Performance Optimizations Research Articles

NOC-NOC: Towards Performance-optimal Distributed Transactions

Substantial research efforts have been devoted to studying the performance optimality problem for distributed database transactions. However, they focus just on optimizing transactional reads, and thus overlook crucial factors, such as the efficiency of writes, which also impact the overall system performance. Motivated by a recent study on Twitter's workloads showing the prominence of write-heavy workloads in practice, we make a substantial step towards performance-optimal distributed transactions by also aiming to optimize writes, a fundamentally new dimension to this problem. We propose a new design objective and establish impossibility results with respect to the achievable isolation levels. Guided by these results, we present two new transaction algorithms with different isolation guarantees that fulfill this design objective. Our evaluation demonstrates that these algorithms outperform the state of the art.

Exergetic efficiency and exergy-based ecological function performance optimizations for two irreversible simple Brayton refrigeration cycle models

Air (Brayton) refrigerator has some application potentials nowadays. Based on two irreversible models of constant- and variable-temperature heat reservoir simple Brayton refrigerators established in previous references, this paper further derives exergetic efficiencies and ecological functions, and provide exergy-based comprehensive comparative analyses and optimizations for the two cycle models. Comparative studies among coefficient of performance (COP), cooling load, exergetic efficiency and ecological function characteristics are performed, including general performance analyses for fixed effectivenesses of heat exchangers and performance optimization by optimizing heat conductance distribution and optimizing heat capacity rate marching between heat-reservoir and working substance. For constant-temperature heat reservoir cycle, when select optimal pressure ratio, exergetic efficiency indicator is more reasonable than cooling load and ecological function indicators. When optimizing heat conductance distribution, cooling load indicator is almost the same as exergetic efficiency indictor. For variable-temperature heat reservoir cycle, the reasonable range of pressure ratio is that slightly larger than optimal one at maximum COP. For fixed pressure ratio, the difference between two optimal heat conductance distributions corresponding maximum cooling load and maximum exergetic efficiency is small. Various fixed design parameters have their influence features on the general performances and optimal performances of the cycle models, they should be considered specifically and pointedly. The major difference comparing with classical thermodynamic analysis is the consideration of heat-transfer loss in heat exchangers herein.

Predictive Control for Unknown Dynamics With Observation Loss: A Temporal Game-Theoretic Approach

This paper is concerned with a model-free predictive control problem on systems with unknown dynamics. Different from existing predictive control, the predictive feedback strategy is designed to take control inputs to cover the infinite horizon when the system exists the observation loss. To predict future control inputs, a temporal game-theoretic approach is presented to model such a predictive control issue as an optimization problem and ensure the optimality of the system performance. Moreover, a predictive algebraic Riccati equation (PARE) is constructed to solve such an optimization problem. By leveraging offline datasets and the real-time data of state and input, a data-driven parallel computational framework is developed to iteratively solve the PARE. In this way, the prior knowledge of the systems is avoided and the computational complexity of the proposed algorithm is reduced. Finally, numerical and practical examples are presented to show the applicability of the proposed results.

Comparative analysis over microstructural, mechanical properties and cutting performance of TiN, TiVN coatings deposited by magnetron sputtering on SiAlON ceramic tool insert

In this work, TiN and TiVN hard coatings were deposited by magnetron sputtering process on SiAlON cutting tools. FESEM and XRD were used to analyze the coating's microstructure and phase composition. The hardness of the coatings was measured through nanoindentation test and the adhesion of the coatings was determined using the scratch test. Turning tests were carried out on uncoated, TiN and TiVN coated cutting tools in D3 hardened steel turning under dry environment. Tool life, wear progression, and cutting force were investigated under different cutting parameters. Meanwhile, the wear patterns and mechanisms were studied. Surface roughness after machining and cutting temperature during machining were also recorded and analyzed during the experimentation work. Chips after every set of experiments were collected and their morphology study was done using FESEM. For cutting tools coated in TiN and TiVN, abrasion with grooves on the worn faces was the main wear mechanism. The results indicate a significant optimization in performance, with cutting forces minimized by up to 30 %, cutting temperatures reduced by 20 %, and machined surface roughness improved by 45 % when compared to the TiN coated cutting tool. The experimental findings showed that the TiVN-coated SiAlON cutting tool outperformed the uncoated cutting tool and the TiN coated SiAlON cutting tool in terms of cutting performance. This research additionally offers a comparative experimental behavior of coated cutting tools, along with an investigation into cutting performance, which is essential for advancing research and enhancing our understanding of machining hardened steels.

Challenges and opportunities for process intensification in Europe from a process systems engineering perspective

Process Intensification (PI) is an effective way to enhance process efficiency and sustainability at affordable costs and efforts, attracting particular interest in the European area, as one of the most important chemical production areas in the world. PI primarily contributes by developing and testing new processing technologies that once integrated within a process improve the overall process performance substantially but as a result, it may alter the overall process (flowsheet) structure and its dynamic behavior. As such PI plays a key role in improving energy efficiency, optimizing resource allocation, and reducing environmental impact of industrial processes, and thereby leading to a cost-effective, eco-efficient, low-carbon and sustainable industry. However, along with opportunities, the PI new technologies have challenges related to failures in longer-term performance. In this respect, Process Systems Engineering (PSE) stance is more on integration aspects of new PI technologies into processes by making process (re)designs, doing operability studies, and performance optimizations within a supply chain setting. PSE contributes to overcoming the challenges by providing systematic approaches for the design and optimization of PI technologies. This perspective paper is a lightly referenced scholarly opinion piece about the status and directions of process intensification field from a PSE viewpoint. Primarily, it focuses on PSE perspectives towards sustainable lower energy usage process systems and provides a brief overview of the current situation in Europe. It also emphasizes the key challenges and opportunities for (new) PI technologies considering their integration in a process in terms of process synthesis and design, process flowsheet optimization, process and plantwide control, (green) electrification, sustainability improvements. Potential research directions on these aspects are given from an industrial and academic perspective of the authors.

Metal-Free, Bio-Triboelectric Nanogenerator Based on a Single Electrode of Bacterial Cellulose Modified with Carbon Black

Developing metal-free electrodes for prototypes of bio-based devices is an essential step in producing non-toxic components for implantable devices and wearables. In particular, the advancement in self-powered devices is a hot topic for several applications due to the possibility of creating free-battery devices and sensors. In this paper, the modification of bacterial cellulose by the progressive incorporation of carbon black (a conductive filler) was explored as a prototype for bio-based electrodes for triboelectric nanogenerators. This process was controlled by the percolation pathways’ activation through the contact of carbon black grains with the bacterial cellulose membrane, which represents a critical step in the overall process of optimization in the power output performance, reaching an open circuit voltage value of 102.3 V, short circuit current of 2 μA, and power density of 4.89 μW/cm2.

Prediction model for haematoma after tissue expander placement: A retrospective cohort study of 7080 cases over 20 years

Haematoma is an early complication of tissue expander placement and can lead to infection, capsule contracture and various complications, hindering successful reconstruction. However, no scientific models can accurately predict the risk of haematoma following tissue expansion. Therefore, this study aimed to develop and validate a prediction model for haematoma following tissue expander placement. The medical records of patients who underwent expander placement between 2001 and 2021 were obtained from the clinical database of the Department of Plastic Surgery at the Xijing Hospital. A total of 4579 consecutive patients with 7080 expanders and 179 expanded pocket haematomas were analysed. Multivariate logistic regression analysis identified adult age (P = 0.006), male sex (P < 0.001), scar reconstruction (P = 0.019), perioperative hypertension (P < 0.001), face and neck location (P = 0.002) and activated partial thromboplastin time above the normal range (P < 0.001) as risk factors for haematoma. Therefore, these were included in the prediction model, and a nomogram was constructed. The discrimination of the nomogram was robust (area under the curve: 0.78; 95% confidence interval: 0.72–0.83). Further, the prediction model had a strong fit (Hosmer–Lemeshow test, P = 0.066) and maintained similar discrimination after considering performance optimism (bootstrapped area under the curve: 0.79; 95% confidence interval: 0.73–0.84). This clinical prediction model was created using a generalisable dataset and can be utilised to obtain valid haematoma predictions after expander placement, assisting surgeons in implementing preventive measures or interventions to reduce the occurrence of haematoma.

Efficient Matching of Regular Expressions with Lookaround Assertions

Regular expressions have been extended with lookaround assertions, which are subdivided into lookahead and lookbehind assertions. These constructs are used to refine when a match for a pattern occurs in the input text based on the surrounding context. Current implementation techniques for lookaround involve backtracking search, which can give rise to running time that is super-linear in the length of input text. In this paper, we first consider a formal mathematical semantics for lookaround, which complements the commonly used operational understanding of lookaround in terms of a backtracking implementation. Our formal semantics allows us to establish several equational properties for simplifying lookaround assertions. Additionally, we propose a new algorithm for matching regular expressions with lookaround that has time complexity O ( m · n ), where m is the size of the regular expression and n is the length of the input text. The algorithm works by evaluating lookaround assertions in a bottom-up manner. Our algorithm makes use of a new notion of nondeterministic finite automata (NFAs), which we call oracle-NFAs. These automata are augmented with epsilon-transitions that are guarded by oracle queries that provide the truth values of lookaround assertions at every position in the text. We provide an implementation of our algorithm that incorporates three performance optimizations for reducing the work performed and memory used. We present an experimental comparison against PCRE and Java’s regex library, which are state-of-the-art regex engines that support lookaround assertions. Our experimental results show that, in contrast to PCRE and Java, our implementation does not suffer from super-linear running time and is several times faster.

Интеграция отечественного шифрования в протокол PDCP сетей связи пятого поколения

A software library in C language is created which contains a high-performance implementation of encryption algorithms used in PDCP protocol of 5G NR network. The standard set of algorithms is extended with GOST R 34.12–2015 («Kuznyechik»). Performance optimizations are done by using special extensions to x86 architecture instruction set: AES-NI и AVX. Implementation results for the two most common and secure algorithms, AES and «Kuznyechik», are presented. Their performance was evaluated including consumed clock cycles during execution. All optimizations are described and development prospects of «Kuznyechik» implementation optimizations are given.

Precise Tracking Control for Articulating Crane: Prescribed Performance, Adaptation, and Fuzzy Optimality by Nash Game.

Articulating crane (AC) is used in various industrial activities. The articulated multisection arm exacerbates nonlinearities and uncertainties, making the precise tracking control challenging. This study proposes an adaptive prescribed performance tracking control (APPTC) for AC to robustly fulfill the task of precise tracking control, with adaptation to resist time-variant uncertainties, whose bounds are unknown but lie in prescribed fuzzy sets. Particularly, a state transformation is applied to simultaneously track the desired trajectory and satisfy the prescribed performance. Adopting the fuzzy set theory to describe uncertainties, APPTC does not invoke any IF-THEN fuzzy rules. There is no linearizations, or nonlinear cancelation for APPTC, thus making it approximation free. The performance of the controlled AC is twofold. First, deterministic performance in fulfilling the control task is ensured by the Lyapunov analysis using uniform boundedness and uniform ultimate boundedness. Second, fuzzy-based performance is further improved by an optimal design, which seeks the optima of control parameters by formulating a two-player Nash game. The existence of Nash equilibrium is theoretically proved, and its acquisition process is given. The simulation results are provided for validations. This is the first endeavor that explores the precise tracking control for fuzzy AC.

Unveiling the potential of emergent nanoscale composite polymer electrolytes for safe and efficient all solid-state lithium-ion batteries.

Solid-state polymer electrolytes (SSPEs) are promising materials for Li-ion batteries due to their enhanced safety features, which are crucial for preventing short circuits and explosions, replacing traditional liquid electrolytes with solid electrolytes are increasingly important to improve battery reliability and lifespan. There are essentially three-types of solid-state electrolytes such as solid polymer electrolyte, composite based polymer electrolyte and gel-based polymer electrolyte are largely used in battery applications. Additionally, battery separators must have high ionic conductivity and porosity to boost safety and performance. Durable solid composites electrolytes with excellent thermal and mechanical properties are key to reducing the risk of lithium dendrite growth, thereby improving overall battery efficiency. Despite their potential, challenges like scalability, cost and real-world performance optimizations still need to be addressed.

A Primal-Dual Box-Constrained QP Pressure Poisson Solver With Topology-Aware Geometry-Inspired Aggregation AMG.

We propose a new barrier-based box-constrained convex QP solver based on a primal-dual interior point method to efficiently solve large-scale pressure Poisson problems with non-negative pressure constraints, which commonly arise in liquid animation. The performance of prior active-set-based approaches is limited by the need to repeatedly update the active set. Our solver eliminates this issue by entirely avoiding the use of an active set, which in turn makes the inner problems of our Newton iteration process fully unconstrained. For efficiency, exploiting the solution uniqueness of convex QPs and the fact that the pressure constraints are simple box constraints, we aggressively update solution candidates without performing any step selection procedure (such as line search) and instead directly clamp candidates back to the bounds wherever constraint violations occur. Additionally, to accelerate the inner linear solves, we present a topology-aware geometry-inspired aggregation algebraic multigrid preconditioner and describe in detail several key performance optimizations that we incorporate. We demonstrate the efficacy of our solver in various practical scenarios and show that it often surpasses various alternatives in terms of speed and memory usage.

Generalized Conditional Feedback System with Model Uncertainty

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a half-quadrotor system control test. Furthermore, the GCF scheme is open to existing optimal control and robust control theories.

Exploring Lean Six Sigma as Dynamic Capability to Enable Sustainable Performance Optimisation in Times of Uncertainty

The purpose of this study is to develop a nested theoretical model (LSS-DC) by critically examining two distinct theoretical concepts, including Lean Six Sigma (LSS) and Dynamic Capabilities (DC), for achieving organizational sustainable performance optimizations (PO). The robust integration of this dynamic concept is achieved using a systematic literature review, synthesis, and empirical evidence derived from 2005 to 2022. The vital benefits of LSS-DC are identified. This study utilizes a systematic literature review method adapted. It reveals the cross-sectional literature search strategy deploying selective keywords DCs, LSS, DCs and LSS, DCs and LSS and PO. In this niche domain employing descriptive and thematic analysis, key insights are extracted from the literature, encompassing a total of 21 peer-reviewed journals. The selection criteria revolve around three aspects: ‘Purpose’, ‘Authorship’, and ‘Credibility and Accuracy’. The authors gathered the secondary data from credible databases such as Scopus, Web of Science, PubMed, ERIC, and IEEE using the keyword search. The study reveals the robust integration of theoretical concepts of LSS and DCs and their impact on organisational performance. The findings suggest that integrating the micro-foundations of DCs (sensing, seizing, and transforming) with LSS allows organisations to not only identify improvement opportunities but also efficiently and effectively act upon them, ultimately leading to sustainable performance optimisation across various aspects of the business. The specific type of DC integration with LSS depends on the organisation’s goals and priorities. The findings of this study are subjective to some extent due to the applied research methodology. Further empirical research is needed to gain a deeper understanding of the phenomenon. This study considers LSS as DC providing an empirical (LSS-DCs) model for sustainable performance optimisation. This is achieved by robustly integrating two distinct theoretical concepts derived from an extensive literature review and the analysis of the data-driven implementation. Finally, the study offers a deeper understanding in terms of how contextual organisational characteristics enhance the outcome of LSS-DC.

Optimizations of performance in a spectral beam splitting photovoltaic/thermal system using ideal optical windows

Performance of a spectral beam splitting photovoltaic/thermal system depends on the characteristics of the splitting liquid. The ideal optical window directly guides the selection of splitting liquid. Most of the existing research established ideal optical windows in step function shapes and there is lack of systematic investigations. In this paper, the wavelength range to be studied is divided into discrete independent grids, thus introducing gridded transmissivity. A hypothetical liquid with unknown gridded transmissivity is used in a double-pass spectral beam splitting photovoltaic/thermal system model. Using particle swarm optimization algorithm, taking the best system performance as the target, the gridded transmissivity is determined. As the grid number increases, the determined distribution of gridded transmissivity converges to a step function. Based on the determined pattern, further optimizations are conducted. Moreover, considering the power consumption of the pump, the initial wavelengths, termination wavelengths, and flow rates to achieve the maximum efficiencies are also determined and discussed. Finally, metallic nanofluids are used to approach a determined ideal optical window. Combination of multiple kinds of metallic nanoparticles shows improvement over one kind in the system. A broadly tenable trend that being closer to the optimal window results in higher efficiency is also obtained.

WFA-GPU: gap-affine pairwise read-alignment using GPUs.

Advances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio and Nanopore technologies. The recently proposed wavefront alignment (WFA) algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. However, high-performance computing (HPC) platforms require efficient parallel algorithms and tools to exploit the computing resources available on modern accelerator-based architectures. This paper presents WFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute exact gap-affine alignments based on the WFA algorithm. We present the algorithmic adaptations and performance optimizations that allow exploiting the massively parallel capabilities of modern GPU devices to accelerate the alignment computations. In particular, we propose a CPU-GPU co-design capable of performing inter-sequence and intra-sequence parallel sequence alignment, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original multi-threaded WFA implementation by up to 4.3× and up to 18.2× when using heuristic methods on long and noisy sequences. Compared to other state-of-the-art tools and libraries, the WFA-GPU is up to 29× faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations. Furthermore, WFA-GPU is the only GPU solution capable of correctly aligning long reads using a commodity GPU. WFA-GPU code and documentation are publicly available at https://github.com/quim0/WFA-GPU.

Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage Systems

The use of storage tiering is becoming popular in data-intensive compute clusters due to the recent advancements in storage technologies. The Hadoop Distributed File System, for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained storage tiering solutions. However, current big data platforms (such as Hadoop and Spark) are not exploiting the presence of storage tiers and the opportunities they present for performance optimizations. Specifically, schedulers and prefetchers will make decisions only based on data locality information and completely ignore the fact that local data are now stored on a variety of storage media with different performance characteristics. This article presents Trident, a scheduling and prefetching framework that is designed to make task assignment, resource scheduling, and prefetching decisions based on both locality and storage tier information. Trident formulates task scheduling as a minimum cost maximum matching problem in a bipartite graph and utilizes two novel pruning algorithms for bounding the size of the graph, while still guaranteeing optimality. In addition, Trident extends YARN’s resource request model and proposes a new storage-tier-aware resource scheduling algorithm. Finally, Trident includes a cost-based data prefetching approach that coordinates with the schedulers for optimizing prefetching operations. Trident is implemented in both Spark and Hadoop and evaluated extensively using a realistic workload derived from Facebook traces as well as an industry-validated benchmark, demonstrating significant benefits in terms of application performance and cluster efficiency.

Insecure or optimistic? Employees’ diverging appraisals of automation, and consequences for job attitudes

Automation may have varied consequences on individual workers, ranging from job loss to enhanced productivity. Although some of these disruptions may be outside of their control, employees' perceptions and reactions to automation may also play a part in shaping automation's impact on them. To this end, we draw on transactional theory of stress to distinguish between a variety of automation perceptions. In particular, we suggest that employees' beliefs that technology can conduct their work (perceived automatability) will be related to both pessimistic (automation-related job insecurity) and optimistic (automation-related performance optimism) appraisals regarding how automation will impact job prospects. Reasoning that people who feel in control feel they can mitigate the harms and harness the potential benefits of automation, we propose that perceived control moderates the extent to which perceived automatability leads to job insecurity and performance optimism. In-turn, we expect that automation-related job insecurity and performance optimism will have contrasting effects on job engagement and turnover intentions. Using a survey (N = 500) and an experiment (N = 194) we found overall support for these predictions. As such, this research sheds new light on employees' perspectives on automation at work, with implications for organizations and policymakers seeking to help employees transition to the future of work.

Transient-Execution Attacks: A Computer Architect Perspective

Computer architects employ a series of performance optimizations at the micro-architecture level. These optimizations are meant to be invisible to the programmer but they are implicitly programmed alongside the architectural state. Critically, the incorrect results of these optimizations are not scrubbed off the micro-architectural state. This side-effect may seem innocuous. However, through transient-execution, an attacker can leverage this knowledge to obtain information from the micro-architectural state and transmit the data to itself. Transient-Execution is a class of attacks that use the side-effects of executed instructions to leak data. Transient-Execution attacks are split into two categories: speculation-based (Spectre-type) and exception-based (Meltdown-type). A successful attack requires, first, access to the sensitive information, and, second, a transmission channel such that the data can be recovered. Therefore, this survey explains how an attacker can use the state from optimizations in the micro-architecture to access sensitive information from other programs running in the same device; and, once the information is obtained, it describes how the data can be encoded and transmitted in the micro-architectural state. Moreover, it introduces a taxonomy and analyzes defenses for such malicious attacks.

Building functional warm-up routines in basketball: a narrative review of literature

Warming up is a crucial preparatory activity used in training sessions and games. Therefore, the aim of this investigation is to examine the construction of functional basketball warm-up routines through a narrative literature review. Four electronic databases were consulted (MEDLINE, Google Scholar, Scielo and LILACS), where 47 articles were selected that discussed warm-up routines in basketball and team sports, as well as 09 textbooks in the field of sports training and physiology of the exercise. A well-designed warm-up promotes optimization in performance and prevents the appearance of injuries. However, there are multiple choices for building warm-up routines. Thus, the structuring of a warm-up should take into account the basic methodological aspects for them to be effective, such as: duration, intensity, adopted exercises, sequence of tasks, use of additional equipment, number of routines and recovery interval. Also, in basketball, the critical times to implement warm-ups are training sessions, game days, in-game re-warm-up, and half-time re-warm-up. Each of these points requires a distinct didactic-pedagogical approach in warm-up procedures. In this way, with the acquisition of a broader view of each component involved in this process, it becomes feasible to solve these issues. Finally, it is imperative that basketball physical trainers are able to masterfully solve the practical problems of warm-up, for more assertive decision-making in relation to routines that incorporate superior results in performance and reduction of injuries.