Multi-threaded Approach Research Articles

Single and multi-threaded power flow algorithm for integrated transmission-distribution-residential networks

The rapid development of renewable sources has significantly increased interdependencies between electricity networks of all voltage levels, leading to bidirectional flows between transmission and distribution networks, and requiring analysis of Integrated Transmission-Distribution (ITD) network. The interconnectivity is further amplified by additional coupling with electricity, gas, heat and hydrogen networks, on both national and regional levels. Moreover, installation of solar, wind and storage on customers’ premises has indicated that residential networks need to be included in the overall integrated model, called Integrated Transmission-Distribution-Residential (ITDR) network. The main goal of the paper is to develop a general model of the ITD/ITDR networks and to solve the power-flow problem on large-scale networks in an efficient way. The general model includes three-phase transmission and multi-phase distribution models, as well as accurate control strategies for traditional and electronically coupled electricity resources. The proposed model is solved via novel single-threaded power flow procedure, which incorporates new network elements’ models and the developed algorithm for integrated power flow calculations in different domains. This is further improved by developing a multi-threaded approach. Analyses on a small-scale ITD network have shown that single- and multi-threaded approaches are, respectively, (1.5–4) and (2–5) times faster compared to the state-of-the-art procedures. As network size increases, the efficiency of the proposed multi-threaded procedure becomes more pronounced compared to the single-threaded one (up to 2.39 times).

A new approach for software-simulation of membrane systems using a multi-thread programming model

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a new model for simulating P systems using a multi-threaded approach in a multi-core processor. This simulation approach establishes a new paradigm that is entirely in line with the philosophy of P-systems: since objects must react in parallel, asynchronously and autonomously with other objects, simulation using multiple synchronized threads completely mimics the behavior of objects within a membrane. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The obtained results show that the new model is correct and that it can be extended to other more complex types of P systems, in order to discover which are the limit of this multi-threaded approach when running it in multi-core processors.

StateOS : A Memory Sufficient Hybrid Operating System for IOT Devices

The proliferation of Internet of Things (IoT) technology has spurred the need for efficient operating systems tailored to the constraints of IoT devices. This paper presents StateOS, a hybrid operating system designed to meet the demands of sensor-based IoT devices and wireless sensors. StateOS combines the memory efficiency of event-driven systems with the clarity of control flow provided by multithreaded approaches. By implementing a microkernel architecture, cross-layer network protocol design, and a hybrid task scheduler, StateOS offers real-time capabilities while minimizing memory footprint. Macro-based task APIs and state machine-based visual programming facilitate cooperative threaded programming, making StateOS suitable for both experienced developers and novices. The paper provides an in-depth overview of StateOS's architecture, kernel functionalities, code examples, implementation on various platforms, performance evaluation, and concludes by highlighting its potential as a valuable tool for modern IoT application development. Keywords — Hybrid operating system (OS), Internet of Things (IoT) and Memory efficiency.

High-Performance Krawtchouk Polynomials of High Order Based on Multithreading

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

Outdoor Navigation Assistive System Based on Robust and Real-Time Visual-Auditory Substitution Approach.

Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are guided by a short 3D spatialised sound that indicates the target point to follow. This sound is combined with other sonified information on potential obstacles in the vicinity. The proposed system is based on inertial sensors, GPS data, and the cartographic knowledge of pedestrian paths to define the trajectory. In addition, visual clues are used to refine the trajectory with ground floor information and obstacle information using a camera to provide 3D spatial information. The proposed method is based on a deep learning approach. The different neural networks used in this approach are evaluated on datasets that regroup navigations from pedestrians' point-of-view. This method achieves low latency and real-time processing without relying on remote connections, instead using a low-power embedded GPU target and a multithreaded approach for video processing, sound generation, and acquisition. This system could significantly improve the quality of life and autonomy of blind people, allowing them to reliably and efficiently navigate in their environment.

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

To locate disease-causing DNA variants on the human gene map, the customary approach has been to carry out a genome-wide association study for one variant after another by testing for genotype frequency differences between individuals affected and unaffected with disease. So-called digenic traits are due to the combined effects of two variants, often on different chromosomes, while individual variants may have little or no effect on disease. Machine learning approaches have been developed to find variant pairs underlying digenic traits. However, many of these methods have large memory requirements so that only small datasets can be analyzed. The increasing availability of desktop computers with large numbers of processors and suitable programming to distribute the workload evenly over all processors in a machine make a new and relatively straightforward approach possible, that is, to evaluate all existing variant and genotype pairs for disease association. We present a prototype of such a method with two components, Vpairs and Gpairs, and demonstrate its advantages over existing implementations of such well-known algorithms as Apriori and FP-growth. We apply these methods to published case-control datasets on age-related macular degeneration and Parkinson disease and construct an ROC curve for a large set of genotype patterns.

A parallel algorithm to find the exact solution of the travelling salesman problem

The traveling salesman problem (TSP) is a problem in computer science that has been extensively studied and has a wide variety of real-world applications. It is considered an NP-hard issue since the only way to get a precise solution to it is to wait an exponentially long amount of time unless P=NP. Both accurate and heuristic algorithms exist to tackle this problem. The Branch-and-Bound algorithm (BnB) is often regarded as the most significant precise approach, despite the fact that it traverses, in the worst scenario, all potential tours in order to calculate the tour efficiently. This study proposed an efficient parallel algorithm to solve TSP by using a cluster multicore system. An exact algorithm will be used to get the optimal solution, and to expedite the search for the best path, we have employed a multi-threaded approach that capitalizes on the processing power of multiple CPU cores to concurrently process sub-problems.

Parallelization of numerical solutions of shallow water equations by the finite volume method for implementation on multiprocessor systems and graphics processors

An overview of approaches to parallelization of grid-based numerical methods for solving shallow water equations for multiprocessor systems and graphics processors is presented. A multithreaded approach for shared-memory computing systems implemented on the basis of the OpenMP programming interface and a geometric decomposition approach with message-passing using the MPI library for distributed-memory computers are described. Multithreading for programming GPUs based on the OpenACC software interface is considered. For the COASTOX-UN system of two-dimensional modeling of hydrodynamics, sediment and radionuclide transport in river systems and coastal areas of the seas, the parallelization of its hydrodynamic model COASTOX-HD was carried out. In the developed numerical model, the shallow water equations are solved by finite-volume numerical methods on unstructured computational grids with triangular cells of variable size. The parallelization is implemented using a hybrid MPI+OpenACC approach targeting multiprocessor systems and GPUs. For multiprocessor computers, geometric decomposition and MPI-based messaging are used, and for GPUs, multithreading is implemented using OpenACC directives. The performance of the developed parallel hydrodynamic model was evaluated during the calculation of typical problems of hydrodynamics of shallow water bodies, river flood, and tsunami wave run-up on the coast on a Dell Precision Workstation 7920 multi-core workstation with two 20-core Intel Xeon Gold 6230 processors and NVIDIA Quadro RTX 5000 and NVIDIA GeForce RTX 3080 graphics cards. It is shown that the developed model has significantly accelerated the simulation on the considered multiprocessor system and the considered GPUs. The acceleration on GPUs depends on the size of the computational grid, increasing to saturation with an increase in the number of grid cells. It is established that for the developed parallel model, whose numerical schemes are related to algorithms with low computational intensity, the memory bandwidth of the NVIDIA architecture GPUs is a more important limiting factor of acceleration than their performance.

StateOS: A Memory-Efficient Hybrid Operating System for IoT Devices

The increasing significance of operating systems (OSs) in the development of the internet of things (IoT) has emerged in the last decade. An event-driven OS is memory efficient and suitable for resource-constrained IoT devices and wireless sensors, although the program’s control flow, which is determined by events, is not always obvious. A multithreaded OS with sequential control flow is often considered clearer. However, this approach is memory-consuming. A hybrid OS seeks to combine the strengths of the event-driven approach with multithreaded approach. An event-driven cooperative threaded OS represents a hybrid approach that supports concurrency by explicitly yielding control to another thread. Although this approach is memory efficient, as cooperative threads are not preemptive, it may not provide sufficient real-time performance. This article proposes a memory-efficient hybrid OS, called StateOS, for resource-constrained IoT devices. It is an event-driven cooperative threaded OS with partial real-time performance. StateOS implements a hybrid task scheduler that combines two cooperative threaded subsystems as kernel processes on a priority-based preemptive scheduler. This approach provides adequate real-time performance for IoT devices at a low memory cost.

Task-Based Parallel Programming for Gate Sizing

Physical synthesis engines need to embrace all available parallelism to cope with the increasing complexity of modern designs and still offer high quality of results. To achieve this goal, the involved algorithms need to be expressed in a way that facilitates fast execution time across a range of computing platforms. In this work, we introduce a task-based parallel programming template that can be used for speeding up timing and power optimization. This approach utilizes all available parallelism and enables significant speedup relative to custom multithreaded approaches. Task-based parallelism is applied to all parts of the optimization engine covering also parts that are traditionally executed serially for preserving maximum timing accuracy. Using Taskflow as the parallel programming and execution engine, we achieved a speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> for gate sizing optimizations on the ISPD13 benchmarks with marginal extra leakage power relative to state-of-the-art multithreaded gate sizers. This result was supported by two dynamic heuristics that restrict the number of examined gate sizes and simplify local timing updates. Both heuristics tradeoff additional runtime reduction with marginal leakage power increases.

Improved Methodology of Cross-Sectional SEM Analysis of Thin-Film Multilayers Prepared by Magnetron Sputtering

In this work, an improved methodology of cross-sectional scanning electron microscopy (SEM) analysis of thin-film Ti/V/Ti multilayers was described. Multilayers with various thicknesses of the vanadium middle layer were prepared by magnetron sputtering. The differences in cross sections made by standard fracture, focused ion beam (FIB)/Ga, and plasma focused ion beam (PFIB)/Xe have been compared. For microscopic characterization, the Helios NanoLab 600i microscope and the Helios G4 CXe with the Quanta XFlash 630 energy dispersive spectroscopy detector from Bruker were used. The innovative multi-threaded approach to the SEM preparation itself, which allows us to retain information about the actual microstructure and ensure high material contrast even for elements with similar atomic numbers was proposed. The fracture technique was the most noninvasive for microstructure, whereas FIB/PFIB results in better material contrast (even than EDS). There were only subtle differences in cross sections made by FIB-Ga and PFIB-Xe, but the decrease in local amorphization or slightly better contrast was in favor of Xe plasma. It was found that reliable information about the properties of modern nanomaterials, especially multilayers, can be obtained by analyzing a two-part SEM image, where the first one is a fracture, while the second is a PFIB cross section.

Multi-threaded approach in generating frequent itemset of Apriori algorithm based on trie data structure

This research is about the application of multi-threaded and trie data structures to the support calculation problem in the Apriori algorithm. The support calculation results can search the association rule for market basket analysis problems. The support calculation process is a bottleneck process and can cause delays in the following process. This work observed five multi-threaded models based on Flynn’s taxonomy, which are single process, multiple data (SPMD), multiple process, single data (MPSD), multiple process, multiple data (MPMD), double SPMD first variant, and double SPMD second variant to shorten the processing time of the support calculation. In addition to the processing time, this works also consider the time difference between each multi-threaded model when the number of item variants increases. The time obtained from the experiment shows that the multi-threaded model that applies a double SPMD variant structure can perform almost three times faster than the multi-threaded model that applies the SPMD structure, MPMD structure, and combination of MPMD and SPMD based on the time difference of 5-itemsets and 10-itemsets experimental result.

Asynchronous object-oriented approach to the automation of the 0.8-meter George Mason University campus telescope in Python

We present a unique implementation of Python coding in an asynchronous object-oriented programming (OOP) framework to fully automate the process of collecting data with the George Mason University (GMU) Observatory's 0.8-meter telescope. The goal of this project is to perform automated follow-up observations for the Transiting Exoplanet Survey Satellite (TESS) mission, while still allowing for human control, monitoring, and adjustments. Prior to our implementation, the facility was computer-controlled by a human observer through a combination of webcams, TheSkyX, ASCOM Dome, MaxIm DL, and a weather station. We have automated slews and dome movements, CCD exposures, saving FITS images and metadata, initial focusing, guiding on the target, using the ambient temperature to adjust the focus as the telescope cools through the rest of the night, taking calibration images (darks and flats), and monitoring local weather data. The automated weather monitor periodically checks various weather data from multiple sources to automate the decision to close the observatory during adverse conditions. We have organized the OOP code structure in such a way that each hardware device or important higher-level process is categorized as its own object class or "module" with associated attributes and methods, with inherited common methods across modules for code reusability. To allow actions to be performed simultaneously across different modules, we implemented a multithreaded approach where each module is given its own CPU thread on which to operate concurrently with all other threads. After the initial few modules (camera, telescope, dome, data I/O) were developed, further development of the code was carried out in tandem with testing on sky on clear nights. The code, in its current state, has been tested and used for observations on 171 nights, with more planned usage and feature additions.

Frame-parallel multithreading in libaom AV1 encoder

The increase in video streaming in recent years has led to the development of advanced video encoders to save bandwidth while meeting practical encoding complexity requirements. Video encoders, therefore, need to provide faster turnaround time through toolset optimizations and/or multithreading (MT). The multithread performance of a video encoder depends on the levels of parallel processing available in the encoder. For the libaom AV1 encoder, the default parallelism is limited to row/tile-based processing within a frame, resulting in low MT scaling even when configured with a high number of threads. We present our proposed frame-parallel MT approach for improving the existing libaom AV1 encoder’s performance. For encoding multiple frames in parallel, it is critical to consider their temporal encoding dependencies. To facilitate the parallel encoding of frames, some frames are prevented from acting as references by either marking them nonreference frames or temporarily removing them from the active reference list. The cross-frame dependencies of various encoding parameters are resolved by disabling or delaying their updates across the frames in a parallel encode set. The proposed method offers an average 31% encode-time reduction at the cost of a reasonable YUV-PSNR BD-rate loss of 1.17% for encoding ultra-high-definition sequences using single tile encoding for cpu-used = 2 with 32 threads. The corresponding MT scaling ratio also improved to 7.1x from 4.9x of the existing libaom implementation.

Automatic compiler/interpreter generation from programs for Domain-Specific Languages: Code bloat problem and performance improvement

Using advanced AI approaches, the development of Domain-Specific Languages (DSLs) can be facilitated for domain experts who are not proficient in programming language development. In this paper, we first addressed the aforementioned problem using Semantic Inference. However, this approach is very time-consuming. Namely, a lot of code bloat is present in the generated language specifications, which increases the time required to evaluate a solution. To improve this, we introduced a multi-threaded approach, which accelerates the evaluation process by over 9.5 times, while the number of fitness evaluations using the improved Long Term Memory Assistance (LTMA) was reduced by up to 7.3%. Finally, a reduction in the number of input samples (fitness cases) was proposed, which reduces CPU consumption further.

H-Map-Based Technique for Mining High Average Utility Itemset

High Utility Itemset Mining (HUIM) is the process of locating itemsets that are profitable and useful to users. One of the key flaws in HUIM is that as the length of the itemset increases, the utility also increases. The true utility/profit of the itemset is not revealed in HUIM. High Average Utility Itemset mining overcomes the limitations of HUIM by taking the length of the itemset into account when estimating the utility. Existing pruning methods used for eliminating weak candidates overestimate the average usefulness of itemsets, causing the mining process to slow down. To prevent processing unpromising candidate itemsets and efficiently reduce the search space and processing time, the proposed methodology employs Upper Bound using Remaining Items Utility, Maximum Itemset Utility, and Sum of Maximum Utility in a Transaction. It also uses the multithreaded parallel approach to reduce the processing time. The H-Map-based data structure (H-Map) used for storing the utility values reduces the lookup time and joins used for itemset extension compared to existing state-of-the-art High Average Utility Itemset mining algorithms. The performance of the proposed work is evaluated in terms of memory usage and the time taken for processing. The proposed work increases the overall efficiency of the system employing effective pruning algorithms for pruning poor candidate itemsets and an efficient data structure for storing utility values.

Parallelized solution to the asymmetric travelling salesman problem using central processing unit acceleration

&lt;p&gt;&lt;span&gt;Travelling salesman problem is a well researched problem in computer science and has many practical applications. It is classified as a NP-hard problem as its exact solution can only be obtained in exponential time unless P = NP. There are different variants of the travelling salesman problem (TSP) and in this paper, asymmetric travelling salesman problem is addressed since this variant is quite often observed in real world scenarios. There are a number of heuristic approaches to this problem which provides approximate solutions in polynomial time, however this paper proposes an exact optimal solution which is accelerated with the help of multi-threading-based parallelization. In order to find the exact optimal solution, we have used the held-karp algorithm involving dynamic programming and to reduce the time taken to find the optimal path, we have used a multi-threaded approach to parallelize the processing of sub-problems by leveraging the central processing unit cores (CPUs). This method is an extension of a well researched solution to the TSP; however, this method shows that solutions to computationally intensive problems involving sub-problems such as the asymmetic travelling salesman problem (ATSP) can be accelerated with the help of modern CPUs.&lt;/span&gt;&lt;/p&gt;

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.

Mining Vehicles Frequently Appearing Together from Massive Passing Records

Vehicles Frequently Appearing Together, or VFATs, can be clues in solving criminal cases. Traditional sequence mining approaches help identify VFATs from passing-through records collected at monitoring sites. However, huge traffic data streams hinder fast identification of VFATs. In this paper, we present a mult i-threaded approach to fast identification of VFATs based on multi-core processors, called Frequent Sequential Mining based on Multi-Cores (FSMMC). It parallels the execution of tasks, partitions large volumes of data, and obtains VFATs by merging local candidates discovered in different threads running on different pro cessor cores. Through local parallel reduction, FSMMC eliminates the repetitive patterns and reduces computational effort. Moreover, it achieves workload balance by the dynamic distribution of tasks to a pool of threads where the thread that finishes first joins anothe r running thread. Both theoretical analysis and case studies show that FSMMC takes full advantage of multi-core computing platforms and has higher speed-up when searching VFATs among massive passing through records, compared with other approaches without multi- threading.

ROLNICTWO A EUROPEJSKI ZIELONY ŁAD

Growing environmental and climate problems are forcing the search for effective solutions to economic activity, including agriculture. The popularization of relevant production practices and techniques is of great importance in this regard. The direction of European agriculture is of particular importance for solving environmental and climate problems. For years, strategies or sustainable development programs have been implemented, which, despite initiating the desired direction of changes, are still insufficient in terms of perceived needs. In December 2019, the European Commission issued a communication on the European Green Deal strategy, which was intended to launch further international action to achieve ambitious climate and environmental targets. The aim of the paper is to present the main issues related to the implementation of the European Green Deal strategy, including agriculture, and to outline the challenges facing it. The study used a review of literature and legal acts. The results of the study indicated the appropriateness of seeking further economic solutions consistent with the European Green Deal strategy. The goals included in the European Green Deal are very ambitious and will require a complex, multi-threaded approach to agricultural policy and a change in the attitude of farmers, i.e., greater consideration of non-production aspects of their activity, in particular in the field of environmental protection. At the same time, the European Green Deal should be assessed in a much broader sense than in terms of environmental requirements. The holistic nature of this document makes it a step towards building a new economy that takes into account the noneconomic consequences of the actions taken.