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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Achieving High Performance With TCP Over 40 GbE on NUMA Architectures for CMS Data Acquisition

TCP and the socket abstraction have barely changed over the last two decades, but at the network layer there has been a giant leap from a few megabits to 100 gigabits in bandwidth. At the same time, CPU architectures have evolved into the multicore era and applications are expected to make full use of all available resources. Applications in the data acquisition domain based on the standard socket library running in a Non-Uniform Memory Access (NUMA) architecture are unable to reach full efficiency and scalability without the software being adequately aware about the IRQ (Interrupt Request), CPU and memory affinities. During the first long shutdown of LHC, the CMS DAQ system is going to be upgraded for operation from 2015 onwards and a new software component has been designed and developed in the CMS online framework for transferring data with sockets. This software attempts to wrap the low-level socket library to ease higher-level programming with an API based on an asynchronous event driven model similar to the DAT uDAPL API. It is an event-based application with NUMA optimizations, that allows for a high throughput of data across a large distributed system. This paper describes the architecture, the technologies involved and the performance measurements of the software in the context of the CMS distributed event building.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Symmetric Indefinite Linear Solver Using OpenMP Task on Multicore Architectures

Recently, the Open Multi-Processing (OpenMP) standard has incorporated task-based programming, where a function call with input and output data is treated as a task. At run time, OpenMP's superscalar scheduler tracks the data dependencies among the tasks and executes the tasks as their dependencies are resolved. On a shared-memory architecture with multiple cores, the independent tasks are executed on different cores in parallel, thereby enabling parallel execution of a seemingly sequential code. With the emergence of many-core architectures, this type of programming paradigm is gaining attention-not only because of its simplicity, but also because it breaks the artificial synchronization points of the program and improves its thread-level parallelization. In this paper, we use these new OpenMP features to develop a portable high-performance implementation of a dense symmetric indefinite linear solver. Obtaining high performance from this kind of solver is a challenge because the symmetric pivoting, which is required to maintain numerical stability, leads to data dependencies that prevent us from using some common performance-improving techniques. To fully utilize a large number of cores through tasking, while conforming to the OpenMP standard, we describe several techniques. Our performance results on current many-core architectures-including Intel's Broadwell, Intel's Knights Landing, IBM's Power8, and Arm's ARMv8-demonstrate the portable and superior performance of our implementation compared with the Linear Algebra PACKage (LAPACK). The resulting solver is now available as a part of the PLASMA software package.

Computer Science↗
DOE OSTI2013

Architectures and Control of Submodule Integrated DC-DC Converters for Photovoltaic Applications

This paper describes photovoltaic (PV) module architectures with parallel-connected submodule-integrated dc-dc converters (subMICs) that improve efficiency of energy capture in the presence of partial shading or other mismatch conditions. The subMICs are bidirectional isolated dc-dc converters capable of injecting or subtracting currents to balance the module substring voltages. When no mismatches are present, the subMICs are simply shut down, resulting in zero insertion losses. It is shown that the objective of minimum subMIC power processing can be solved as a linear programming problem. A simple close-to-optimal distributed control approach is presented that allows autonomous subMIC control without the need for a central controller or any communication among the subMICs. Furthermore, the proposed control approach is well suited for an isolated-port architecture, which yields additional practical advantages including reduced subMIC power and voltage ratings. The architectures and the control approach are validated by simulations and experimental results using three bidirectional flyback subMICs attached to a standard 180-W, 72-cell PV module, yielding greater than 98% module-level power processing efficiency for a mismatch less than 25%.

Olalla, C↗
DOE OSTI2020

Isochronous Architecture-Based Voltage-Active Power Droop for Multi-Inverter Systems

This article proposes an isochronous architecture for parallel inverters with only voltage-active power droop (VP-D) control for improving active power sharing as well as plug-and-play of multi-inverter-based distributed energy resources (DERs). The isochronous framework obviates the need for an explicit regulation of the frequency while allowing for sharing of reactive power. The article shows that the detrimental effects of circulating currents between inverters can be addressed in the framework developed. The isochronous architecture is implemented by employing a GPS to disseminate clock timing signals that enable the microgrid to maintain the nominal system frequency. Small signal eigenvalue analysis of a Multi-inverter Microgrid system near the steady-state operating point is presented to evaluate the system stability. Moreover, unlike traditional droop-based methods, the isochronous strategy lends itself to analytical guarantees which are developed in the article. Furthermore, the effect of delays in receiving GPS clock signals by an inverter is experimentally characterized and an inverter isolation mechanism is proposed that disconnects inverters facing large GPS communication delays. Hardware experiments on an 1.2 kVA-prototype and controller-hardware-in-the-loop validation on the CIGRE distribution network are conducted to demonstrate the effectiveness of the proposed architecture towards active and reactive power sharing between inverters with load scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures

Purpose: Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. Methods: A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the GATE/GEANT4 Monte Carlo application for homogeneous and heterogeneous geometries. Results: Comparisons with GATE/GEANT4 for various geometries show deviations within 2%–1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10{sup 7} primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. Conclusions: MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

The Influence of Repressor DNA Binding Site Architecture on Transcriptional Control

How the architecture of DNA binding sites dictates the extent of repression of promoters is not well understood. Here, we addressed the importance of the number and information content of the three direct repeats (DRs) in the binding and repression of the icdA promoter by the phosphorylated form of the global Escherichia coli repressor ArcA (ArcA-P). We show that decreasing the information content of the two sites with the highest information (DR1 and DR2) eliminated ArcA binding to all three DRs and ArcA repression of icdA. Unexpectedly, we also found that DR3 occupancy functions principally in repression, since mutation of this low-information-content site both eliminated DNA binding to DR3 and significantly weakened icdA repression, despite the fact that binding to DR1 and DR2 was intact. In addition, increasing the information content of any one of the three DRs or addition of a fourth DR increased ArcA-dependent repression but perturbed signal-dependent regulation of repression. Thus, our data show that the information content and number of DR elements are critical architectural features for maintaining a balance between high-affinity binding and signal-dependent regulation of icdA promoter function in response to changes in ArcA-P levels. Optimization of such architectural features may be a common strategy to either dampen or enhance the sensitivity of DNA binding among the members of the large OmpR/PhoB family of regulators as well as other transcription factors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Abstractions and Directives for Adapting Wavefront Algorithms to Future Architectures

Architectures are rapidly evolving, and exascale machines are expected to offer billion-way concurrency. We need to rethink algorithms, languages and programming models among other components in order to migrate large scale applications and explore parallelism on these machines. Although directive-based programming models allow programmers to worry less about programming and more about science, expressing complex parallel patterns in these models can be a daunting task especially when the goal is to match the performance that the hardware platforms can offer. One such pattern is wavefront. This paper extensively studies a wavefront-based miniapplication for Denovo, a production code for nuclear reactor modeling. We parallelize the Koch-Baker-Alcouffe (KBA) parallel-wavefront sweep algorithm in the main kernel of Minisweep (the miniapplication) using CUDA, OpenMP and OpenACC. Our OpenACC implementation running on NVIDIA's next-generation Volta GPU boasts an 85.06x speedup over serial code, which is larger than CUDA's 83.72x speedup over the same serial implementation. Our experimental platform includes SummitDev, an ORNL representative architecture of the upcoming Summit supercomputer. Our parallelization effort across platforms also motivated us to define an abstract parallelism model that is architecture independent, with a goal of creating software abstractions that can be used by applications employing the wavefront sweep motif.

Searles, Robert↗
DOE OSTI2023

Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

Loring, Burlen↗
DOE OSTI2025

QECC-Synth: A Layout Synthesizer for Quantum Error Correction Codes on Sparse Architectures

Quantum Error Correction (QEC) codes are essential for achieving fault-tolerant quantum computing (FTQC). However, their implementation faces significant challenges due to disparity between required dense qubit connectivity and sparse hardware architectures. Current approaches often either underutilize QEC circuit features or focus on manual designs tailored to specific codes and architectures, limiting their capability and generality. In response, we introduce QECC-Synth, an automated compiler for QEC code implementation that addresses these challenges. We leverage the ancilla bridge technique tailored to the requirements of QEC circuits and introduces a systematic classification of its design space flexibilities. We then formalize this problem using the MaxSAT framework to optimize these flexibilities. Evaluation shows that our method significantly outperforms existing methods while demonstrating broader applicability across diverse QEC codes and hardware architectures.

Yin, Keyi [University of California, San Diego]↗
DOE OSTI2025

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

In this work, we performed an initial design space exploration of an accelerated processing unit (APU)—a hybrid CPU+GPU architecture that integrates both compute units (CUs) and memory into a unified system. This integration aims to reduce data movement, enhance memory locality, and improve energy efficiency by enabling the CPU and GPU to share memory directly. This effort focused on the interplay of key design components—cache line size, the number of CUs, and main memory technology—and the trade-offs of each configuration were analyzed. This paper highlights the various configurations’ impact on memory accesses, data reuse, and power utilization. The results provide valuable insights that can be leveraged to optimize APU architectures for high-performance and energy-efficient computing and thus create a balanced architecture. This optimization can be achieved by adopting dynamic cache management, runtime CU scaling, and advanced memory integration, highlighting the potential of APUs to address critical challenges in compute, data movement, and memory power consumption.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗
DOE OSTI2017

Novel Short-Circuit Detection in Li-ion Battery Architectures

Industry and the battery research community don’t have accurate and affordable methods to predict catastrophic battery failures. Recently we published a new method of early detection of nascent internal shorts that are precursors to catastrophic failure. This present work was performed to determine the methods short detection capability within battery architectures consisting of two parallel strings with up to four cells in the string. It was found that detection sensitivity is a function of the number of healthy versus compromised cells in the architecture and placement of the measuring device. The method allows one to locate shorted cells without disconnecting cells or loads from the battery architecture.

25 ENERGY STORAGE↗
DOE OSTI2024

EWTN: Quantifying Mass Transport to Enable Water Electrolyzer Architectures with Low Flow-Rate Sensitivity

To develop cost-effective and high-performing polymer electrolyte water electrolyzers (PEWEs) for gigawatt-scale applications, researchers have focused on reducing precious metal catalyst loadings and optimizing porous transport layers. However, the performance of PEWEs is also affected by system architecture. Mass transport losses are dependent on localized architecture and material interactions. In-situ measurements, such as current density distribution maps have demonstrated advantages in understanding the intricate characteristics and influence of two-phase flow within PEWEs. This study proposes the parameter of effective water transport number (EWTN) as a quantitative tool to investigate such current density distribution (CDD) measurements for PEWEs. Results show that higher flow-rates have EWTN values of 0.95 and above, indicating no mass transport limitations; while lower flow-rates with large gradients CDD have EWTN values between 0.6-0.8, indicating mass transport limited conditions. The new analysis also identified a correlation between mass transport losses due to bubble accumulation, membrane hydration, and ohmic overpotentials. To address these limitations, an unitized pin-type LGDL/flow-field design was developed, which effectively prevents local gas phase accumulation, resulting in improved mass transport characteristics. The results of this work show reduced flow-rate sensitivity with the pin-type architecture and ∼13% increased performance at 0.24 ml / min / cm 2 .

Electrochemistry↗
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An intra-node HPC network architecture with nanosecond-scale photonic switches

We propose a single-stage network architecture for intra-node connectivity that makes use of nanosecond-scale photonic switches. Although buffering at the switch points is of vital importance for complex multi-stage networks, this is not the case for smaller-scale single-stage networks where the end nodes are located only one hop apart. By limiting the buffering to the end points, the proposed architecture manages to minimize the required electro-optic and opto-electronic conversions, leading in this way to both low end-to-end latency and better energy efficiency. Combining these advantages with nanosecond-scale switching times can allow for high-throughput operation even for frequent switch reconfigurations. The performance of the proposed architecture is evaluated via discrete-event simulations for a wide range of synthetic-traffic cases. The simulation results show that high-throughput operation of ≥90% can be achieved even for small message sizes, i.e., 32 KB for all-to-all communication and 2 KB for uniform random traffic, at a data rate of 400 Gb/s and a switch reconfiguration time of ≤72 ns. Moreover, if 100-ns reconfiguration times are achievable as opposed to 150-ns, then for the all-to-all traffic case a 16% and 28% reduction in completion time can be achieved for message sizes of 8 KB and 1 KB, respectively. In a forthcoming era of optically interfaced processors and accelerators, nanosecond-scale photonic switches appear as a highly promising solution for keeping up with the intra-node bandwidth scaling due to their high-bandwidth, low-latency and fast-switching capabilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Multicellular Architecture of Malignant Breast Epithelia Influences Mechanics

Cell-matrix and cell-cell mechanosensing are important in many cellular processes, particularly for epithelial cells. A crucial question, which remains unexplored, is how the mechanical microenvironment is altered as a result of changes to multicellular tissue structure during cancer progression. In this study, we investigated the influence of the multicellular tissue architecture on mechanical properties of the epithelial component of the mammary acinus. Using creep compression tests on multicellular breast epithelial structures, we found that pre-malignant acini with no lumen (MCF10AT) were significantly stiffer than normal hollow acini (MCF10A) by 60%. This difference depended on structural changes in the premalignant acini, as neither single cells nor normal multicellular acini tested before lumen formation exhibited these differences. To understand these differences, we simulated the deformation of the acini with different multicellular architectures and calculated their mechanical properties; our results suggest that lumen filling alone can explain the experimentally observed stiffness increase. We also simulated a single contracting cell in different multicellular architectures and found that lumen filling led to a 20% increase in the "perceived stiffness" of a single contracting cell independent of any changes to matrix mechanics. Our results suggest that lumen filling in carcinogenesis alters the mechanical microenvironment in multicellular epithelial structures, a phenotype that may cause downstream disruptions to mechanosensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A mechanical reduced order model for elastomeric 3D printed architectures

Direct ink writing of silicone elastomers enables printing with precise control of porosity and mechanical properties of ordered cellular solids, suitable for shock absorption and stress mitigation applications. With the ability to manipulate structure and feedstock stiffness, the design space becomes challenging to parse to obtain a solution producing a desired mechanical response. We derive an analytical design approach for a specific architecture. Results from finite element simulations and quasi-static mechanical tests of two different parallel strand architectures were analyzed to understand the structure-property relationships under uniaxial compression. Combining effective stiffness-density scaling with least squares optimization of the stress responses yielded general response curves parameterized by resin modulus and strand spacing. An analytical expression of these curves serves as a reduced order model, which, when optimized, provides a rapid design capability for filament-based 3D printed structures. Finally, as a demonstration, the optimal design of a face-centered tetragonal architecture is computed that satisfies prescribed minimum and maximum load constraints.

36 MATERIALS SCIENCE↗
DOE OSTI2014

A Compute Capable SSD Architecture for Next-Generation Non-volatile Memories

Existing storage technologies (e.g., disks and ash) are failing to cope with the processor and main memory speed and are limiting the overall perfor- mance of many large scale I/O or data-intensive applications. Emerging fast byte-addressable non-volatile memory (NVM) technologies, such as phase-change memory (PCM), spin-transfer torque memory (STTM) and memristor are very promising and are approaching DRAM-like performance with lower power con- sumption and higher density as process technology scales. These new memories are narrowing down the performance gap between the storage and the main mem- ory and are putting forward challenging problems on existing SSD architecture, I/O interface (e.g, SATA, PCIe) and software. This dissertation addresses those challenges and presents a novel SSD architecture called XSSD. XSSD o oads com- putation in storage to exploit fast NVMs and reduce the redundant data tra c across the I/O bus. XSSD o ers a exible RPC-based programming framework that developers can use for application development on SSD without dealing with the complication of the underlying architecture and communication management. We have built a prototype of XSSD on the BEE3 FPGA prototyping system. We implement various data-intensive applications and achieve speedup and energy ef- ciency of 1.5-8.9 and 1.7-10.27 respectively. This dissertation also compares XSSD with previous work on intelligent storage and intelligent memory. The existing ecosystem and these new enabling technologies make this system more viable than earlier ones.

97 MATHEMATICS AND COMPUTING↗