First principles based multiparadigm modeling of electronic structures and dynamics

Electronic structures and dynamics are the key to linking the material composition and structure to functionality and performance.

An essential issue in developing semiconductor devices for photovoltaics is to design materials with optimal band gaps and relative positioning of band levels. Approximate DFT methods have been justified to predict band gaps from KS/GKS eigenvalues, but the accuracy is decisively dependent on the choice of XC functionals. We show here for CuInSe₂ and CuGaSe₂, the parent compounds of the promising CIGS solar cells, conventional LDA and GGA obtain gaps of 0.0-0.01 and 0.02-0.24 eV (versus experimental values of 1.04 and 1.67 eV), while the historically first global hybrid functional, B3PW91, is surprisingly the best, with band gaps of 1.07 and 1.58 eV. Furthermore, we show that for 27 related binary and ternary semiconductors, B3PW91 predicts gaps with a MAD of only 0.09 eV, which is substantially better than all modern hybrid functionals, including B3LYP (MAD of 0.19 eV) and screened hybrid functional HSE06 (MAD of 0.18 eV).

The laboratory performance of CIGS solar cells (> 20% efficiency) makes them promising candidate photovoltaic devices. However, there remains little understanding of how defects at the CIGS/CdS interface affect the band offsets and interfacial energies, and hence the performance of manufactured devices. To determine these relationships, we use the B3PW91 hybrid functional of DFT with the AEP method that we validate to provide very accurate descriptions of both band gaps and band offsets. This confirms the weak dependence of band offsets on surface orientation observed experimentally. We predict that the CBO of perfect CuInSe₂/CdS interface is large, 0.79 eV, which would dramatically degrade performance. Moreover we show that band gap widening induced by Ga adjusts only the VBO, and we find that Cd impurities do not significantly affect the CBO. Thus we show that Cu vacancies at the interface play the key role in enabling the tunability of CBO. We predict that Na further improves the CBO through electrostatically elevating the valence levels to decrease the CBO, explaining the observed essential role of Na for high performance. Moreover we find that K leads to a dramatic decrease in the CBO to 0.05 eV, much better than Na. We suggest that the efficiency of CIGS devices might be improved substantially by tuning the ratio of Na to K, with the improved phase stability of Na balancing phase instability from K. All these defects reduce interfacial stability slightly, but not significantly.

A number of exotic structures have been formed through high pressure chemistry, but applications have been hindered by difficulties in recovering the high pressure phase to ambient conditions (i.e., one atmosphere and room temperature). Here we use dispersion-corrected DFT (PBE-ulg flavor) to predict that above 60 GPa the most stable form of N₂O (the laughing gas in its molecular form) is a 1D polymer with an all-nitrogen backbone analogous to cis-polyacetylene in which alternate N are bonded (ionic covalent) to O. The analogous trans-polymer is only 0.03-0.10 eV/molecular unit less stable. Upon relaxation to ambient conditions both polymers relax below 14 GPa to the same stable non-planar trans-polymer, accompanied by possible electronic structure transitions. The predicted phonon spectrum and dissociation kinetics validate the stability of this trans-poly-NNO at ambient conditions, which has potential applications as a new type of conducting polymer with all-nitrogen chains and as a high-energy oxidizer for rocket propulsion. This work illustrates in silico materials discovery particularly in the realm of extreme conditions.

Modeling non-adiabatic electron dynamics has been a long-standing challenge for computational chemistry and materials science, and the eFF method presents a cost-efficient alternative. However, due to the deficiency of FSG representation, eFF is limited to low-Z elements with electrons of predominant s-character. To overcome this, we introduce a formal set of ECP extensions that enable accurate description of p-block elements. The extensions consist of a model representing the core electrons with the nucleus as a single pseudo particle represented by FSG, interacting with valence electrons through ECPs. We demonstrate and validate the ECP extensions for complex bonding structures, geometries, and energetics of systems with p-block character (C, O, Al, Si) and apply them to study materials under extreme mechanical loading conditions.

Despite its success, the eFF framework has some limitations, originated from both the design of Pauli potentials and the FSG representation. To overcome these, we develop a new framework of two-level hierarchy that is a more rigorous and accurate successor to the eFF method. The fundamental level, GHA-QM, is based on a new set of Pauli potentials that renders exact QM level of accuracy for any FSG represented electron systems. To achieve this, we start with using exactly derived energy expressions for the same spin electron pair, and fitting a simple functional form, inspired by DFT, against open singlet electron pair curves (H₂ systems). Symmetric and asymmetric scaling factors are then introduced at this level to recover the QM total energies of multiple electron pair systems from the sum of local interactions. To complement the imperfect FSG representation, the AMPERE extension is implemented, and aims at embedding the interactions associated with both the cusp condition and explicit nodal structures. The whole GHA-QM+AMPERE framework is tested on H element, and the preliminary results are promising.

Physical, Metabolic, and Energetic Investigations of Methane-Metabolizing Microbial Communities

Understanding the roles of microorganisms in environmental settings by linking phylogenetic identity to metabolic function is a key challenge in delineating their broad-scale impact and functional diversity throughout the biosphere. This work addresses and extends such questions in the context of marine methane seeps, which represent globally relevant conduits for an important greenhouse gas. Through the application and development of a range of culture-independent tools, novel habitats for methanotrophic microbial communities were identified, established settings were characterized in new ways, and potential past conditions amenable to methane-based metabolism were proposed. Biomass abundance and metabolic activity measures – both catabolic and anabolic – demonstrated that authigenic carbonates associated with seep environments retain methanotrophic activity, not only within high-flow seep settings but also in adjacent locations exhibiting no visual evidence of chemosynthetic communities. Across this newly extended habitat, microbial diversity surveys revealed archaeal assemblages that were shaped primarily by seepage activity level and bacterial assemblages influenced more substantially by physical substrate type. In order to reliably measure methane consumption rates in these and other methanotrophic settings, a novel method was developed that traces deuterium atoms from the methane substrate into aqueous medium and uses empirically established scaling factors linked to radiotracer rate techniques to arrive at absolute methane consumption values. Stable isotope probing metaproteomic investigations exposed an array of functional diversity both within and beyond methane oxidation- and sulfate reduction-linked metabolisms, identifying components of each proposed enzyme in both pathways. A core set of commonly occurring unannotated protein products was identified as promising targets for future biochemical investigation. Physicochemical and energetic principles governing anaerobic methane oxidation were incorporated into a reaction transport model that was applied to putative settings on ancient Mars. Many conditions enabled exergonic model reactions, marking the metabolism and its attendant biomarkers as potentially promising targets for future astrobiological investigations. This set of inter-related investigations targeting methane metabolism extends the known and potential habitat of methanotrophic microbial communities and provides a more detailed understanding of their activity and functional diversity.

Dynamic Stall on Vertical Axis Wind Turbines

In this study the dynamics of flow over the blades of vertical axis wind turbines was investigated using a simplified periodic motion to uncover the fundamental flow physics and provide insight into the design of more efficient turbines. Time-resolved, two-dimensional velocity measurements were made with particle image velocimetry on a wing undergoing pitching and surging motion to mimic the flow on a turbine blade in a non-rotating frame. Dynamic stall prior to maximum angle of attack and a leading edge vortex development were identified in the phase-averaged flow field and captured by a simple model with five modes, including the first two harmonics of the pitch/surge frequency identified using the dynamic mode decomposition. Analysis of these modes identified vortical structures corresponding to both frequencies that led the separation and reattachment processes, while their phase relationship determined the evolution of the flow.

Detailed analysis of the leading edge vortex found multiple regimes of vortex development coupled to the time-varying flow field on the airfoil. The vortex was shown to grow on the airfoil for four convection times, before shedding and causing dynamic stall in agreement with 'optimal' vortex formation theory. Vortex shedding from the trailing edge was identified from instantaneous velocity fields prior to separation. This shedding was found to be in agreement with classical Strouhal frequency scaling and was removed by phase averaging, which indicates that it is not exactly coupled to the phase of the airfoil motion.

The flow field over an airfoil undergoing solely pitch motion was shown to develop similarly to the pitch/surge motion; however, flow separation took place earlier, corresponding to the earlier formation of the leading edge vortex. A similar reduced-order model to the pitch/surge case was developed, with similar vortical structures leading separation and reattachment; however, the relative phase lead of the separation mode, corresponding to earlier separation, necessitated that a third frequency to be incorporated into the reattachment mode to provide a relative lag in reattachment.

Finally, the results are returned to the rotating frame and the effects of each flow phenomena on the turbine are estimated, suggesting kinematic criteria for the design of improved turbines.

Avaliação de ligações entre perfis tubulares em aço submetidas a esforços de flexão.

Os perfis tubulares sem costura são largamente utilizados em diversos países, porém, no Brasil, o uso desses perfis na construção civil era bastante limitado, restringindo-se praticamente a coberturas espaciais. Considerando uma nova realidade para o uso de perfis tubulares, este trabalho apresenta uma análise de ligações tipo T com perfis tubulares quadrados (SHS) para o banzo e para o montante efetuada com base na norma europeia, Eurocode 3, no CIDECT, na NBR 16239:2013 e ISO 14346 através de um modelo em elementos finitos desenvolvido no programa Ansys. Verificou-se a influência do momento atuante no montante no comportamento global das ligações. As não-linearidades físicas e geométricas foram incorporadas aos modelos, a fim de se mobilizar totalmente a capacidade resistente desta ligação. A não-linearidade do material foi considerada através do critério de plastificação de von Mises através da lei constitutiva tensão versus deformação trilinear de forma a exibir um comportamento elasto-plástico com encruamento. A nãolinearidade geométrica foi introduzida no modelo através da Formulação de Lagrange Atualizado. Os resultados numéricos são avaliados para a ligação em estudo quanto aos modos de falha e a distribuição de tensões. A análise dos momentos resistentes obtidos em comparação com os resultados do modelo numérico, apresentou valores excessivamente a favor da segurança no cálculo utilizando as equações de dimensionamento. Um estudo para fatores de correção das equações de dimensionamento é proposto.

Influence of fluid viscosity on the response of buried structures in earthquakes

The use of high viscous pore fluid has been widely established to match the rate of excess pore pressure generation and subsequent dissipation in dynamic centrifuge tests. The appropriate viscosity is linked to the geometric and gravity scaling factors which corresponds to the use of pore fluid of 'N' cSt in a 'N'g centrifuge test. The use of either water (1 cSt) or pore fluid lower than 'N' cSt can influence the behaviour of soil liquefaction in a centrifuge test. In this paper, the floatation of a tunnel following soil liquefaction is investigated using pore fluids with two different viscosities. The results show that the uplift displacement of the tunnel is significantly affected by the pore fluid viscosity. © 2010 Taylor & Francis Group, London.

A recursive rule base adjustment algorithm for a fuzzy logic controller

This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule base be altered by combined tuning of membership functions and controller scaling factors. The alternative approach proposed here entails alteration of the fuzzy rule base. The recursive rule base adjustment algorithm proposed in this paper has the benefit that it is computationally more efficient for the generation of a DT, and advantage for online realization. Simulation results are presented to support this thesis. (c) 2005 Elsevier B.V. All rights reserved.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

On the potential of significance-driven execution for energy-aware HPC

Dynamic Voltage and Frequency Scaling (DVFS) exhibits fundamental limitations as a method to reduce energy consumption in computing systems. In the HPC domain, where performance is of highest priority and codes are heavily optimized to minimize idle time, DVFS has limited opportunity to achieve substantial energy savings. This paper explores if operating processors Near the transistor Threshold Volt- age (NTV) is a better alternative to DVFS for break- ing the power wall in HPC. NTV presents challenges, since it compromises both performance and reliability to reduce power consumption. We present a first of its kind study of a significance-driven execution paradigm that selectively uses NTV and algorithmic error tolerance to reduce energy consumption in performance- constrained HPC environments. Using an iterative algorithm as a use case, we present an adaptive execution scheme that switches between near-threshold execution on many cores and above-threshold execution on one core, as the computational significance of iterations in the algorithm evolves over time. Using this scheme on state-of-the-art hardware, we demonstrate energy savings ranging between 35% to 67%, while compromising neither correctness nor performance.

TProf: An energy profiler for task-parallel programs

We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS).

Power-Capped DVFS and Thread Allocation with ANN Models on Modern NUMA Systems

Abstract—Power capping is an essential function for efficient power budgeting and cost management on modern server systems. Contemporary server processors operate under power caps by using dynamic voltage and frequency scaling (DVFS). However, these processors are often deployed in non-uniform memory
access (NUMA) architectures, where thread allocation between cores may significantly affect performance and power consumption. This paper proposes a method which maximizes performance under power caps on NUMA systems by dynamically optimizing two knobs: DVFS and thread allocation. The method selects the optimal combination of the two knobs with models based on artificial neural network (ANN) that captures the nonlinear effect of thread allocation on performance. We implement
the proposed method as a runtime system and evaluate it with twelve multithreaded benchmarks on a real AMD Opteron based NUMA system. The evaluation results show that our method outperforms a naive technique optimizing only DVFS by up to
67.1%, under a power cap.

Power Modeling and Capping for Heterogeneous ARM/FPGA SoCs

Low-power processors and accelerators that were originally designed for the embedded systems market are emerging as building blocks for servers. Power capping has been actively explored as a technique to reduce the energy footprint of high-performance processors. The opportunities and limitations of power capping on the new low-power processor and accelerator ecosystem are less understood. This paper presents an efficient power capping and management infrastructure for heterogeneous SoCs based on hybrid ARM/FPGA designs. The infrastructure coordinates dynamic voltage and frequency scaling with task allocation on a customised Linux system for the Xilinx Zynq SoC. We present a compiler-assisted power model to guide voltage and frequency scaling, in conjunction with workload allocation between the ARM cores and the FPGA, under given power caps. The model achieves less than 5% estimation bias to mean power consumption. In an FFT case study, the proposed power capping schemes achieve on average 97.5% of the performance of the optimal execution and match the optimal execution in 87.5% of the cases, while always meeting power constraints.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Performance and Fault Tolerance of Preconditioned Iterative Solvers on Low-Power ARM Architectures

As the complexity of computing systems grows, reliability and energy are two crucial challenges asking for holistic solutions. In this paper, we investigate the interplay among concurrency, power dissipation, energy consumption and voltage-frequency scaling for a key numerical kernel for the solution of sparse linear systems. Concretely, we leverage a task-parallel implementation of the Conjugate Gradient method, equipped with an state-of-the-art pre-conditioner embedded in the ILUPACK software, and target a low-power multi core processor from ARM.In addition, we perform a theoretical analysis on the impact of a technique like Near Threshold Voltage Computing (NTVC) from the points of view of increased hardware concurrency and error rate.

Evaluating fault tolerance on asymmetric multicore systems-on-chip using iso-metrics

The end of Dennard scaling has promoted low power consumption into a firstorder concern for computing systems. However, conventional power conservation schemes such as voltage and frequency scaling are reaching their limits when used in performance-constrained environments. New technologies are required to break the power wall while sustaining performance on future processors. Low-power embedded processors and near-threshold voltage computing (NTVC) have been proposed as viable solutions to tackle the power wall in future computing systems. Unfortunately, these technologies may also compromise per-core performance and, in the case of NTVC, xreliability. These limitations would make them unsuitable for HPC systems and datacenters. In order to demonstrate that emerging low-power processing technologies can effectively replace conventional technologies, this study relies on ARM’s big.LITTLE processors as both an actual and emulation platform, and state-of-the-art implementations of the CG solver. For NTVC in particular, the paper describes how efficient algorithm-based fault tolerance schemes preserve the power and energy benefits of very low voltage operation.

Energy Optimization of Memory Intensive Parallel Workloads

Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.