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About 10,000 papers for “execution time”

Execution time.

Hina Ali (4034399), Khalid Saleem (18864648) · Figshare · 2024

Cited by 0Open access

Execution Time

Alistair Dunlod, Tow Hev · 1997

memory simulation, memory hierarchy, automatic program parallelisation This paper describes a performance estimating system developed at the University of Southampton for estimating the execution time of SPMD Fortran programs on distributed memory parallel systems. The system called PERFORM, (Performance EstimatoR For RISC Microprocessors), is an implementation of a new performance estimating technique based on the concept of execution-driven simulation. Whereas straightforward execution-driven simulation is slow, memory intensive and machine specific, our development of this technique has avoided these traditional problems associated with simulation methods. Specifically, our development of this method uses a feedback loop to reduce the amount of code executed during the simulation, while code sections that do not alter the control flow of the program are removed from the simulation code. Variables used in removed code sections are also removed from the simulation code, significantly reducing the memory requirements. This method is also architecturally independent, in that the properties and performance characteristics of the target platform can be specified in a machine description file. Our results show that PERFORM is a substantial

Cited by 0

Execution Time.

Yaron S. Butterfield (604395), Maayan Kreitzman (433217), Nina Thiessen (604396) et al. · Figshare · 2015

<p>*BWA-MEM algorithm.</p><p>**BWA memory.</p><p>Comparison of execution time and memory usage. All tools were run on a node with 64 GB memory with no other applications running.</p>

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Execution time.

Hina Ali (4034399), Khalid Saleem (18864648) · Figshare · 2024

Cited by 0Open access

Execution time comparisons.

Vaitea Opuu (4966111), Nono S. C. Merleau (11099389), Vincent Messow (13450329) et al. · Figshare · 2022

<p>For samples of 30 sequences per length, we averaged the execution times of five folding tools. The empirical time complexity <i>O</i>(<i>L</i><sup><i>η</i></sup>) where <i>η</i> is obtained by non-linear regression. RAFFT denotes the naive algorithm whereas RAFFT(50) denotes the algorithm where 50 structures can be saved per stack.</p>

Cited by 0Open access

Docking execution time.

Hongjian Li (13702), Kwong-Sak Leung (514367), Pedro J. Ballester (408441) et al. · Figshare · 2015

<p>CPU time and elapsed time in hours of docking 3,000 clean ligands of 3 molecular weight sets against 12 diverse receptors by AutoDock Vina and idock. idock outperforms AutoDock Vina by at least 8.69 times and at most 37.51 times.</p>

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Execution time comparison.

Lanbo Liu (12043943), Lihong Wan (2805469) · Figshare · 2024

<div><p>In the domain of adaptable educational environments, our study is dedicated to achieving three key objectives: forecasting the adaptability of student learning, predicting and evaluating student performance, and employing aspect-based sentiment analysis for nuanced insights into student feedback. Using a systematic approach, we commence with an extensive data preparation phase to ensure data quality, followed by applying efficient data balancing techniques to mitigate biases. By emphasizing higher education or educational data mining, feature extraction methods are used to uncover significant patterns in the data. The basis of our classification method is the robust WideResNeXT architecture, which has been further improved for maximum efficiency by hyperparameter tweaking using the simple Modified Jaya Optimization Method. The recommended WResNeXt-MJ model has emerged as a formidable contender, demonstrating exceptional performance measurements. The model has an average accuracy of 98%, a low log loss of 0.05%, and an extraordinary precision score of 98.4% across all datasets, demonstrating its efficacy in enhancing predictive capacity and accuracy in flexible learning environments. This work presents a comprehensive helpful approach and a contemporary model suitable for flexible learning environments. WResNeXt-MJ’s exceptional performance values underscore its capacity to enhance pupil achievement in global higher education significantly.</p></div>

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The execution time.

Jiliang Zhang (1500367), Shuya Xing (4220347) · Figshare · 2025

<div> Innovation diffusion in China’s local environmental governance occurs both horizontally and vertically from the bottom up. This process is critical to improving environmental policies and advancing our understanding of how local innovations spread. To explore this phenomenon, the study applies fuzzy-set qualitative comparative analysis (fsQCA) to 17 environmental cases. It examines five key factors: central agenda setting, recognition by central government departments, focal events, policy innovation costs and difficulties, and diffusion among local governments. The findings indicate that national policy integration is driven by five distinct pathways, rather than by any single factor. These pathways fall into three broader models: Central Guidance, Drive by Centre and Society, and Local Autonomy. This study introduces a new analytical framework to explain the vertical diffusion of local environmental innovations within China’s governance structure. It deepens our understanding of innovation diffusion in local governance and provides practical guidance for both central and local governments. Additionally, it lays a foundation for future research on environmental policy diffusion. </div>

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Execution time variation.

Ivan Komarov (120593), Ali Dashti (461880), Roshan M. D'Souza (120596) · Figshare · 2015

&lt;p&gt;In this study, we kept the number of queries and varied and to study the mean and standard deviation of execution time over 1000 trials for each data set ().&lt;/p&gt;

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Express execution lines and execution time estimation

Rogério Luís de C. Costa, Pedro Furtado · 2014

In this work, we propose the use of a size-based strategy to estimate the execution time of database commands. Proposed strategy classifies database commands into small or regular and long-running according to their estimated sizes and applies diverse approaches to estimate command's execution time considering specified classes. We prototyped proposed strategy and made several tests. Obtained results prove that our strategy leads to good execution time estimations without affecting system's performance.

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Real-Time Execution Monitoring

Bernhard Plattner · IEEE Transactions on Software Engineering · 1984

Today's programming methodology emphasizes the study of static aspects of programs. In practice, however, monitoring a program in execution, i.e., monitoring a process, is routinely done by any programmer whose task it is to produce a reliable piece of software. There are two reasons why one might want to examine the dynamic aspects of a program: first, to evaluate the performance of a program, and hence to assess its overall behavior; and second, to demonstrate the presence of programming errors, isolate erroneous program code, and correct it. This latter task is commonly called ``debugging a program'' and requires a detailed insight into the innards of a program being executed. Today, many computer systems are being used to measure and control real-world processes. The pace of execution of these systems and their control programs is therefore bound to timing constraints imposed by the real-world process. As a step towards solving the problems associated with execution monitoring of real-time programs, we develop a set of appropriate concepts and define the basic requirements for a real-time monitoring facility. As a test case for the theoretical treatment of the topic, we design hardware and software for an experimental real-time monitoring system and describe its implementation.

Cited by 82

The execution time comparison.

Quyuan Wang (8724972), Xinyu Ni (14859604), Yuping Tu (21755873) et al. · Figshare · 2025

<div> The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying economic mechanisms of resource trading. In this study, we examine how task publishers can effectively allocate budgets between computational and data resources during co-training. To address the uncertainty in data acquisition, we introduce Constant Proportion Portfolio Investment approach to assist in the construction of the payoff maximization problem with budget constraints. With the aid of economic tools, we design Swing Gradient Search Algorithm to obtain the optimal investment portfolio strategy, thereby addressing the coupling relationship between the quantities of resource acquisition. We also explore how market dynamics evolve in response to changes in supply and demand. To maintain dynamic market equilibrium, we develop two types of pricing algorithms, one based on stepped price adjustments for selected sellers, and another based on smoothed adjustments for all sellers. Simulation results demonstrate that the proposed strategies and algorithms offer acceptable performance in terms of algorithmic efficiency and strategic effectiveness, while also preserving fundamental economic principles and supporting stable market dynamics. </div>

Cited by 0Open access

Real-Time Execution Systems

Jack Muckstadt · Springer series in operations research/Springer series in operations research and financial engineering · 2024

Cited by 0

Execution time of classifiers.

Xiangting Shi (21439579), Xiaochen Wang (235611), Yakang Zhang (20598123) et al. · Figshare · 2025

<div> Financial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. The model is tested on two reliable datasets: the European Cardholder (ECH) transactions dataset, which contains 284,807 transactions and 492 fraud instances, and the IEEE-CIS dataset, which has more than 1 million transactions. Our approach enhances comparison to existing methods of feature selection and classification accuracy. On the ECH dataset, RMGACNet achieves an accuracy of 0.9772, a precision of 0.9768, and an F1 score of 0.9770 measures; on the IEEE-CIS dataset, it achieves an accuracy of 0.9882, a precision of 0.9876 and an F1 score of 0.9879. The findings indicate that RMGACNet routinely surpasses existing models’ efficiency and accuracy while ensuring strong execution time performance, especially when handling large-scale datasets. The suggested model demonstrates scalability and stability, making it suitable for real-time financial systems. </div>

Cited by 0Open access