Search NASA⌕ Search

SEARCH · Search NASA

Results for “Memory Optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 721 records · Page 40

Towards a NEAMS-based high-fidelity model of the MARVEL reactor

This report outlines the progress of Idaho National Laboratory in developing a high-fidelity and high-resolution model of the Microreactor Applications Research Validation and Evaluation reactor. The model was developed under the Nuclear Energy Advanced Modeling and Simulation microreactor application driver at Idaho National Laboratory. The overarching objective of this activity is the development of a high-fidelity multiphysics MARVEL model using NEAMS tools, and to verify and validate NEAMS tools against MARVEL reference simulation and experimental data, respectively. This is a unique opportunity to conduct multiphysics analysis on a soon-to-be-deployed microreactor. This multiphysics model developed under the NEAMS-funded INL microreactor application driver leverages three single-physics models coupled via the MOOSE’s MultiApp and Transfer systems. The latter systems enable in-memory data transfer between MOOSE-based and MOOSE-wrapped applications. The first single-physics model, that functions as main application, leverages Griffin to model the neutron transport in the core through the discontinuous finite element (DFEM) discrete ordinates solver (SN). Several optimization flags that were developed by the Griffin developer team were beta-tested to enhance the solver’s performance. These include the combined use of using_average_xs and update_averaged_xs_on that enable to avoid expensive on-the-fly cross sections evaluations at each linear iterations in favor of evaluations of the macroscopic cross sections at each Picard iteration. The second single-physics model uses BISON to handle solid heat transfer and asymptotic hydrogen redistribution analysis in the fuel. While the model returns consistent results for the temperature and hydrogen distribution in the fuel, a mismatch was noticed in the calculated temperature in the reflector due to the value of the gap conductance used in our model. Ongoing investigations are being performed to assess the origin of this discrepancy. Finally, the System Analysis Module (SAM) was used to model the flow of the sodium-potassium eutectic in the primary loop. A first verification was also performed showing good agreement in terms of mass flow rate and inlet temperature. All mesh files were generated using the MOOSE Reactor module, removing the need for external meshing tools. Notably, this workscope represents one of the initial applications of the MOOSE Reactor module for modeling highly irregular geometries. The use of the reactor module significantly streamlined the mesh generation process. The full multiphysics mode, that combines all the single physics models, was leveraged to conduct initial steady-state multiphysics simulations to compute power, and temperature distribution in the reactor. Initial testing was performed for transient simulations as well. In this case, the new checkpoint restart capability for eigenvalue calculations was tested showing the capability for streamlined restart of transient calculations. Future work will focus on improving the fidelity of the model by performing comprehensive code-to-code comparisons. For instance, the full-core Griffin neutronics model will be benchmarked against MCNP reference results, that were provided by the MARVEL design team. Additionally, the SAM T/H model will be verified against reference RELAP-5 results for selected accident scenarios. Besides code-to-code verification exercises, the model fidelity will be improved by replacing the single-channel SAM model with a more complex SAM-Pronghorn coupled model, in which the sub-channel capability is deployed to obtain radial temperature resolution in the coolant. This model will be developed in synergy with the NEAMS thermal hydraulics team.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

MILC staggered conjugate gradient performance on Intel KNL

We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second gener- ation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing centers for scientific research. The CG solver consumes the majority of time in production running, so we have spent most of our effort on it. We compare performance of an MPI+OpenMP baseline version of the MILC code with a version incorporating the QPhiX staggered CG solver, for both one-node and multi-node runs.

97 MATHEMATICS AND COMPUTING↗

HiPACE++: A portable, 3D quasi-static particle-in-cell code

Modeling plasma accelerators is a computationally challenging task and the quasi-static particle-in-cell algorithm is a method of choice in a wide range of situations. In this work, we present the first performance-portable, quasi-static, three-dimensional particle-in-cell code HiPACE++. By decomposing all the computation of a 3D domain in successive 2D transverse operations and choosing appropriate memory management, HiPACE++ demonstrates orders-of-magnitude speedups on modern scientific GPUs over CPU-only implementations. The 2D transverse operations are performed on a single GPU, avoiding time-consuming communications. The longitudinal parallelization is done through temporal domain decomposition, enabling near-optimal strong scaling from 1 to 512 GPUs. HiPACE++ is a modular, open-source code enabling efficient modeling of plasma accelerators from laptops to state-of-the-art supercomputers.

97 MATHEMATICS AND COMPUTING↗

Accelerating error correction in tomographic reconstruction

Abstract Spurred by recent advances in detector technology and X-ray optics, upgrades to scanning-probe-based tomographic imaging have led to an exponential growth in the amount and complexity of experimental data and have created a clear opportunity for tomographic imaging to approach single-atom sensitivity. The improved spatial resolution, however, is highly susceptible to systematic and random experimental errors, such as center of rotation drifts, which may lead to imaging artifacts and prevent reliable data extraction. Here, we present a model-based approach that simultaneously optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. Our algorithm utilizes an adaptive regularizer that is dynamically adjusted at each alternating iteration step. Furthermore, we describe its implementation in a software package targeting high-throughput workflows for execution on distributed-memory clusters. We demonstrate the performance of our solver on large-scale synthetic problems and show that it is robust to a wide range of noise and experimental drifts with near-ideal throughput.

Ali, Sajid (ORCID:0000000321864636)↗

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

Abstract Diverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

Krajewski, Adam M. (ORCID:0000000222660099)↗

Reversible migration of silver on memorized pathways in Ag-Ge{sub 40}S{sub 60} films

Reversible and reproducible formation and dissolution of silver conductive filaments are studied in Ag-photodoped thin-film Ge{sub 40}S{sub 60} subjected to electric fields. A tip-planar geometry is employed, where a conductive-atomic-force microscopy tip is the tip electrode and a silver patch is the planar electrode. We highlight an inherent “memory” effect in the amorphous chalcogenide solid-state electrolyte, in which particular silver-ion migration pathways are preserved “memorized” during writing and erasing cycles. The “memorized” pathways reflect structural changes in the photodoped chalcogenide film. Structural changes due to silver photodoping, and electrically-induced structural changes arising from silver migration, are elucidated using Raman spectroscopy. Conductive filament formation, dissolution, and electron (reduction) efficiency in a lateral device geometry are related to operation of the nano-ionic Programmable Metallization Cell memory and to newly emerging chalcogenide-based lateral geometry MEMS technologies. The methods in this work can also be used for qualitative multi-parameter sampling of metal/amorphous-chalcogenide combinations, characterizing the growth/dissolution rates, retention and endurance of fractal conductive filaments, with the aim of optimizing devices.

36 MATERIALS SCIENCE↗

Real-Time and Adaptive Reservoir Computing With Application to Profile Prediction in Fusion Plasma

Nuclear fusion is a promising alternative to address the problem of sustainable energy production. The tokamak is an approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. Here we study the characteristics and optimization of reservoir computing (RC) for real-time and adaptive prediction of plasma profiles in the DIII-D tokamak. Our experiments demonstrate that RC achieves comparable results to state-of-the-art (deep) convolutional neural networks (CNNs) and long short-term memory (LSTM) models, with a significantly easier and faster training procedure. This efficient approach allows for fast and frequent adaptation of the model to new situations, such as changing plasma conditions or different fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

54 ENVIRONMENTAL SCIENCES↗

Selective and Scalable Control of Spin Quantum Memories in a Photonic Circuit

A central goal in many quantum information processing applications is a network of quantum memories that can be entangled with each other while being individually controlled and measured with high fidelity. This goal has motivated the development of programmable photonic integrated circuits (PICs) with integrated spin quantum memories using diamond color center spin-photon interfaces. However, this approach introduces a challenge into the microwave control of individual spins within closely packed registers. Here, we present a quantum memory-integrated photonics platform capable of (i) the integration of multiple diamond color center spins into a cryogenically compatible, high-speed programmable PIC platform, (ii) selective manipulation of individual spin qubits addressed via tunable magnetic field gradients, and (iii) simultaneous control of qubits using numerically optimized microwave pulse shaping. The combination of localized optical control, enabled by the PIC platform, together with selective spin manipulation opens the path to scalable quantum networks on intrachip and interchip platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An anti-PD-1–GITR-L bispecific agonist induces GITR clustering-mediated T cell activation for cancer immunotherapy

Costimulatory receptors such as glucocorticoid-induced tumor necrosis factor receptor–related protein (GITR) play key roles in regulating the effector functions of T cells. In human clinical trials, however, GITR agonist antibodies have shown limited therapeutic effect, which may be due to suboptimal receptor clustering-mediated signaling. To overcome this potential limitation, a rational protein engineering approach is needed to optimize GITR agonist-based immunotherapies. Here we show a bispecific molecule consisting of an anti-PD-1 antibody fused with a multimeric GITR ligand (GITR-L) that induces PD-1-dependent and FcγR-independent GITR clustering, resulting in enhanced activation, proliferation and memory differentiation of primed antigen-specific GITR + PD-1 + T cells. The anti-PD-1–GITR-L bispecific is a PD-1-directed GITR-L construct that demonstrated dose-dependent, immunologically driven tumor growth inhibition in syngeneic, genetically engineered and xenograft humanized mouse tumor models, with a dose-dependent correlation between target saturation and Ki67 and TIGIT upregulation on memory T cells. Anti-PD-1–GITR-L thus represents a bispecific approach to directing GITR agonism for cancer immunotherapy.

60 APPLIED LIFE SCIENCES↗

Precisely computing phonons via irreducible derivatives

Computing phonons from first principles is typically considered a solved problem, yet inadequacies in existing techniques continue to yield deficient results in systems with sensitive phonons. Here, in this study, we circumvent this issue using the lone irreducible derivative (LID) and bundled irreducible derivative (BID) approaches to computing phonons via finite displacements, where the former optimizes precision via energy derivatives and the latter provides the most efficient algorithm using force derivatives. A condition number optimized basis for BID is derived which guarantees the minimum amplification of error. Additionally, a hybrid LID-BID approach is formulated, in which select irreducible derivatives computed using LID replace BID results. We illustrate our approach on two prototypical systems with sensitive phonons: the shape memory alloy AuZn and metallic lithium. Comparing our resulting phonons in the aforementioned crystals to calculations in the literature reveals nontrivial inaccuracies. Our approaches can be fully automated, making them well suited for both niche systems of interest and high-throughput approaches.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

SU-F-BRF-01: A GPU Framework for Developing Interactive High-Resolution Patient-Specific Biomechanical Models

Purpose: To develop a GPU-based framework that can generate highresolution and patient-specific biomechanical models from a given simulation CT and contoured structures, optimized to run at interactive speeds, for addressing adaptive radiotherapy objectives. Method: A Massspring-damping (MSD) model was generated from a given simulation CT. The model's mass elements were generated for every voxel of anatomy, and positioned in a deformation space in the GPU memory. MSD connections were established between neighboring mass elements in a dense distribution. Contoured internal structures allowed control over elastic material properties of different tissues. Once the model was initialized in GPU memory, skeletal anatomy was actuated using rigid-body transformations, while soft tissues were governed by elastic corrective forces and constraints, which included tensile forces, shear forces, and spring damping forces. The model was validated by applying a known load to a soft tissue block and comparing the observed deformation to ground truth calculations from established elastic mechanics. Results: Our analyses showed that both local and global load experiments yielded results with a correlation coefficient R{sup 2} > 0.98 compared to ground truth. Models were generated for several anatomical regions. Head and neck models accurately simulated posture changes by rotating the skeletal anatomy in three dimensions. Pelvic models were developed for realistic deformations for changes in bladder volume. Thoracic models demonstrated breast deformation due to gravity when changing treatment position from supine to prone. The GPU framework performed at greater than 30 iterations per second for over 1 million mass elements with up to 26 MSD connections each. Conclusions: Realistic simulations of site-specific, complex posture and physiological changes were simulated at interactive speeds using patient data. Incorporating such a model with live patient tracking would facilitate real time assessment of variations of the actual anatomy and delivered dose for adaptive intervention and re-planning.

60 APPLIED LIFE SCIENCES↗

Data Analysis Approach for Large Data Volumes in a Connected Community

Recent advancements within smart neighborhoods where utilities are enabling automatic control of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems are providing new opportunities to minimize energy costs through reduced peak load. This requires systematic collection, storage, management, and in-memory processing of large volumes of streaming data for fast performance. In this paper, we propose a multi-tier layered IoT software framework that enables effective descriptive and predictive data analysis for understanding live operation of the neighborhood, fault identification, and future opportunities for further optimization of load curves. We then demonstrate how we achieve live situational awareness of the connected neighborhood through a suite of visualization components. Finally, we discuss a few analytic dashboards that address questions such as peak load reductions obtained due to optimization, customer preference for automatic control of appliances (do they override the automatic control of HVAC?, etc.). 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Chinthavali, Supriya↗

Novel Platform for Digitization of Radiation Measurements, Decommissioning and Dismantling and Nuclear Waste Management - 18229

An innovative Platform for a seamless digitization of the information produced during the characterization of radioactive waste, contaminated objects, and in general radioprotection measurements has ben developed by CAEN SyS. The Platform is based on a spectroscopic handheld instrument integrating RFID technology, barcode reader, camera, voice recorder, and geo-localization, a selection of radiation tolerant RFID tags tested above 300 Gy integrated dose, ruggedized portable RFID readers and a secure database framework capable to collect data also from external sources such as gamma scanners and imaging systems. The Platform allows for real-time measurement of dose rate, radiation spectroscopy, tagging, tracking and inventorying of items. All data produced by the Platform are securely stored into the non-volatile memory of the RFID tags and in the database for both onsite and remote access to the information by operators. This paper presents the current development of the Platform and shows the advantages of such approach to achieve an easy yet reliable waste traceability, waste bagging optimization, and a structured methodology for radiation measurements, meeting an excellent quality control as increasingly required by Safety Authorities. Applications here envisaged and explored are dismantling and decontamination operations, radioprotection measurements and patrols, nuclear material management, safeguards and verification inspections. More in general, the Platform can address the needs of all plants, research laboratories and facilities that are looking to improve their Safety Standards, decrease operational costs, have a precise knowledge of the contaminated environment and objects as well as optimization for personnel dose minimization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗

Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.

attention↗

Tunable Interfacial to Filamentary Resistive Switching Mechanism in Room-Temperature-Grown Amorphous YBa 2 Cu 3 O x with Excess Cu Addition

Resistive switching technologies have the potential not only to create large efficiency gains in computer memory but also to revolutionize emerging fields such as neuromorphic computing. In this paper, we report on novel resistive switching behavior in devices made from room-temperature-grown Cu-rich amorphous YBa 2 Cu 3 O x (YBCO) films, a material otherwise well-known as a high-temperature superconductor. In Nb:STO substrate/amorphous YBCO film (≈200 nm)/metallic Cu (15 nm)/metallic Pt (15 nm) devices, we demonstrate that the resistive switching can be tuned between mechanisms involving extended areas of the YBCO/electrode interface and a single-point filamentary mechanism simply by changing the Cu content of the deposition target and hence in the films. Changing the Cu content can also be used to optimize the properties of the devices further, with devices with an added 15 mol % of Cu in YBCO initially providing an on/off ratio >100, switching endurance potential >6500 cycles, and state retention >2 × 10 4 s, all at low switching fields of 0.3 MV/cm. The amalgam of promising resistive switching properties, fast growth (150 nm/min) at room temperature, and tuneability of the switching mechanism indicates the strong potential of this proof-of-concept amorphous system for future memory applications.

Cu↗

Simulation at Extreme-Scale: Co-Design Thinking and Practices

The current trend of computer architecture evolving towards exaflop/s is the fast increasing floating point performance (the so-called “free” flops) accompanied by much slowly improving the bandwidth of memory and network. Numerical simulation would undergo the challenge posed by the unbalanced increase in the compute power and the capability of data movement. In this paper, after reviewing the challenges of hardware and software in moving towards exascale computing, we present co-design thinking for selecting, optimizing, and developing a numerical algorithm and a simulation tool to meet the challenge of simulation at extreme scale. Examples are presented to demonstrate the new way of thinking and its effectiveness on the emerging architecture.

Computer Science↗