 20 patients was 2.3 mm (range, 0.7-3.8) toward the posterior edge of the treatment fields, 2.6 mm (range, 1.0-6.8) in the left-right direction, and 2.3 mm (range, 0.6-6.5) in the anteroposterior direction. At least 30% of the LAD volume was displaced >5 mm in any direction in 2 patients (10%), and <10% of the LAD volume was displaced >5 mm in 10 patients (50%). The extent of displacement of the heart periphery during cardiac motion was negligible near the treatment fields. Conclusions: Displacement of the heart periphery near the treatment fields was negligible during DIBH; however, displacement of the LAD from cardiac contraction varied substantially between and within patients. We recommend maintaining {>=}5 mm of distance between the LAD and the field edge for patients undergoing breast cancer radiotherapy during DIBH.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Interfractional Variations in the Setup of Pelvic Bony Anatomy and Soft Tissue, and Their Implications on the Delivery of Proton Therapy for Localized Prostate Cancer

Purpose: To quantify daily variations in the anatomy of patients undergoing radiation therapy for prostate carcinoma, to estimate their effect on dose distribution, and to evaluate the effectiveness of current standard planning and setup approaches employed in proton therapy. Methods: We used series of computed tomography data, which included the pretreatment scan, and between 21 and 43 in-room scans acquired on different treatment days, from 10 patients treated with intensity-modulated radiation therapy at Morristown Memorial Hospital. Variations in femur rotation angles, thickness of subcutaneous adipose tissue, and physical depth to the distal surface of the prostate for lateral beam arrangement were recorded. Proton dose distributions were planned with the standard approach. Daily variations in the location of the prescription isodose were evaluated. Results: In all 10 datasets, substantial variation was observed in the lateral tissue thickness (standard deviation of 1.7-3.6 mm for individual patients, variations of >5 mm from the planning computed tomography observed in all series), and femur rotation angle (standard deviation between 1.3{sup o} and 4.8{sup o}, with the maximum excursion exceeding 10{sup o} in 6 of 10 datasets). Shifts in the position of treated volume (98% isodose) were correlated with the variations in the lateral tissue thickness. Conclusions: Analysis suggests that, combined with image-guided setup verification, the range compensator expansion technique prevents loss of dose to target from femur rotation and soft-tissue deformation, in the majority of cases. Anatomic changes coupled with the uncertainties of particle penetration in tissue restrict possibilities for margin reduction in proton therapy of prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Segmentation of tomography datasets using 3D convolutional neural networks

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as X-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. Here, in this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new version of FCDenseNet which we extended to 3D. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (intersection-over-union of 95.56% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 × 852 × 250 voxel 3D volumes in only ~60 s, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Collisional excitation of C2H− by H2: New interaction potential and scattering calculations

Interstellar anions play an important role in astrochemistry as being tracers of the physical and chemical conditions in cold molecular clouds and circumstellar gas. The local thermodynamic equilibrium is generally not fulfilled in media where anions are detected and radiative and collisional data are required to model the observed lines. The C2H− anion has not yet been detected in the interstellar medium; however, collisional data could be used for non-LTE models that would help in identifying the most intense lines. For this purpose, we have computed the first 4D potential energy surface (PES) of the C2H−–H2 complex using an explicitly correlated coupled-cluster approach. The PES is characterized by a single deep minimum with a well-depth of 924.96 cm−1. From this interaction potential, we derived excitation cross sections and rate coefficients of C2H− induced by collisions with para- and ortho-H2. The results obtained for collisions with para-H2 are compared to previous calculations performed using a 2D-PES obtained from an average over H2 rotations.

Chemistry↗
DOE OSTI2013

High Energy Physics and Nuclear Physics Network Requirements

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements needed by instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In August 2013, ESnet and the DOE SC Offices of High Energy Physics (HEP) and Nuclear Physics (NP) organized a review to characterize the networking requirements of the programs funded by the HEP and NP program offices. Several key findings resulted from the review. Among them: 1. The Large Hadron Collider's ATLAS (A Toroidal LHC Apparatus) and CMS (Compact Muon Solenoid) experiments are adopting remote input/output (I/O) as a core component of their data analysis infrastructure. This will significantly increase their demands on the network from both a reliability perspective and a performance perspective. 2. The Large Hadron Collider (LHC) experiments (particularly ATLAS and CMS) are working to integrate network awareness into the workflow systems that manage the large number of daily analysis jobs (1 million analysis jobs per day for ATLAS), which are an integral part of the experiments. Collaboration with networking organizations such as ESnet, and the consumption of performance data (e.g., from perfSONAR [PERformance Service Oriented Network monitoring Architecture]) are critical to the success of these efforts. 3. The international aspects of HEP and NP collaborations continue to expand. This includes the LHC experiments, the Relativistic Heavy Ion Collider (RHIC) experiments, the Belle II Collaboration, the Large Synoptic Survey Telescope (LSST), and others. The international nature of these collaborations makes them heavily reliant on transoceanic connectivity, which is subject to longer term service disruptions than terrestrial connectivity. The network engineering aspects of undersea connectivity will continue to be a significant part of the planning, deployment, and operation of the data analysis infrastructure for HEP and NP experiments for the foreseeable future. Given their critical dependency on networking services, the experiments have expressed the need for tight integration (both technically and operationally) of the domestic and the transoceanic parts of the network infrastructure that supports the experiments. 4. The datasets associated with simulations continue to increase in size, and the need to move these datasets between analysis centers is placing ever-increasing demands on networks and on data management systems at the supercomputing centers. In addition, there is a need to harmonize cybersecurity practice with the data transfer performance requirements of the science. This report expands on these points, and addresses others as well. The report contains a findings section in addition to the text of the case studies discussed during the review.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Comparison of Raman Scattering Measurements and Modeling in NIF Ignition Experiments

Recent NIF indirect-drive experiments have shown significant Raman scattering from the inner beams. NIF data has motivated improvements to rad-hydro modeling, leading to the 'high flux model' [M. D. Rosen et al., HEDP 7, 180 (2011)]. Cross-beam energy transfer [P. A. Michel et al., Phys. Plasmas 17, 056305 (2010] in the laser entrance hole is an important tool for achieving round implosions, and is uniformly distributed across the laser spot in rad-hydro simulations (but not necessarily in experiments). We find the Raman linear gain spectra computed with these plasma conditions agree well in time-dependent peak wavelength with the measured data, especially when overlapping laser-beam intensities are used. More detailed, spatially non-uniform modeling of the cross-beam transfer has been performed. The resulting gains better follow the time history of the measured backscatter. We shall present the impact of spatially non-uniform energy transfer on SRS gain. This metric is valid when amplification is in a linear regime, and so we shall also present an assessment of whether electron trapping in Langmuir waves can play a role in these shots.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

EqualChance: Addressing Intra-set Write Variation to Increase Lifetime of Non-volatile Caches

To address the limitations of SRAM such as high-leakage and low-density, researchers have explored use of non-volatile memory (NVM) devices, such as ReRAM (resistive RAM) and STT-RAM (spin transfer torque RAM) for designing on-chip caches. A crucial limitation of NVMs, however, is that their write endurance is low and the large intra-set write variation introduced by existing cache management policies may further exacerbate this problem, thereby reducing the cache lifetime significantly. We present EqualChance, a technique to increase cache lifetime by reducing intra-set write variation. EqualChance works by periodically changing the physical cache-block location of a write-intensive data item within a set to achieve wear-leveling. Simulations using workloads from SPEC CPU2006 suite and HPC (high-performance computing) field show that EqualChance improves the cache lifetime by 4.29X. Also, its implementation overhead is small, and it incurs very small performance and energy loss.

cache lifetime↗
DOE OSTI2018

Designing Algorithms for the EMU Migrating-threads-based Architecture

The decades-old memory bottleneck problem for data-intensive applications is getting worse as the processor core counts continue to increase. Workloads with sparse memory access characteristics only achieve a fraction of a system’s total memory bandwidth. EMU architecture provides a radical approach to the issue by migrating the computational threads to the location where the data resides. The system enables access to a large PGAS-type memory for hundreds of nodes via a Cilk- based multi-threaded execution scheme.EMU architecture brings brand new challenges in application design and development. Data distribution and thread creation strategies play a crucial role in achieving optimal performance in the EMU platform. In this work, we identify several design considerations that need to be taken care of while developing applications for the new architecture and we evaluate their performance effects on the EMU-chick hardware. We also present a modified BFS algorithm for the EMU system and give experi- mental results for its execution on the platform.

Belviranli, Mehmet E.↗
DOE OSTI2014

Beam dynamics in an ultra-low energy storage rings (review of existing facilities and feasibility studies for future experiments)

Storage rings operating at ultra-low energies and in particular electrostatic storage rings have proven to be invaluable tools for atomic and molecular physics. Due to the mass independence of the electrostatic rigidity, these machines are able to store a wide range of different particles, from light ions to heavy singly charged bio-molecules. However, earlier measurements showed strong limitations on beam intensity, fast decay of ion current, reduced life time etc. The nature of these effects was not fully understood. Also a large variety of experiments in future generation ultra-low energy storage and decelerator facilities including in-ring collision studies with a reaction microscope require a comprehensive investigation of the physical processes involved into the operation of such rings. In this paper, we present review of non-linear and long term beam dynamics studies on example of the ELISA, AD Recycler, TSR and USR rings using the computer codes BETACOOL, OPERA-3D and MAD-X. The results from simulations were benchmarked against experimental data of beam losses in the ELISA storage ring. We showed that decay of beam intensity in ultra-low energy rings is mainly caused by ion losses on ring aperture due to multiple scattering on residual gas. Beam is lost on ring aperture due to small ring acceptance. Rate of beam losses increases at high intensities because of the intra-beam scattering effect adds to vacuum losses. Detailed investigations into the ion kinetics under consideration of the effects from electron cooling and multiple scattering of the beam on a supersonic gas jet target have been carried out as well. The life time, equilibrium momentum spread and equilibrium lateral spread during collisions with this internal gas jet target were estimated. In addition, the results from experiments at the TSR ring, where low intensity beam of CF{sup +} ions at 93 keV/u has been shrunk to extremely small dimensions have been reproduced. Based on these simulations, conditions for stable ring operation with extremely low emittance beam are presented. Finally, results from studies into the interaction of ions with a gas jet target at 3–30 keV energy range are summarized.

43 PARTICLE ACCELERATORS↗
DOE OSTI2012

Radiation modeling using the Uintah heterogeneous CPU/GPU runtime system. In: XSEDE '12 Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond, Article No. 4

The Uintah Computational Framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids, together with a novel asynchronou task-based approach with fully automated load balancing. Uintah demonstrates excellent weak and strong scalability at full machine capacity on XSEDE resources such as Ranger and Kraken, and through the use of a hybrid memory approach based on a combination of MPI and Pthreads, Uintah now runs on up to 262k cores on the DOE Jaguar system. In order to extend Uintah to heterogeneous systems, with ever-increasing CPU core counts and additional on-node GPUs, a new dynamic CPU-GPU task scheduler is designed and evaluated in this study. This new scheduler enables Uintah to fully exploit these architectures with support for asynchronous, out-of-order scheduling of both CPU and GPU computational tasks. A new runtime system has also been implemented with an added multi-stage queuing architecture for efficient scheduling of CPU and GPU tasks. This new runtime system automatically handles the details of asynchronous memory copies to and from the GPU and introduces a novel method of pre-fetching and preparing GPU memory prior to GPU task execution. In this study this new design is examined in the context of a developing, hierarchical GPU-based ray tracing radiation transport model that provides Uintah with additional capabilities for heat transfer and electromagnetic wave propagation. The capabilities of this new scheduler design are tested by running at large scale on the modern heterogeneous systems, Keeneland and TitanDev, with up to 360 and 960 GPUs respectively. On these systems, we demonstrate significant speedups per GPU against a standard CPU core for our radiation problem.

Humphrey, Alan↗
DOE OSTI2011

A report documenting the completion of the Los Alamos National Laboratory portion of the ASC level II milestone ""Visualization on the supercomputing platform

This report provides documentation for the completion of the Los Alamos portion of the ASC Level II 'Visualization on the Supercomputing Platform' milestone. This ASC Level II milestone is a joint milestone between Sandia National Laboratory and Los Alamos National Laboratory. The milestone text is shown in Figure 1 with the Los Alamos portions highlighted in boldfaced text. Visualization and analysis of petascale data is limited by several factors which must be addressed as ACES delivers the Cielo platform. Two primary difficulties are: (1) Performance of interactive rendering, which is the most computationally intensive portion of the visualization process. For terascale platforms, commodity clusters with graphics processors (GPUs) have been used for interactive rendering. For petascale platforms, visualization and rendering may be able to run efficiently on the supercomputer platform itself. (2) I/O bandwidth, which limits how much information can be written to disk. If we simply analyze the sparse information that is saved to disk we miss the opportunity to analyze the rich information produced every timestep by the simulation. For the first issue, we are pursuing in-situ analysis, in which simulations are coupled directly with analysis libraries at runtime. This milestone will evaluate the visualization and rendering performance of current and next generation supercomputers in contrast to GPU-based visualization clusters, and evaluate the perfromance of common analysis libraries coupled with the simulation that analyze and write data to disk during a running simulation. This milestone will explore, evaluate and advance the maturity level of these technologies and their applicability to problems of interest to the ASC program. In conclusion, we improved CPU-based rendering performance by a a factor of 2-10 times on our tests. In addition, we evaluated CPU and CPU-based rendering performance. We encourage production visualization experts to consider using CPU-based rendering solutions when it is appropriate. For example, on remote supercomputers CPU-based rendering can offer a means of viewing data without having to offload the data or geometry onto a CPU-based visualization system. In terms of comparative performance of the CPU and CPU we believe that further optimizations of the performance of both CPU or CPU-based rendering are possible. The simulation community is currently confronting this reality as they work to port their simulations to different hardware architectures. What is interesting about CPU rendering of massive datasets is that for part two decades CPU performance has significantly outperformed CPU-based systems. Based on our advancements, evaluations and explorations we believe that CPU-based rendering has returned as one viable option for the visualization of massive datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A fast band–Krylov eigensolver for macromolecular functional motion simulation on multicore architectures and graphics processors

We introduce a new iterative Krylov subspace-based eigensolver for the simulation of macromolecular motions on desktop multithreaded platforms equipped with multicore processors and, possibly, a graphics accelerator (GPU). The method consists of two stages, with the original problem first reduced into a simpler band-structured form by means of a high-performance compute-intensive procedure. This is followed by a memory-intensive but low-cost Krylov iteration, which is off-loaded to be computed on the GPU by means of an efficient data-parallel kernel. The experimental results reveal the performance of the new eigensolver. Concretely, when applied to the simulation of macromolecules with a few thousands degrees of freedom and the number of eigenpairs to be computed is small to moderate, the new solver outperforms other methods implemented as part of high-performance numerical linear algebra packages for multithreaded architectures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Power and Performance Trade-offs for Space Time Adaptive Processing

Computational efficiency – performance relative to power or energy – is one of the most important concerns when designing RADAR processing systems. This paper analyzes power and performance trade-offs for a typical Space Time Adaptive Processing (STAP) application. We study STAP implementations for CUDA and OpenMP on two computationally efficient architectures, Intel Haswell Core I7-4770TE and NVIDIA Kayla with a GK208 GPU. We analyze the power and performance of STAP’s computationally intensive kernels across the two hardware testbeds. We also show the impact and trade-offs of GPU optimization techniques. We show that data parallelism can be exploited for efficient implementation on the Haswell CPU architecture. The GPU architecture is able to process large size data sets without increase in power requirement. The use of shared memory has a significant impact on the power requirement for the GPU. A balance between the use of shared memory and main memory access leads to an improved performance in a typical STAP application.

Gawande, Nitin A.↗