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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Maximizing ion dynamics and electrochemical performance of ionic liquid-acetonitrile electrolyte in Ti 3 C 2 T x MXene

Modification of the structure and morphology of MXene electrodes and the formulation of the electrolytes used in their supercapacitor configurations are significant factors affecting the performance of electrochemical devices. In this study, we investigated the electrochemical performance and ion dynamics of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [EmimTFSI], ionic liquid in the presence of acetonitrile (ACN) at different concentrations in Ti 3 C 2 T x MXene supercapacitor. We found an optimum concentration of ACN, at which more cations from the ionic liquid attach to the MXene electrode surface, providing higher electrochemical performance. This higher capacitance is also associated with increased microscopic dynamics of the cation away from the pore wall. These findings give a guideline to optimize the performance of MXene-based supercapacitors using organic solvents-ionic liquid-based electrolyte systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing electrocatalytic performance of RuO 2 -based catalysts: mechanistic insights, strategic approaches, and recent advances

Abstract Electrochemical water splitting presents the ultimate potential of hydrogen and oxygen production; however, regulating the rate and efficiency of water splitting is highly dependent on the accessibility of extremely efficient electrode materials for slow performance kinetics and large overpotential of both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Ruthenium oxide (RuO 2 ) based materials display high performance for OER and HER because of their capacity to bind oxygen, eminent catalytic activity, low cost compared to other precious metals, and stability in a wide pH range. However, there is still much space to promote the OER and HER activity and stability of RuO 2 to fulfill the necessity for practical applications in water splitting. Different researchers applied multiple approaches that boosted the catalytic performance of RuO 2 -based electrocatalysts toward overall water splitting. Herein, this review provides a comprehensive overview of recent advancements in RuO 2 -based materials in the field of water electrolysis for the generation of alternative energies. It gives a general description of water splitting in acidic and alkaline settings, including reaction mechanisms as well as common evaluation elements for the catalytic function of the materials. Most of the reviews reported based on RuO 2 materials are only focused on OER performance, but this review highlighted comprehensive ideas on different strategies like morphology design, electronic structure, electrolytes, and compositions for optimizing both electrocatalytic HER and OER functioning of RuO 2 -based electrocatalysts.

KC, Binod Raj (ORCID:0009000885806906)↗
DOE OSTI2021

MeV-scale performance of water-based and pure liquid scintillator detectors

This paper presents studies of the performance of water-based liquid scintillators (WbLS) in both 1-kt and 50-kt detectors. In this work, performance is evaluated in comparison to both pure water Cherenkov detectors and a nominal model for pure scintillator detectors. Performance metrics include energy, vertex, and angular resolution, along with a metric for the ability to separate the Cherenkov from the scintillation signal as being representative of various particle identification capabilities that depend on the Cherenkov/scintillation ratio. We also modify the time profile of scintillation light to study the same performance metrics as a function of rise and decay time. We go on to interpret these results in terms of their impact on certain physics goals, such as solar neutrinos and the search for Majorana neutrinos. This work supports and validates previous results, and the assumptions made therein, and serves as a significant stepping stone to complete detector design studies by using a more detailed detector model and full reconstruction, with a primarily data-driven optical model, and fewer model assumptions. With this model, a high-coverage 50-kt detector achieves better than 10 (1)% precision on the flux of neutrinos from the Carbon-Nitrogen-Oxygen cycle with a water-based liquid scintillator (pure LS) target in five years of data taking. A 1-kt LS detector, with a conservative 50% fiducial volume of 0.5 kt, can achieve better than 5% detection. A liquid scintillator detector has sensitivity into the normal hierarchy region for Majorana neutrinos with half-life sensitivity of $T^{0νββ}_{1/2}$ > 1.4 × 10 28 years at 90% C.L. for 10 years of data taking with a Te-loaded target.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A differentiable simulation package for performing inference of synchrotron-radiation-based diagnostics

The direction of particle accelerator development is ever-increasing beam quality, currents and repetition rates. This poses a challenge to traditional diagnostics that directly intercept the beam due to the mutual destruction of both the beam and the diagnostic. An alternative approach is to infer beam parameters non-invasively from the synchrotron radiation emitted in bending magnets. However, inferring the beam distribution from a measured radiation pattern is a complex and computationally expensive task. To address this challenge we present SYRIPY ( SYnchrotron Radiation In PYthon ), a software package intended as a tool for performing inference of synchrotron-radiation-based diagnostics. SYRIPY has been developed using PyTorch , which makes it both differentiable and able to leverage the high performance of GPUs, two vital characteristics for performing statistical inference. The package consists of three modules: a particle tracker, Lienard–Wiechert solver and Fourier optics propagator, allowing start-to-end simulation of synchrotron radiation detection to be carried out. SYRIPY has been benchmarked against SRW , the prevalent numerical package in the field, showing good agreement and up to a 50× speed improvement. Finally, we have demonstrated how SYRIPY can be used to perform Bayesian inference of beam parameters using stochastic variational inference.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Our goal is to have a better understanding of performance portability of SYCL kernels on a GPU. Toward this goal, we migrate representative kernels in bioinformatics applications from CUDA to SYCL, evaluate their performance on an NVIDIA GPU, and explain the performance gaps through performance profiling and analyses. We hope that the findings provide valuable feedback to the development of the SYCL ecosystem.

Jin, Zheming↗
DOE OSTI2021

Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments

The study presented here focuses on performance characteristics and trade-offs associated with running machine-learning tasks in multi-GPU environments on both on-site cloud computing resources and commercial cloud services (Azure). Specifically, this study examines these tradeoffs by examining the performance of training and fine-tuning of transformer-based deep-learning (DL) networks on clinical notes and data, a task of critical importance in the medical domain. To this end, we perform DL-related experiments on the widely deployed NVIDIA V100 GPUs and on the newer A100 GPUs connected via NVLink or PCIe. This study analyzes the execution time of major operations to train DL models and investigate popular options to optimize each of them. We examine and present the findings on the impacts that various operations (e.g. data loading into GPUs, training, fine-tuning), optimizations, and system configurations (single vs. multi-GPU, NVLink vs. PCIe) have on the overall training performance.

Begoli, Edmon↗
DOE OSTI2025

SYCL for Performance Portability: Application Experience with Coupled Cluster Formalism in Quantum Chemistry on Exascale Systems

The exascale computing has brought unprecedented heterogeneity in node architectures, with systems such as Frontier and Aurora featuring diverse GPU accelerators, network connectivity among others. Ensuring performance portability across these platforms is a key challenge. To address this, we employ the SYCL programming model to develop portable, high-performance quantum chemistry workloads. As a representative application, we focus on the non-iterative Triples component of the coupled-cluster CCSD(T) method, a key driver in quantum chemistry. In this work, we report on our experience deploying SYCL-based implementations using both DPC++ and AdaptiveCPP across two flagship exascale platforms: OLCF Frontier with AMD MI250X GPUs and ALCF Aurora with Intel GPUs. Our results demonstrate that SYCL enables efficient, single-source implementations that scale to thousands of nodes, delivering performance on par with vendor-optimized HIP solutions. We highlight key insights into runtime behavior, kernel portability, and scaling characteristics, showing that SYCL offers a viable path for performance-portable computing.

Bagusetty, Abhishek [Argonne National Laboratory (↗
DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗
DOE OSTI2020

Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular. In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. Additionally, we propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. Furthermore, we develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL is up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL's Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Understanding Performance Portability of SYCL Kernels: A Case Study with the All-Pairs Distance Calculation in Bioinformatics on GPUs

SYCL is a portable programming model. Toward the goal of a better understanding of performance portability of SYCL kernels on GPUs, we select a bioinformatics kernel for computing the all-pairs distance as a case study. After migrating the kernel from CUDA to HIP and SYCL, we evaluate the performance of the CUDA, HIP, and SYCL kernels on NVIDIA V100 and AMD MI210 GPUs. We analyze the GPU instructions from the kernels to explain performance gaps between SYCL and CUDA/HIP. We hope that the findings are valuable for improving performance portability of SYCL.

Jin, Zheming↗
DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗
DOE OSTI2023

Automatic Code Generation for High-Performance Graph Algorithms

Graph problems are common across fields of scientific computing and social sciences. However, despite their importance, implementing graph algorithms effectively on modern computing systems is a challenging task that requires significant programming effort and generally results in customized implementations. Current computing and memory hierarchies are not architected for irregular computations resulting in challenges for graph algorithms to achieve high performance on those architectures. In this paper, we present GraphX, a novel compiler framework and DSL designed to simplify the development of efficient graph algorithms and achieve high performance on modern computing systems. GraphX consists of a DSL for efficient implementation of graph algorithms, various optimizations, such as support for sparse linear algebra and workspace transformations, optimized graph primitives, including semiring and masking, and a high-performance code generation engine. Using GraphX, users can implement graph algorithms using a semantically-rich language with graph-oriented operators. GraphX uses these semantics to automatically generate efficient code for target architectures, increasing performance and portability across architectures. The composable nature of GraphX makes it possible to extend the set of optimizations and architectures without modifying the source code. We demonstrate GraphX outperforms state-of-the-art graph libraries, such as LAGraph, up to $3.7 speedup in semiring operations, $2.19 speedup in an important sparse computational kernel, and $9.05 speedup in graph processing algorithms.

compiler, graph algorithms, semiring, masking, wor↗
DOE OSTI2021

Spectral Effects in Albedo and Rearside Irradiance Measurement for Bifacial Performance Estimation

We investigate the impact of spectral dependence of ground surface reflectivity on albedo and rearside irradiance measurements necessary for bifacial photovoltaic (PV) module performance estimation and monitoring. Because PV modules are spectrally selective, albedo and irradiance measurements performed with common irradiance sensors may require spectral mismatch corrections when used for performance prediction. We investigate via simulation the differences in spectrally responsive albedo measured with thermopile pyranometers and crystalline silicon PV reference cells in comparison to a typical crystalline-silicon bifacial PV module. Simulations are performed for nine different representative ground surface materials using simulated solar spectra together with spectral reflectivity data distributed with the SMARTS simulation software. For the materials considered, the results show that albedo spectral mismatch relative to the bifacial module is distributed over a range of ±9.2% for thermopile pyranometers versus only ±3.7% for a typical PV reference cell. We consider the impact of this spectrally-responsive albedo mismatch on bifacial PV module rearside irradiance measurements. Using synthesized rearside spectral irradiance distributions, we find that for the nine different ground surface materials the predicted rearside irradiance measurement deviates from the effective irradiance observed by the PV module by on the order of 16.5 W/m 2 for the pyranometer and 3.6 W/m 2 for the PV reference cell. We discuss the implications for bifacial albedo and irradiance measurement.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

A Performance-Portable MultiGPU Implementation of 3D Euler Equations using ProtoX and IRIS

Computational scientists often face challenges when developing and optimizing code for high-performance computing (HPC), especially when trying to leverage GPUs. Given the heterogeneity of the nodes that comprise many modern HPC facilities, considerable demand exists for performance portable solutions for the core computational kernels used in many scientific computing libraries. In this work, we demonstrate a fourth-order finite volume method–based implementation of the Euler equations, which are an integral part of computational fluid dynamics. Our performance-portable multiGPU implementation for Euler equations uses ProtoX to generate kernels and IRIS for portability. ProtoX is a domain-specific language that uses a structured-grid partial differential equation library called Proto as its front end and the SPIRAL code generation system as its back end to generate optimized kernels for different architectures. Optimized kernels generated by ProtoX are orchestrated through the IRIS intelligent runtime system to provide portability. Two levels of optimizations within the IRIS runtime— directed acyclic graph fusion and task fusion—are explored to efficiently utilize computing resources in a multiGPU environment. Performance improvement through these optimizations is showcased by comparing the base ProtoX-IRIS implementation on AMD GPUs (Frontier node) and on NVIDIA GPUs (NVIDIA DGX-1).

Mankad, Het↗
DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗
DOE OSTI2025

Characterizing and Modeling the Influence of Geometry on the Performance of Superconducting Nanowire Cryotrons

The scaling of superconducting nanowire detectors to larger arrays is often limited by room-temperature-readout cabling. Cryogenic integrated circuits constructed from nanowire cryotrons, or nanocryotrons, can address this limitation by performing signal processing on chip. In this study, we characterize key performance metrics of the nanocryotron to elucidate its potential as a logical element in cryogenic integrated circuits and develop an electro-thermal model to connect material parameters with device performance. We find that the performance of the nanocryotron depends on the device geometry, and trade-offs are associated with optimizing the gain, jitter, and energy dissipation. Here, we demonstrate that nanocryotrons fabricated on niobium nitride can achieve a grey zone less than 210 nA wide for a 5 ns long input pulse corresponding to a maximum achievable gain of 48 dB, an energy dissipation of less than 20 aJ per operation, and a jitter of less than 60 ps.

Superconductor↗
DOE OSTI2024

Formal Definitions and Performance Comparison of Consistency Models for Parallel File Systems

The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes a performance penalty that becomes more significant as the scale of parallel file systems increases and the access time to storage devices, such as node-local solid storage devices, decreases. While some efforts have been made to adopt relaxed storage consistency models, these models are often defined informally and ambiguously as by-products of a particular implementation. Here in this work, we establish a connection between memory consistency models and storage consistency models and revisit the key design choices of storage consistency models from a high-level perspective. Further, we propose a formal and unified framework for defining storage consistency models and a layered implementation that can be used to easily evaluate their relative performance for different I/O workloads. Finally, we conduct a comprehensive performance comparison of two relaxed consistency models on a range of commonly seen parallel I/O workloads, such as checkpoint/restart of scientific applications and random reads of deep learning applications. We demonstrate that for certain I/O scenarios, a weaker consistency model can significantly improve the I/O performance. For instance, in small random reads that are typically found in deep learning applications, session consistency achieved a 5x improvement in I/O bandwidth compared to commit consistency, even at small scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effect of the J-Shaped Wind Turbine Airfoil Opening Ratio and Thickness on the Performance of Symmetrical Airfoils

Abstract In this work, the effect of the inner opening ratio on the J-shaped airfoils aerodynamic performance was studied and documented for symmetrical airfoils. Three different airfoil thicknesses were investigated: small (NACA0008), medium (NACA0015), and large (NACA0024). For each airfoil thickness, effects of three inner opening ratios were analyzed: one-third, one-half, and two-thirds. The performance of each opening ratio was compared with the performance of the solid airfoil “zero opening ratio” for different angles of attack between 5 deg and 20 deg. All designs were simulated using the computational fluid dynamics (CFD) technology against experimental results for solid NACA4412 airfoil in the University of Wisconsin-Milwaukee (UWM) wind tunnel facility and other published experimental data. It was found that large eddy simulation yields accurate solutions with a smaller number of mesh cells compared to the k–ω turbulence model but with much longer computational time. The lift-to-drag ratio for all studied airfoils has a maximum value for solid airfoils compared to those equipped with openings. For airfoils equipped with 00.00% opening ratio “solid,” NACA0015 airfoil has the maximum lift-to-drag ratio. Furthermore, it was found that NACA0008 equipped with a 33.33% opening ratio has the best performance of all studied J-shaped airfoils.

Energy & Fuels↗