Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PERFORM”

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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

Abstract The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field‐based measures of abundance, demographic performance, and forecasted stochastic population growth (λ s ). Predicted climatic suitability showed a J‐shaped relationship with A. utahensis abundance, where low‐abundance populations were associated with low‐to‐intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λ s from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM‐predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.

Baer, Kathryn C.↗
DOE OSTI2023

Spatial Effect on the Performance of Carboxylate Anode Materials in Na-Ion Batteries

Developing low-voltage carboxylate anode materials is critical for achieving low-cost, high-performance, and sustainable Na-ion batteries (NIBs). However, the structure design rationale and structure-performance correlation for organic carboxylates in NIBs remains elusive. Herein, the spatial effect on the performance of carboxylate anode materials is studied by introducing heteroatoms in the conjugation structure and manipulating the positions of carboxylate groups in the aromatic rings. Planar and twisted organic carboxylates are designed and synthesized to gain insight into the impact of geometric structures to the electrochemical performance of carboxylate anodes in NIBs. Among the carboxylates, disodium 2,2’-bipyridine-5,5’-dicarboxylate (2255-Na) with a planar structure outperforms the others in terms of highest specific capacity (210 mAh g -1 ), longest cycle life (2000 cycles), and best rate capability (up to 5 A g -1 ). Further, the cyclic stability and redox mechanism of 2255-Na in NIBs are exploited by various characterization techniques. Moreover, high-temperature (up to 100 °C) and all-organic batteries based on a 2255-Na anode, a polyaniline (PANI) cathode, and an ether-based electrolyte are achieved and exhibited exceptional electrochemical performance. Therefore, this work demonstrates that designing organic carboxylates with extended planar conjugation structures is an effective strategy to achieve high-performance and sustainable NIBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Performance evaluations of signed and unsigned noisy approximate quantum Fourier arithmetic

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route toward a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor’s algorithm, weighted-sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM’s superconducting-qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands’ superposed integer states. We analyze performance and identify the most effective approximation depths for unsigned quantum addition and quantum multiplication within the given context. We then perform a similar analysis of signed addition and compare to the unsigned results. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges—relevant to signed, unsigned, modular and non-modular versions—that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. Here, we analyze the performance trends in our results and speculate on possible future developments within this computational paradigm.

Computational models↗
DOE OSTI2022

Experimental and analytical study of the hydrodynamic and single and two-phase convective heat transfer performance of flexible PDMS microchannels with micropillar arrays

Various copper and silicon based thermal management systems are used in the cooling of electronics. However, the rigid nature of these materials along with their high thermal and electrical conductivity pose a difficulty in developing direct contact embedded flexible cooling systems that can offer robust cooling performance. The low density, thermal stability, chemical inertness, and electrical insulation of Polydimethylsiloxane (PDMS) make it an ideal material to develop lightweight direct contact thermal management systems for electronics. Its ease of fabrication with tunable flexibility provides the opportunity to go beyond traditional electronics and develop advanced active and passive thermal management systems for a wide–range of applications in foldable and wearable electronics, liquid cooling garments, microgravity, and electric motors. In this study, a flexible PDMS based microchannel with micropillar arrays, which enhance the thermal performance of the device through capillary-assisted flow, has been developed. The hydrodynamic and convection heat transfer performance of three PDMS wick pillar geometries, ranging from a porosity of 0.8–0.91, are investigated and compared under single-phase and two-phase conditions. Dielectric coolant FC-3283 is employed and permeability measurements are made for mass fluxes ranging from 53 kg/m 2 s to 369 kg/m 2 s. Given its conformability, the device demonstrates a deviation from Darcy’s Law, within the laminar regime, with an increasing permeability with mass flux at the rate of ~0.5–0.8 Darcy/(kg/m 2 s). A semi-analytical model has been developed and reported to quantify the conformability of the device. The heat transfer performance is experimentally evaluated using the same dielectric fluid for mass flux ranging from 105 kg/m 2 s to 420 kg/m 2 s with heat fluxes ranging from 1.5 W/cm 2 to 16 W/cm 2 . Heat transfer coefficients of up to 7000 W/m 2 K are observed, which are comparable to copper and silicon microchannels. The effect of porosity on the single phase thermal performance has been evaluated against the pumping power to provide a basis for thermal management system design. Finally, high-speed imaging is performed to study the two-phase flow characteristics to provide insight into the vapor formation and removal.

42 ENGINEERING↗
DOE OSTI2020

O 2 dosage as a descriptor of TWC performance under lean/rich dithering in stoichiometric natural gas engines

The effects of oscillations in gas composition, known as lean/rich dithering, on the performance of a commercial Pd-based three-way catalyst (TWC) for stoichiometric natural gas (NG) engines were evaluated using synthetic exhaust flow reactor experiments. Under simulated NG exhaust conditions, NO conversion was intimately correlated to CH 4 conversion at slightly fuel-rich operating conditions. CH 4 conversion significantly varied with lambda (or O 2 concentration) and depended on the direction of the lambda change. The dynamic CH 4 conversion is likely related to the change of catalyst oxidation state and structures of Pd active sites. The CH 4 -NO cross-over point was found at a rich-biased lambda rather than stoichiometry. Compared to static operation, catalyst performance was much higher under a realistic dithering condition. The impacts of dithering parameters including amplitude and frequency on CH 4 and NO conversions were explored. O 2 dosage is proposed as an effective descriptor of the TWC dithering performance. In the O 2 -dosage space, the performance of distinct dithering parameters collapse, helping to elucidate the fundamental influence of dithering parameters on TWC performance. With the help of O2 dosage, an optimal operating window leading to high CH 4 and NO conversions was identified. A higher dithering amplitude is required to keep the catalyst in a reduced state for higher CH 4 and NO conversions, while an appropriate O 2 dosage that does not exceed the breakthrough OSC is necessary to avoid excess O 2 that results in NO slip. The dithering amplitude and O 2 -dosage-metric maps provide a new method for studying dynamic TWC performance and identifying optimum operation strategies.

03 NATURAL GAS↗
DOE OSTI2025

Bridging interfacial properties and cell performance: A multiscale model for proton-exchange-membrane fuel cells

Here, to elucidate the impact of local interfaces on mass-transport resistance and overall cell performance of low-loaded proton-exchange-membrane fuel cells (PEMFCs), we present a multiscale modeling framework incorporating a novel modified agglomerate model. The model considers three distinct Pt-electrolyte interfaces: Pt on the carbon surface covered by either ionomer or water film and Pt inside carbon nanopores. Detailed mass-transport voltage-loss breakdowns reveal that coupled agglomerate-interface-scale mass transport dominates the mass-transport loss. The ionomer poisons the exterior-Pt surface through suppressing O 2 adsorption and intrinsic ORR activity, leading to low current-density performance. Conversely, interior-Pt interface enhances the kinetic performance but limits high current-density performance due to its low interfacial permeability. The exterior-Pt/water interface demonstrates superior kinetic performance and mass transport, though its practical implementation requires ensuring proton transport. By coupling the multiscale CL properties with ink parameters, the model identifies an optimal I to C ratio of approximately 0.5, a moderate value where the ionomer content is sufficient to guarantee proton transport without fully covering the Pt surface and forming large agglomeration, thus allowing the utilization of the Pt-water interface and avoiding high mass-transport loss. Overall, the model helps unravel limiting phenomena across different operating regimes and provides routes for optimizing performance.

Cell diagnostic↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2024

Steady-state fuel performance analyses for the preliminary fuel concept of general atomics fast modular reactor

Here this manuscript presents the fuel performance analysis results of the General Atomics Fast Modular Reactor (FMR) based on an axi-symmetric (2D-RZ) geometry. Three fuel performance model sets that fit the FMR fuel specifications best, i.e., a BISON baseline model set, a BISON diffusion enhancement model set, and a BISON-FASTGRASS model set, were identified and evaluated against a series of relevant experimental cases featuring high burnup and low irradiation temperature conditions. The three BISON-based model sets were then utilized to conduct a comprehensive fuel performance analysis of the FMR fuel under normal operation including the shutdown/restarting periods for refueling. The evaluation of the fuel performance parameters, represented by temperature, internal pressure, stress, and strain, shows that the FMR fuel maintains its thermal and mechanical integrity during normal operation. Technology gaps and limitations are also discussed to guide future efforts for extending the performance analysis to transient scenarios as well as improving the fuel performance evaluation through experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Evaluating the effect of membrane-ionomer combinations and supporting electrolytes on the performance of cobalt nanoparticle anodes in anion exchange membrane electrolyzers

In low temperature polymer electrolyte membrane electrolyzers, component choices and membrane electrode assembly (MEA) fabrication have pivotal roles in determining cell performance. Here, cobalt anodes were used in combination with different ionomers, membranes, and supporting electrolytes to delineate their effect on MEA performance in anion exchange membrane (AEM) based electrolyzers. Changes to the ionomer type suppress or enhance catalyst redox and were found to alter the resulting exchange current densities and Tafel slopes. Changes in membrane conductivity account for large differences in performance and overpotential losses among the different MEAs, with higher conductivity minimizing ohmic losses. Within supporting electrolytes, lower concentration results in lower conductivity and lower MEA performance; the performance, however, is higher in potassium hydroxide than bicarbonate. These experiments address the influence of individual components and their integration on cell-level performance, to help develop a pathway to an electrolyte free system while providing a foundation for studying electrode composition and component incorporation into MEAs.

25 ENERGY STORAGE↗
DOE OSTI2026

Power performance and loads characterization of laboratory-scale cross-flow rotors fabricated using additive manufacturing

Tidal energy conversion is a relatively new application for additive manufacturing (AM), where the focus has been on fabricating axial-flow turbine blades. AM techniques add material precisely where it is needed, creating more complex shapes with less waste. Cross-flow rotor geometry presents an opportunity for AM to improve rotor performance by fabricating features that cannot be created economically via conventional manufacturing. The challenges associated with using AM in cross-flow design include water resistance and degradation over time while retaining a level of quality equivalent to conventionally machined parts. In this work, AM materials were tested by environmentally conditioning samples in a seawater tank for 5 months, followed by performance and phase-resolved load testing of laboratory-scale rotors in a hydraulic flume. We found that while metals like titanium and Inconel have excellent performance in marine environments, achieving the desired geometry and performance is difficult. Thermoplastics degraded in seawater but were easier to form into desired geometries and could exceed the performance of an aluminum control rotor. Warping and surface finish were significant detractors from AM rotor performance. These results suggest that the primary benefits of using AM for cross-flow rotors is to quickly fabricate and test unconventional rotor geometries.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Evidence of Zintl Intermediate Phase and Its Impacts on Li and Na Storage Performance of Pb-Based Alloying Anodes

Anode materials based on conversion and alloying reactions are promising to achieve high energy density of advanced sodium-ion batteries (SIBs). While the chemical similarities between sodium and lithium as alkali elements make the benchmarking strategy practical in developing new high-performance anodes, simply borrowing the anode material from one system to the other does not always guarantee success unless it is based on sound understanding of both Li- and Na-reaction mechanisms. In this work, we report the Na storage performance of a Pb-based anode and its fundamental reaction dynamics. In contrast to its excellent electrochemical performances in Li cells (reversible ~600 mAh/g), the newly developed Pb@PbO–C nanocomposite anode has limited electrochemical Na reaction properties showing moderate capacity and rate performances (~300 mAh/g at 20 mA/g). Synchrotron-based X-ray diffraction and absorption spectroscopy studies reveal the fundamental differences in the Na and Li reaction mechanism of the Pb-based anode. Further, unlike Li reaction, the unique Na reaction mechanism involves the formation of a highly ionic NaPb Zintl phase, which comprises tetrahedral Pb 4 clusters, as an intermediate phase. The strong covalent character of the Pb 4 Zintl clusters adversely affects the electronic conductivity and thus limits the electrochemical performance of the Pb-based anode in Na cells. These findings provide new insights applicable to developing high-performance alloying anode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Enhanced Electrochemical Performance of Disordered Rocksalt Cathodes Enabled by a Graphite Conductive Additive

Cobalt-free cation-disordered rocksalt (DRX) cathodes are a promising class of materials for next-generation Li-ion batteries. Although they have high theoretical specific capacities (>300 mA h/g) and moderate operating voltages (~3.5 V vs Li/Li + ), DRX cathodes typically require a high carbon content (up to 30 wt %) to fully utilize the active material which has a detrimental impact on cell-level energy density. To assess pathways to reduce the electrode’s carbon content, the present study investigates how the carbon’s microstructure and loading (10–20 wt %) influence the performance of DRX cathodes with the nominal composition Li 1.2 Mn 0.5 Ti 0.3 O 1.9 F 0.1 . While electrodes prepared with conventional disordered carbon additives (C65 and ketjenblack) exhibit rapid capacity fade due to an unstable cathode/electrolyte interface, DRX cathodes containing 10 wt % graphite show superior cycling performance (e.g., reversible capacities ~260 mA h/g with 85% capacity retention after 50 cycles) and rate capability (~135 mA h/g at 1000 mA/g). Furthermore, a suite of characterization tools was employed to evaluate the performance differences among these composite electrodes. Overall, these results indicate that the superior performance of the graphite-based cathodes is largely attributed to the: (i) formation of a uniform graphitic coating on DRX particles which protects the surface from parasitic reactions at high states of charge and (ii) homogeneous dispersion of the active material and carbon throughout the composite cathode which provides a robust electronically conductive network that can withstand repeated charge–discharge cycles. Overall, this study provides key scientific insights on how the carbon microstructure and electrode processing influence the performance of DRX cathodes. Based on these results, exploration of alternative routes to apply graphitic coatings is recommended to further optimize the material performance.

25 ENERGY STORAGE↗
DOE OSTI2023

Effect of Interlayer Time-Lapse and Workability Retention on Printed Concrete Performance

The rapid advancement of additive manufacturing technologies for concrete construction requires further understanding of material performance and its relationship with print parameters such as interlayer time-lapse and workability retention during the print. Here this study addresses the effects of the time delay between consecutive extruded layers on the mechanical performance of printed concrete using a mix designed by Quikrete. A series of specimens were prepared with printing delays of 10, 15, 30, and 60 min. This work correlates the change in fracture properties, mechanical performance, and workability retention with the interlayer time-lapse between two consecutive concrete layers. Interlayer time-lapse is defined as the time difference in printing of two consequent layers of concrete. It affects individual properties differently based on the loading direction and test types, but it is consistent in terms of interlayer performance. The understanding of the effect of interlayer time-lapse on the fracture properties between two consecutive printed layers fills the knowledge gaps in printed concrete research. Therefore, this study can help researchers perform required optimization and safely estimate the performance of printed concrete.

3D printing↗
DOE OSTI2024

Development and performance evaluation of active insulation systems using solid-state thermal switches

Traditional building envelopes have passive insulation systems that cannot respond to dynamic changes in the environment. An Active Insulation System (AIS) consists of Active Insulation Materials (AIMs) that dynamically vary the thermal conductivity of the insulation system. Several researchers have evaluated the impact of AIS on building thermal and energy performance by using simulation tools. Up to 70% savings in annual heating and cooling energy and significant reductions in peak demand have been predicted for some climates with wall systems employing AIS. However, materials and assembly development still need a cost-effective product that achieves the required performance. Here, in this study, we present the process of developing an AIS that we will install in a test hut for its performance evaluation. Minimum performance criteria of the AIS system are developed based on R-low/R-high ratio, required time and efficiency to switch states, and cost estimates. The following steps during this study are creating the concept to meet the requirements, predicting the performance via simulations, developing the experimental setup for bench-scale testing, and finally, constructing a full-scale wall assembly and monitoring the performance when exposed to environmental chamber tests. The selected approach uses off-the-shelf products to create an AIS that can switch R-value between 0.98 ft 2 ·°F·h/BTU (0.173 m 2 ·K/W) and 5.81 ft 2 ·°F·h/BTU (1.02 m 2 ·K/W) and have a switching time of less than one minute between R-high and R-low.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Balancing performance of active magnetic regenerators: a comprehensive experimental study of aspect ratio, particle size, and operating conditions

Abstract Effective and, at the same time, efficient active magnetic regenerator (AMR) performance requires balanced geometry and operating conditions. Here the influence of regenerator shape, magnetocaloric material size, operating frequency, and utilization on the performance of gadolinium packed-particle bed AMRs is demonstrated experimentally. Various metrics are applied to assess effectiveness and efficiency. Observed temperature spans and cooling powers across a wide range of operating conditions are used to evaluate system performance and estimate exergetic cooling power and exergetic power quotient. A new metric combining exergetic cooling power and pump power provides an estimate of the maximum achievable second law efficiency. Five regenerator geometries with equal volumes and the aspect ratio from 1.0 to 3.8, and four different ranges of Gd spherical particles between 182 and 354 µ m, are investigated. Improvements in system performance are demonstrated by a boost in specific cooling power of gadolinium from 0.85 to 1.16 W g −1 and maximum temperature span from 8.9 to 15.1 K. The optimum exergetic cooling power is observed for 1.37 utilization and 3 Hz operating frequency, exergetic power quotient exhibits a maximum at the same utilization but at 2 Hz frequency, while the highest efficiency is recorded at 1 Hz and utilization of 0.5, demonstrating that multiple performance metrics must be balanced to achieve regenerator design meeting all performance targets.

42 ENGINEERING↗
DOE OSTI2025

Iso-cost-performance of thermal energy storage

As thermal energy storage (TES) systems gain increasing recognition as next-generation energy storage solutions, evaluating their techno-economic performance is crucial. This perspective analyzes the cost-performance of latent heat-based TES systems and introduces the concept of iso-cost-performance—maintaining a constant cost per unit energy ($\$$/kWh) despite material degradation. Using an empirical degradation model, we show that after 1200 thermal cycles, the thermal conductivity and volumetric energy density of a paraffin-based phase change material (PCM) decreased by 36.0% and 26.1%, respectively, resulting in a 31.2% reduction in the figure of merit (FOM) and, consequently, the system cost-performance. However, by introducing thermally conductive additives to enhance effective thermal conductivity, the TES system can recover its initial FOM, achieving iso-cost-performance operation. This framework quantitatively demonstrates how degradation-mitigation strategies—such as improving thermal conductivity—can offset material degradations and maintain long-term cost-effectiveness. Beyond PCM-based TES, the proposed FOM-based approach provides a generalized pathway for cost-performance optimization across various TES technologies.

25 ENERGY STORAGE↗
DOE OSTI2021

Battle of the Defaults: Extracting Performance Characteristics of HDF5 under Production Load

Popular parallel I/O libraries, such as HDF5, provide tuning parameters to obtain superior performance. However, the selection of effective parameters on production systems is complex due to the interdependence of I/O software and file system layers. Hence, application developers typically use the default parameters and often experience poor I/O performance. This work conducts a benchmarking-based analysis on the HDF5 behaviors with a wide variety of I/O patterns to extract performance characteristics under the production workload. To make the analysis well controlled, we exercise I/O benchmarks on POSIX-IO, MPI-IO, and HDF5 using the same I/O patterns and in the same jobs. To address high performance variability in production environments, we repeat the benchmarks across I/O patterns, storage devices, and time intervals. Based on the results, we identified consistent HDF5 behaviors that appropriate configurations and operations on dataset layout and file-metadata placement can improve performance significantly. We apply our findings and evaluate the tuned I/O library on two supercomputers: Summit and Cori. The results show that our tuned parameters can achieve more than 10× I/O performance speedup than that with default parameters on both systems, suggesting the effectiveness, stability, and generality of our solution.

Xie, Bing↗
DOE OSTI2024

A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Optimizing a particular subprogram out of the set of Basic (sparse) Linear Algebra Subprograms (BLAS) for a given architecture is a common topic of research. In applications, however, these BLAS functions rarely appear in isolation; usually, many of them are used together, in various combinations and with varying inputs. As the need to solve a large, sparse linear system is ubiquitous throughout HPC applications, linear solvers constitute a realistic, sufficiently complex and well-defined representative use case for composite BLAS routines. To this end, based on a representative set of matrices drawn from a diverse set of fields, we present a framework to study, from the performance and energy perspective, the efficacy of GPU- resident parallel Conjugate Gradient (CG) linear solver with different preconditioner options, including Gauss-Seidel, Jacobi, and incomplete Cholesky. We also propose a novel GPU-based preconditioner, in which the triangular solves are approximated by an iterative process. The development of this preconditioner was motivated by solving large graph Laplacian linear systems, for which the existing preconditioners either perform slow on GPU-based platforms or are not applicable. We compare the performance of these preconditioners on different hardware accelerator architectures, i.e., AMD MI250X, MI100, Nvidia A100, V100, and Jetson. Our experiments reveal performance trade-offs and provide information on how to select the best strategy for the given linear system, dictated by its properties, and the platform of interest. We demonstrate the application of our novel preconditioner for solving CG and graph Laplacian systems. Overall, the framework can be utilized as a benchmark to guide informed decisions in choosing a specific preconditioner, i.e., whether it is better to rely on the performance of a triangular solver or on the performance of sparse matrix-vector product. Finally, by considering power consumption to solve the linear systems, we report the energy footprint for the solvers.

Preconditioned Conjugate Gradient, GPUs, iterative↗