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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Synchronization for CXL Based Memory

Compute Express Link (CXL) is an important emerging standard for disaggregated memory. While this standard provisions coherency across numerous hosts and devices, implementing hardware support for type three devices is challenging. In this work, we look at the overhead of software synchronization and using software-based coherency. Moreover, we discuss the limits of software-based coherency in fully expressing modern synchronization techniques for a CXL-based disaggregate memory system. We demonstrate our approach using a CXL hardware prototype and running a version of the famous Peterson Lock (enhanced to run with more than two threads). We analyze its performance and share how more advanced synchronization techniques might interact with software-based coherence CXL hardware and program execution models.

High Performance Computing (HPC)↗
DOE OSTI2024

Studying CPU and memory utilization of applications on Fujitsu A64FX and Nvidia Grace Superchip

ARM-based manycore CPU architectures are well-positioned to provide the rising memory throughput requirements of modern data intensive scientific applications in High Performance Computing (HPC). The Fujitsu A64FX CPU platform is based on the ARM v8.2A architecture, and is the processor of the flagship Japanese supercomputer - "Fugaku", which was previously ranked as the #1 supercomputer in the world according to the Top500 list. The Nvidia Grace superchip features 144 Neoverse V2 cores based on the ARMv9 architecture with 4x128b SVE2, providing exceptional computational power. The chip supports up to 480GB of memory, making it ideal for AI, machine learning, and scientific computing workloads. In this paper, we conduct a thorough performance exploration of a variety of parallel bandwidth-sensitive benchmarks and applications compiled with the native Fujitsu compiler on a Fugaku A64FX compute node and ARM (LLVM) Compiler on an NVIDIA Grace superchip compute node, engaging all the computational cores per cluster using OpenMP multithreading (assuming the cores can drive the available bandwidth). Our ultimate goals are to study the resource utilization of scientific applications and benchmarks on A64FX and Grace superchip, considering graph application scenarios ( GAP Benchmark suite) and eleven appli- cation proxies from the Rodinia heterogeneous benchmark suite (considering domains such as Data Mining, Bioinformatics, Fluid Dynamics, Pattern Recognition, etc.). Through exhaustive performance monitoring, we quantify the resource utilization of diverse OpenMP-based HPC applications on both the Fujitsu A64FX and the Nvidia Grace Superchip platforms.

benchmarking, Performance Analysis, High performan↗
DOE OSTI2014

Materials and Physics Challenges for Spin Transfer Torque Magnetic Random Access Memories

Magnetic random access memories utilizing the spin transfer torque effect for writing information are a strong contender for non-volatile memories scalable to the 20 nm node, and perhaps beyond. I will here examine how these devices behave as the device size is scaled down from 70 nm size to 20 nm. As device sizes go below ~50 nm, the size becomes comparable to intrinsic magnetic length scales and the device behavior does not simply scale with size. This has implications for the device design and puts additional constraints on the materials in the device.

Heinonen, O.↗
DOE OSTI2020

Memory Analysis Tool

This tool traces all memory accesses to stack (static allocation) and heap (dynamic allocation) on a trace run in a particular hardware and then estimates execution time on given arbitrary hardware configurations for hardware design space exploration. It can also compute memory access statistics, such as reuse distance.

Sato, Kento↗
DOE OSTI2022

High-temperature superelasticity in NiTiPt and NiTiPd shape memory alloys

Alloying NiTi shape memory alloys with sufficient Pt and Pd at the expense of Ni has been shown to increase transformation temperatures well above those possible with the binary composition, enabling consideration of a new class of high-temperature mechanical sensors and actuators. To achieve shape memory behavior at temperatures greater than 200 °C, previous studies of NiTiPt and NiTiPd compositions have focused primarily on alloys with greater than 16 at.% Pt and 25 at.% Pd. While generally not the focus, none of these previous studies demonstrated superelastic behavior at high temperatures. In an effort to identify alloys that transform to austenite at approximately 200 °C and exhibit superelastic behavior with recoverable strains greater than 2%, a series of Ti-rich NiTiPt ternary alloys with 15.5–16.5 at.% Pt, and Ti-rich NiTiPd ternary alloys with 25–26 at.% Pd have been targeted. The polycrystalline alloys were processed via arc melting followed by homogenization heat treatment. Thermomechanical properties were measured using electro-discharge machined tension test coupons and a miniature load frame equipped with a clamshell furnace. Ultimately, superelasticity in uniaxial tension with clear plateau regions was demonstrated and characterized in these alloys at temperatures between 219 °C and 270 °C.

Massad, Jordan E.↗
DOE OSTI2022

Memory and CPU efficient coherent mode decomposition of partially coherent synchrotron radiation with subtraction of common quadratic phase terms

Application examples of a memory and CPU efficient coherent mode decomposition (CMD) method for wave-optics based simulation of the partially coherent undulator radiation propagation through a hard X-ray beamline in a 3rd generation synchrotron radiation source are presented. The high efficiency of the method is achieved thanks to the analytical treatment of the common quadratic phase terms that are developed in the phase of cross-spectral density (CSD) of partially coherent radiation at a distance from source. This treatment allows for a considerable, several orders of magnitude, reduction of the 4D CSD mesh density (and the memory occupied by the CSD) required for ensuring sufficient accuracies of wavefront propagation simulations with the modes produced by the CMD at a beamline entrance. This method, implemented in the “Synchrotron Radiation Workshop” open-source software, dramatically increases the feasibility of the CMD of 4D CSD for producing 2D coherent modes for a large variety of applications at storage rings and other types of radiation sources.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Ultra Low Density Shape Memory Polymer Foams With Tunable Physicochemical Properties for Treatment of intracranial Aneurysms

Shape memory polymers (SMPs) are a rapidly emerging class of smart materials that can be stored in a deformed temporary shape, and can actively return to their original shape upon application of an external stimulus such as heat, pH or light. This behavior is particularly advantageous for minimally invasive biomedical applications comprising embolic/regenerative scaffolds, as it enables a transcatheter delivery of the device to the target site. The focus of this work was to exploit this shape memory behavior of polyurethanes, and develop an efficient embolic SMP foam device for the treatment of intracranial aneurysms.In summary, this work reports a novel family of ultra low density polymer foams which can be delivered via a minimally invasive surgery to the aneurysm site, actuated in a controlled manner to efficiently embolize the aneurysm while promoting physiological fluid/blood flow through the reticulated/open porous structure, and eventually biodegrade leading to complete healing of the vasculature.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Improving Physics Based Electron Neutrino Appearance Identication with a Long Short-Term Memory Network

The NOvA experiment is a long baseline neutrino oscillation experiment with the objective to measure the oscillation probability of muon type neutrinos ($\nu_{\mu}$) into electron type neutrinos ($\nu_e$). NOvA measures the interactions of neutrinos from the NuMI beam in two functionally identical liquid scintillator detectors. The far detector detects the appearance of electron neutrinos, leading to measurement of the oscillation parameters under study. Using an off -axis beam with an 810 km baseline length, NOvA is sensitive to measuring the neutrino mass hierarchy, the CP violating parameter, and the octant of the mixing angle, $\theta_{23}$. The data presented in this thesis has been collected from October 2013 until May 2018. The first NOvA $\nu_e$ charged current identifier utilized an artificial neural network with the physical features of the highest energy reconstructed shower as inputs. The $\nu_ e$ charged current identifier in this thesis utilizes a Long Short-Term Memory network with the physical features of every reconstructed shower in a particular interaction. In addition to the Long Short-Term Memory network, there are two Boosted Decision Trees to assist in event level selection. In the analysis of the data, 54 $\nu_e$ candidate events were detected with an expected background of 15 events. The results of this analysis prefer the normal mass hierarchy with maximal mixing and $\frac{\delta_{CP}}{\pi}=1.92_{-1.19}^{0.08}$. Results from this analysis differ from the published NOvA analysis, due to the differences of electron identification techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Sparse Data Acquisition on Emerging Memory Architectures

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. Experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data is sparse.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Multi-Level Memory Algorithmics for Large, Sparse Problems

In this report, we abstract eleven papers published during the project and describe preliminary unpublished results that warrant follow-up work. The topic is multi-level memory algorithmics, or how to effectively use multiple layers of main memory. Modern compute nodes all have this feature in some form.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Understanding Data Structures by Extracting Memory Access Graphs

Understanding the data structures employed by a program is important for reverse engineering activities and can improve the results of automated software analysis techniques. In a compiled binary, access to data structure fields and array indices defined in the source program are replaced by raw pointer arithmetic. We present a representation for capturing the essential details of how a program accesses memory regions, which we call a Memory Access Graph (MAG), and a static analysis for automatically extracting this information from a program binary. The static analysis to extract the MAGs from the program is straightforward and does not require sophisticated integer or pointer analysis. The MAGs are readily understood by reverse engineers; they are generally able to perceive the data structure definition corresponding to a MAG. We briefly discuss automatic extraction of structure definitions outlining some of the difficulties in doing so.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Unified Memory: GPGPU-Sim/UVM Smart Integration

CPU/GPU heterogeneous compute platforms are an ubiquitous element in computing and a programming model specified for this heterogeneous computing model is important for both performance and programmability. A programming model that exposes the shared, unified, address space between the heterogeneous units is a necessary step in this direction as it removes the burden of explicit data movement from the programmer while maintaining performance. GPU vendors, such as AMD and NVIDIA, have released software-managed runtimes that can provide programmers the illusion of unified CPU and GPU memory by automatically migrating data in and out of the GPU memory. However, this runtime support is not included in GPGPU-Sim, a commonly used framework that models the features of a modern graphics processor that are relevant to non-graphics applications. UVM Smart was developed, which extended GPGPU-Sim 3.x to in- corporate the modeling of on-demand pageing and data migration through the runtime. This report discusses the integration of UVM Smart and GPGPU-Sim 4.0 and the modifications to improve simulation performance and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Controlling reversible phase transitions in rare-earth nickelates for novel memory devices

Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, machine learning (ML), precision synthesis, and multi-modal X-ray imaging experiments to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Harnessing the Heterogeneity of T Cell Differentiation Fate to Fine-Tune Generation of Effector and Memory T Cells

Recent studies show that naïve T cells bearing identical T cell receptors experience heterogeneous differentiation and clonal expansion processes. The factors controlling this outcome are not well characterized, and their contributions to immune cell dynamics are similarly poorly understood. In this study, we develop a computational model to elaborate mechanisms occurring within and between two important physiological compartments, lymph nodes and blood, to determine how immune cell dynamics are controlled. Our multiorgan (multi-compartment) model integrates cellular and tissue level events and allows us to examine the heterogeneous differentiation of individual precursor cognate naïveT cells to generate both effector and memory T lymphocytes. Using this model, we simulate a hypothetical immune response and reproduce both primary and recall responses to infection. Increased numbers of antigen-bearing dendritic cells (DCs) are predicted to raise production of both effector and memoryT cells, and distinct “sweet spots” of peptide-MHC levels on those DCs exist that favor CD4+ or CD8+ T cell differentiation toward either effector or memory cell phenotypes. This has important implications for vaccine development and immunotherapy.

cognate↗
DOE OSTI2016

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multifunctional Fiber-Reinforced Polymer Composites for Damage Detection and Memory

Self-structural health monitoring (SHM) functionalities for fiber-reinforced polymer composites have become highly sought after to ensure the structural safety of newly advancing components in the automotive, civil, mechanical, and aerospace industries. This paper introduces a self-damage detection and memory (SDDM) hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive sensor network, and the structural glass fiber operates as electrical insulation. In this study, SDDM specimens were fabricated, and tensile and impact tests were performed. The tensile tests of SDDM specimens find two distinct loading peaks: first where the carbon fiber fails, and second where the glass fiber fails. A linear correlation was observed between the carbon fiber resistance and composite strain up to a threshold, beyond which a sharp nonlinear increase in resistance occurred. The resistance then approached infinity, coinciding with the first loading peak and failure of the carbon fiber elements. This demonstrates the potential for a damage early warning threshold. Additionally, the effect of stitching the sensor tow in a zig-zag pattern over a large area was investigated using tailored fiber placement (TFP) of 1-loop, 3-loop, and 5-loop specimens. Tensile testing found that increasing the number of loops improved the sensor’s accuracy for strain sensing. Furthermore, impact tests were conducted, and as the impact energy progressively increased, the sensor resistance permanently increased. This illustrates a capability for self-memory of microdamage throughout the life cycle of the structure, potentially useful for predicting the remaining life of the composite.

Demo, Luke B. (ORCID:0000000208806522)↗
DOE OSTI2022

HAMR - Heterogeneous Accelerator Memory Resource (HAMR) v1.0

HAMR is a library defining an accelerator technology agnostic memory model that bridges between accelerator technologies (CUDA, HIP, ROCm, OpenMP, Sycl, OpenCL, Kokos, etc) and traditional CPUs in heterogeneous computing environments. HAMR is light weight and implemented in modern C++. HAMR can be used to manage memory with in a single code or as a data model for coupling codes in a technologically agnostic way. HAMR provides a Python module for coupling C++ and Python codes which implements zero-copy data transfers to and from Python using the Numpy array interface and Numba CUDA array interface protocols.

Loring, Burlen↗
NASA NTRS1965

Improved wire memory matrix uses very little power

Thin-film, plated-wire memory matrix for computer applications requires little power yet has higher speed and four times greater storage capacity than ferrite-core memories of the same size.

Fedde, G. A.↗