Efficient Execution Research Articles

Symbolic execution of floating-point programs: How far are we?

Floating-point programs are challenging for symbolic execution due to the constraint solving problem. This paper empirically studies five existing symbolic execution methods for floating-point programs to evaluate their effectiveness and limitations. We have implemented the existing methods based on the state-of-the-art symbolic execution tool KLEE and constructed a real-world floating-point program benchmark for evaluation. We evaluate the existing methods with respect to statement coverage and the ability to detect floating-point exceptions. The results demonstrate that the existing methods complement each other. Based on the evaluation results, we propose a synergistic approach to improving the efficiency of the symbolic execution for floating-point programs. The experimental results indicate our synergistic method’s effectiveness in finding floating-point exceptions.

An analysis of floating wind turbine towing operations using OpenFAST

Abstract The floating wind industry is slowly entering the commercialisation phase and array-scale floating wind farms are imminent. Irrespective of the type of floater used, towing operations represent a crucial marine activity required during installation, major repairs and decommissioning phases. Numerical analysis of towing operations is vital for predicting hydrodynamic motions and towing loads, enabling the identification of metocean limits crucial for achieving optimised safe and efficient execution. This paper presents a unique approach to utilise OpenFAST for the examination of towing operations. Compared to other numerical analysis tools available in the market, OpenFAST stands out as a free, open-source tool specifically designed for wind turbine analysis. The study introduces and evaluates an analytical method using OpenFAST, demonstrating its efficacy in modelling towing dynamics, predicting forces and assessing motion responses of a semi-submersible floating wind turbine in varying wave conditions. The numerical simulations are compared against the results from an experimental campaign. The capability of the method in simulating towing dynamics and the prediction of motion responses are discussed.

Performance Comparison of UCB, TS, and -Greedy TS Algorithms through Simulation of Multi-Armed Bandit Machine

Multi-Armed Bandit (MAB) algorithms are classic algorithms that address sequential decision-making under uncertainty by solving the exploration-exploitation trade-off dilemma. This study investigates the performance comparison of multiple MAB algorithms using a simulated multi-armed bandit machine with Bernoulli reward distribution as the experimental environment. This study compares the performance differences among Upper Confidence Bound (UCB), Thompson Sampling (TS) and -Greedy Thompson Sampling (-TS) in this environment, and attempts to set different parameters, namely the number of arms and the number of experimental rounds and calculate the corresponding cumulative regret of the algorithm under various conditions. The size of the cumulative regret reflects the performance of each algorithm in the simulated slot machine model with rewards that conform to the Bernoulli distribution. In addition, the algorithm running time under the same conditions is also recorded to analyze from the perspective of algorithm efficiency. The experimental results show that under the experimental environment of this study, the cumulative regret produced by the UCB algorithm is more than three times that of the other two algorithms. When the number of trials is small, the cumulative regret generated by the TS algorithm is small, but overall, the performance of the TS algorithm and the -TS algorithm set in this experiment in minimizing the cumulative regret is not much different. However, TS runs in a shorter time under the same conditions. The results of this experiment show that after the number of rounds of experimental operation reaches a large enough number, the operating efficiency of the TS algorithm will be significantly higher than that of the -TS algorithm. TS algorithms have higher randomness, so they show better performance under this experimental condition. The -TS under the parameter setting of this study encourages exploration more in the early stage of the experiment, so it will produce greater cumulative regret than the traditional TS algorithm. In the long run, the performance difference between the two in the multi-armed bandit problem with Bernoulli distribution of rewards is very small. However, the TS algorithm has more advantages in algorithm execution efficiency, so when solving similar problems, the TS algorithm is a better choice.

Ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

ABSTRACTAs we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low‐overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large‐scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

The recovery of decreased executive attention in Tibetan migrants at high-altitude.

Attention is one of the basic cognitive functions sensitive to high altitude, and most studies have focussed on exposure times of approximately 3 years; however, it is unclear how attention changes in migrants who have lived and worked at high altitude for nearly 20 years. We explored the dynamics of attentional networks and neurophysiological mechanisms in migrants over 3-20 years using the Attentional Network Test combined with Electrocardiograph and Electroencephalography and found a consistent quadratic correlation between exposure and executive control efficiency, P3 amplitude and heart rate variability (HRV), with a decrease followed by an increase/relative stability, with approximately 10 years being the breakpoint. However, neither linear nor quadratic trajectories were observed for the alerting and orienting network. Mediation analysis revealed that the P3 amplitude mediated the decrease and increase in executive control efficiency with exposure time depends on the breakpoint. Correlations between HRV and executive control efficiency and P3 amplitude suggest that U-shaped changes in executive control in migrants may be related to body homeostasis maintained by the autonomic nervous system, and that P3 amplitude may serve as a neurophysiological marker of migrants' adaptation/recovery from high-altitude exposure.

Towards invertible 2D crystal structure representation for efficient downstream task execution

Abstract In the study of MoS2 lattice defects, we explore the use of Siamese Neural Networks (SNN) to create invariant embeddings, which respect the crystalline symmetry of the lattice. By training our model with contrastive learning, we successfully differentiate configurations with varying defects, achieving perfect accuracy in recognizing equivalent placements. Our method showcases the capability to predict physical properties like formation energy per site and the bandgap with strong performance across both low and high-defect density scenarios, outperforming traditional methods when enhanced with polynomial features. Despite its effectiveness, the model presents limitations at high defect densities, indicating a need for further refinement. Our approach lays the groundwork for reverse-engineering processes. Thus, we open pathways for generative models that can navigate from specified property ranges to optimal defect configurations, fostering an efficient solution-space exploration for bespoke material synthesis.

A framework for a low-cost system of automated gate control in assays of spatial cognition in fishes.

Automation of experimental setups is a promising direction in behavioral research because it can facilitate the acquisition of data while increasing its repeatability and reliability. For example, research in spatial cognition can benefit from automated control by systematic manipulation of sensory cues and more efficient execution of training procedures. However, commercial solutions are often costly, restricted to specific platforms, and mainly focused on the automation of data acquisition, stimulus presentation, and reward delivery. Animal welfare considerations as well as experimental demands may require automating the access of an animal or animals to the experimental arena. Here, we provide and test a low-cost, versatile Raspberry Pi-based solution for such use cases. We provide four application scenarios of varying complexities, based on our research of spatial orientation and navigation in weakly electric fish, with step-by-step protocols for the control of gates in the experimental setups. This easy-to-implement, platform-independent approach can be adapted to various experimental needs, including closed-loop as well as field experiments. As such, it can contribute to the optimization and standardization of experiments in a variety of species, thereby enhancing the comparability of data.

An adaptive snow ablation-inspired particle swarm optimization with its application in geometric optimization

In response to the shortcomings of particle swarm optimization (PSO), such as low execution efficiency and difficulty in overcoming local optima, this paper proposes a multi-strategy PSO method incorporating snow ablation operation (SAO), known as SAO-MPSO. Firstly, Cubic initialization is performed on particles to obtain a good initial environment. Subsequently, SAO and PSO are combined in parallel, and a balanced search mechanism led by multiple sub-populations is devised, significantly improving the search efficiency of overall population. Finally, the degree day method of SAO is introduced, and particles are endowed with memory of environmental changes to prevent premature convergence of PSO, while balancing the exploration and exploitation (ENE) capabilities in later phases. All adaptive parameters are used throughout this method in place of fixed parameters to improve the robustness and adaptability. For a comprehensive analysis of SAO-MPSO, its good ENE ability is verified on CEC 2020 and CEC 2022 and this method is compared with existing improved PSO versions on both test sets. The results show that SAO-MPSO has certain advantages in the comparison of similar improved algorithms. In order to further validate the strength of SAO-MPSO in dealing with nonlinear optimization problems (OPs) with strong constraints, firstly, based on the ball Wang-Ball (BWB) curve, a combined BWB (CBWB) curve is constructed, and a construction method for CBWB curves that satisfy G1 and G2 continuity is derived. Then, with the energy minimization and scale parameters of the CBWB curve as the optimization objective and variables respectively, a shape optimization model that satisfies G2 continuity is established. Finally, three numerical optimization examples based on this model are solved using SAO-MPSO and compared with 10 other methods. The results show that the energy obtained by SAO-MPSO is the smallest, which verifies the effectiveness of this method applied to shape OPs of CBWB curve.

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management

To optimize the current college sports data information management system, this study combines the Apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the Apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system’s reliability and effectiveness through experiments. The experimental results show that the running time of the traditional Apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10–15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

Improved Execution Efficiency of FPE Scheme Algorithm Based on Structural Optimization

The model of preserving a format encryption scheme based on a Feistel structure has developed rapidly and has been widely used in recent years. In this paper, the software implementation of the FF1 algorithm for the model was presented, and its execution efficiency was evaluated. Then, the efficiency bottleneck problem and its causes were identified. Based on the above analysis results, optimization methods were given from the perspectives of prepossessing, algorithm structure, and format conversion function, and implementation plans were provided. Finally, the simulation results show that the optimized performance improvement is significant, and the degree of performance improvement increases with the increase in plain text length.

Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy

The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.

HiPy: Extracting High-Level Semantics from Python Code for Data Processing

Data science workloads frequently include Python code, but Python's dynamic nature makes efficient execution hard. Traditional approaches either treat Python as a black box, missing out on optimization potential, or are limited to a narrow domain. However, a deep and efficient integration of user-defined Python code into data processing systems requires extracting the semantics of the entire Python code. In this paper, we propose a novel approach for extracting the high-level semantics by transforming general Python functions into program generators that generate a statically-typed IR when executed. The extracted IR then allows for high-level, domain-specific optimizations and the generation of efficient C++ code. With our prototype implementation, HiPy, we achieve single-threaded speedups of 2-20x for many workloads. Furthermore, HiPy is also capable of accelerating Python code in other domains like numerical data, where it can sometimes even outperform specialized compilers.

Optimization of office process task allocation based on deep reinforcement learning

In office platforms, we often need to face the situation of a large number of parallel heterogeneous process tasks. This not only tests the ability of the task executors themselves, but also puts forward requirements for the performance of the collaborative scheduling system. Using the reinforcement learning method, combined with quantitative analysis such as collaborative coordination and slackness, and based on the Markov Bollinger theory, a multi-agent Bollinger model is proposed to realize the optimization scheduling system with overall process coordination and maximum completion time as the optimization objectives, thereby improving the overall execution efficiency. Taking the real business system process as the test scenario, under the same optimization objective, the reinforcement learning algorithm based on D3QN and DRL and the meta-heuristic algorithm based on ant colony are compared to verify the effectiveness of the proposed method.

Area-Time-Efficient Secure Comb Scalar Multiplication Architecture Based on Recoding

With the development of mobile communication, digital signatures with low latency, low area, and high security are in increasing demand. Elliptic curve cryptography (ECC) is widely used because of its security and lightweight. Elliptic curve scalar multiplication (ECSM) is the basic arithmetic in ECC. Based on this background information, we propose our own research objectives. In this paper, a low-latency and low-area ECSM architecture based on the comb algorithm is proposed. The detailed methodology is as follows. The recoding-k algorithm and randomization-Z algorithm are used to improve security, which can resist sample power analysis (SPA) and differential power analysis (DPA). A low-area multi-functional architecture for comb is proposed, which takes into account different stages of the comb algorithm. Based on this, the data dependency is considered and the comb architecture is optimized to achieve a uniform and efficient execution pattern. The interleaved modular multiplication algorithm and modified binary inverse algorithm are used to achieve short clock cycle delay and high frequency while taking into account the need for a low area. The proposed architecture has been implemented on Xilinx Virtex-7 series FPGA to perform ECSM on 256-bits prime field GF(p). In the hardware architecture with only 7351 slices of resource usage, a single ECSM only takes 0.74 ms, resulting in an area-time product (ATP) of 5.41. The implementation results show that our design can compete with the existing state-of-the-art engineering in terms of performance and has higher security. Our design is suitable for computing scenarios where security and computing speed are required. The implementation of the overall architecture is of great significance and inspiration to the research community.

Sec-isoamyl Mercaptan (SIT), a Multi-faceted Disulfide Based Protecting Group for Cysteine Thiol

IntroductionThe successful synthesis of a peptide requires the synchronization of several processes, including the efficient execution of protecting group chemistry. For cysteine (Cys)-peptides, this is more crucial because the trifunctional Cys has a free thiol in its side chain. During synthesis, this free thiol function remains protected with suitable protecting groups and can be removed after synthesis using appropriate methods. Sec-isoamyl mercaptan (SIT) is a versatile disulfide-based protecting group for Cys side chain thiol. The removal of SIT from Cys thiol can be achieved using a mild reducing agent (e. g. DTT). This later promotes efficient disulfide bond formation by oxidation. SIT can also direct/activate the Cys thiol for the chemoselective formation of disulfide bonds by thiol-disulfide interchange.MethodsPeptides were synthesized using solid-phase peptide synthesis techniques. The removal of the SIT group was carried out either in the solid phase or in the solution.ResultsIn the present study, we have shown that SIT can be efficiently removed both in solution and on-resin to facilitate disulfide-bridged peptide synthesis. This was exemplified by two syntheses of an atosiban derivative, where the SIT was removed in solution or in solid-phase. Furthermore, a SIT-based facile one-pot synthesis pathway was devised for disulfide-rich peptides. The strategy was faster and greener as it did not involve using an oxidizer. Conotoxin (two S–S) and linaclotide amide (three S–S) were successfully synthesized by adopting the SIT-based strategy. Finally, a racemization study was carried out for SIT, Trt and StBu-protected Cys-peptides. In all cases, SIT-protected peptides showed lesser racemization than StBu-protected peptides. In some instances (synthesis using DMF), SIT-protected peptides showed less racemization compared to the Trt congeners.ConclusionOverall, the multifaceted use of SIT-protection during the synthesis of disulfide-rich peptides has illustrated its versatility as a Cys thiol protecting group.

Reflections on the Challenges of Old-age Security and Policy Options for an Ageing Population

The global phenomenon of population aging is advancing at an unprecedented pace, particularly in China, where the accelerating aging process poses substantial challenges to the existing pension security system. The issue of elderly care has emerged as a critical topic in the realms of society, economy, and politics, impacting national sustainability and social stability. The challenges brought about by an aging society extend beyond the pressures of pension payments and insufficient elderly care services, encompassing risks related to funding sources and management, as well as concerns over the fairness of the security system. In the face of these challenges, there is an urgent need to optimize pension policies, advance pension system reforms, enhance the elderly care service system, and strengthen the coordination between social and medical security to construct a more equitable and sustainable pension security framework. Measures must be collaboratively advanced through top-level institutional design, policy execution efficiency, and societal involvement to provide robust support in addressing the challenges of aging. This paper will delve into the impact of population aging on the pension security system, analyze the current state and shortcomings of policies, and propose strategies to enhance the system's resilience, offering insights and recommendations for strengthening the pension security framework.

Effects of minimum monitor unit per dynamic control point on intensity‐modulated radiotherapy planning for nasopharyngeal carcinoma: A retrospective study

The present study aimed to improve the dose distribution of radiotherapy planning for nasopharyngeal carcinoma (NPC) by comparing the effects of various minimum monitor units (MUs) per dynamic control point (MMCP) values on the quality and execution efficiency of dynamic intensity-modulated radiotherapy (IMRT) planning. Thirty-four clinically implemented dynamic IMRT plans for patients with NPC were retrospectively selected. In total, 170 plans were obtained by modifying only the MMCP values (set as 1, 3, 5, 7, and 9) in the treatment planning system's (TPS) optimization parameters. These plans were divided into 5 groups. Analyzing the effects of MMCP on the target and organ dose at risk (OAR), also the execution efficiency of the treatment plan in each group and using a quality score system, we conducted an objective quantitative study of the dose distribution and execution efficiency. The target dose evaluation indicators (target coverage (TC), homogeneity index (HI), and conformity index (CI)) of all IMRT plans showed a trend of variation with an increase in MMCP values, and the difference was statistically significant when MMCP values were 5, 7, 9, and 1 (p < 0.05). With an increase in MMCP, the dose to OAR slightly increased, but the difference was not statistically significant (p > 0.05). With an increase in MMCP, the average number of MUs per segment significantly increased (p < 0.01). The groups based on MMCP values of 1, 3, 5, 7, and 9 received quality score system of 1.188, 1.180, 1.171, 0.987, and 1.184, respectively, with the MMCP7 group achieving the lowest score, indicating that this plan had the highest overall quality. The MMCP value for dynamic IMRT planning in the Monaco TPS for patients with NPC should be set to 7 to achieve fewer segments, the best execution efficiency without significantly deteriorating the target and OAR dose.

Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven.

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms. Then, a general HSI restoration model is formulated for optimization. Later, we comprehensively review existing HSI denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy [2-D convolutional neural network (CNN), 3-D CNN, hybrid, and unsupervised networks], to model-data-driven strategy. The advantages and disadvantages of each strategy for HSI denoising are summarized and contrasted. Behind this, we present an evaluation of the HSI denoising methods for various noisy HSIs in simulated and real experiments. The classification results of denoised HSIs and execution efficiency are depicted through these HSI denoising methods. Finally, prospects of future HSI denoising methods are listed in this technical review to guide the ongoing road for HSI denoising. The HSI denoising dataset could be found at https://qzhang95.github.io.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.