Multithreading Technique Research Articles

A counter mode and multi-channel based chaotic image encryption algorithm for the internet of things

To deal with the threat of image privacy leakage in the Internet of things, this paper presents a novel batch images encryption algorithm using the counter mode and a multi-channel processing scheme. We employ multi-thread technique combined with an adapter to construct a novel multi-channel processing scheme, which can encrypt four different sized images in one round. Moreover, the counter encryption mode, which can compute round keys from a plaintext related session key, is introduced to decrease the difficulty of session key management when dealing with batch images. The security tests demonstrate the exceptional performance of the proposed algorithm in terms of security, as evidenced by P-values of statistical tests far larger than 0.01, correlation coefficients and entropies of cipher images close to 0 and greater than 7.99. Additionally, the results of NPCR and UACI tests closely approximate the theoretical values 99.6094% and 33.4635%, the proposed algorithm can better resist statistical, exhaustive, differential, or even chosen plaintext attacks. Moreover, due to the novel parallel scheme with a linear time complexity of O(2W+2H), which demonstrates an acceleration of over 300% compared to existing algorithms, it only takes 2.1sto encrypt one hundred images with varying sizes. Therefore, the proposed algorithm succeeds in exceeding existing algorithms in meeting the efficiency and security requirements for encrypting batch images.

AI for Cloud Cost Management: Predictive and Prescriptive Analytics

With reference to the previous information and literature review, the purpose of this research paper is to evaluate the impact of multithreading concerning computer resource utilization and performance by undertaking and comparing various crucial multithreading techniques, including work stealing, fork join, and the use of the thread pool control. In an experimental context, benchmark applications that can be characteristic of several domains were evaluated in a controlled multicore computing platform. Time, CPU occupation, memory usage and throughputs were determined systematically and compared according to the various workloads. The results highlight the superiority of the work-steing algorithm in keeping the execution time and CPU usage low compared to the rest as a sign of its efficiency in managing work loads. Further, the memory usage and the throughput statistics showed the degree of inefficiency of each algorithm and performance penalty. Interviews with developers elaborated on more practical issues of multithreading as the generally rigid nature of the problems suggested that optimal solutions called for adaptive measures in practice. Based on the results achieved in this research, developers and researchers can increase their knowledge about available multithreading algorithms and make suggestions to choose and apply them with high efficiency and low resource consumption.

Real-time Graph Visualization with JavaFX: Exploring Large-scale Network Structures

The ability to visualize and comprehend complex network structures is crucial in various domains, including social media analysis, computer networks, and transportation systems. However, visualizing large-scale graphs poses significant challenges due to computational limitations and rendering performance constraints. This research paper presents a novel approach to real-time graph visualization using JavaFX, a powerful Java-based framework for developing rich client applications. By leveraging efficient data structures, rendering optimizations, and multithreading techniques, our proposed system achieves real-time visualization of large-scale graphs, enabling users to explore and interact with dynamic network structures seamlessly. The system incorporates advanced layout algorithms and visual encodings to enhance the clarity and interpretability of the visualizations. Extensive experiments were conducted using real-world and synthetic datasets to evaluate the system's performance, scalability, and usability. The results demonstrate the effectiveness of our approach in rendering large graphs in real-time, outperforming existing techniques. Furthermore, a user study was conducted to assess the system's usability and gather feedback on interaction and exploration features, highlighting potential applications in various domains. Keywords— Graph Visualization, Real-time Rendering, JavaFX, Large-scale Networks, Layout Algorithms, Rendering Optimizations, User Interaction, Performance Evaluation.

ENHANCING GAME ENGINE PERFORMANCE THROUGH MULTITHREADING

Today, game engines play a significant role in the modern gaming world, with possible multithreading techniques being pivotal in optimizing gaming performance and overcoming performance bottlenecks. This paper discusses a worthwhile approach to increasing game engine performance by leveraging effective multithreading principles, along with the issues that need to be considered in the design of a multithreaded game engine and aims to contribute to the continuing evolution of game development. Through an ideal integration of data and task parallelisms in a well-balanced engine, developers can fully benefit from the capabilities of multicore processors, resulting in smoother and more responsive gaming experiences. The results obtained from employing both data and task parallelism jointly in the same architecture showed a remarkable performance increase of about 2.3 times faster than the single-threaded model. The paper also addresses common multithreading challenges, such as synchronization issues and load balancing, together with their solutions, emphasizing the effectiveness of the proposed model. Keywords: Multithreaded Game Engine, Multithreading, Game Engine Performance, Multithreading Principles, Data Parallelism, Task Parallelism, Task scheduler.

A new thread-level speculative automatic parallelization model and library based on duplicate code execution

Loop-efficient automatic parallelization has become increasingly relevant due to the growing number of cores in current processors and the programming effort needed to parallelize codes in these systems efficiently. However, automatic tools fail to extract all the available parallelism in irregular loops with indirections, race conditions or potential data dependency violations, among many other possible causes. One of the successful ways to automatically parallelize these loops is the use of speculative parallelization techniques. This paper presents a new model and the corresponding C++ library that supports the speculative automatic parallelization of loops in shared memory systems, seeking competitive performance and scalability while keeping user effort to a minimum. The primary speculative strategy consists of redundantly executing chunks of loop iterations in a duplicate fashion. Namely, each chunk is executed speculatively in parallel to obtain results as soon as possible and sequentially in a different thread to validate the speculative results. The implementation uses C++11 threads and it makes intensive use of templates and advanced multithreading techniques. An evaluation based on various benchmarks confirms that our proposal provides a competitive level of performance and scalability.

Accelerating Multiple Sequence Alignments Using Parallel Computing

Multiple sequence alignment (MSA) stands as a critical tool for understanding the evolutionary and functional relationships among biological sequences. Obtaining an exact solution for MSA, termed exact-MSA, is a significant challenge due to the combinatorial nature of the problem. Using the dynamic programming technique to solve MSA is recognized as a highly computationally complex algorithm. To cope with the computational demands of MSA, parallel computing offers the potential for significant speedup in MSA. In this study, we investigated the utilization of parallelization to solve the exact-MSA using three proposed novel approaches. In these approaches, we used multi-threading techniques to improve the performance of the dynamic programming algorithms in solving the exact-MSA. We developed and employed three parallel approaches, named diagonal traversing, blocking, and slicing, to improve MSA performance. The proposed method accelerated the exact-MSA algorithm by around 4×. The suggested approaches could be basic approaches to be combined with many existing techniques. These proposed approaches could serve as foundational elements, offering potential integration with existing techniques for comprehensive MSA enhancement.

A simple migrating birds optimization algorithm with two search modes to solve the no-wait job shop problem

The no-wait job shop problem has important applications in various industries such as steel-making and chemical processes, and its mainstream approaches are metaheuristics with timetabling methods. Inspired by the characteristics of the problem, we consider both left and right shifts for some jobs and present an effective timetabling method called left–right shift (LRS) method. The LRS method schedules jobs as far left as possible and tries all the possible right shifts for some jobs to expand the search space. We propose a simple migrating birds optimization (SMBO) algorithm for searching the space of job sequences. The population of the proposed algorithm is equally divided into two subpopulations with two search modes, one for the original instance and the other for the inverse instance. Local exploitation and global exploration are well balanced in the population evolution scheme of the proposed algorithm. To enhance the run efficiency of the algorithm, we introduce a speedup method for left timetabling and employ the simultaneous multithreading technique for the parallel evolution of subpopulations. Experimental results show that the proposed SMBO algorithm outperforms the state-of-the-art by a significant margin and obtains new upper bounds for a large number of benchmark instances.

Parallelization of Garfield++ and neBEM to simulate space-charge effects in RPCs

Numerical simulation of avalanches, saturated avalanches, and streamers can help us understand the detector physics of Resistive Plate Chambers (RPC). 3D Monte Carlo simulation of an avalanche inside an RPC, the transition from avalanche to saturated avalanche to streamer may help the search for the optimum voltage and alternate gas mixtures. This task is dauntingly resource hungry, especially when space-charge effects become important, which often coincides with important regimes of operation of these devices. By modifying the electric field inside the RPC dynamically, the space charge plays a crucial role in determining the response of the detector. In this work, a numerical model has been proposed to calculate the dynamic space-charge field inside an RPC and the same has been implemented in the Garfield++ framework. By modeling space charge as large number of line charges and using the multithreading technique OpenMP to calculate electric field, drift line, electron gain, and space-charge field, it has been possible to maintain time consumption within reasonable limits. For this purpose, a new class, pAvalancheMC has been introduced in Garfield++. The calculations have been successfully verified with those from existing solvers and an example is provided to show the performance of pAvalancheMC. Moreover, the details of the transition of an avalanche into a saturated avalanche have been discussed. The induced-charge distribution is calculated for a timing RPC and results are verified with the experiment.

3DT-CM: A Low-complexity Cross-matching Algorithm for Large Astronomical Catalogues Using 3d-tree Approach

Location-based cross-matching is a preprocessing step in astronomy that aims to identify records belonging to the same celestial body based on the angular distance formula. The traditional approach involves comparing each record in one catalog with every record in the other catalog, resulting in a one-to-one comparison with high computational complexity. To reduce the computational time, index partitioning methods are used to divide the sky into regions and perform local cross-matching. In addition, cross-matching algorithms have been adopted on high-performance architectures to improve their efficiency. But the index partitioning methods and computation architectures only increase the degree of parallelism, and cannot decrease the complexity of pairwise-based cross-matching algorithm itself. A better algorithm is needed to further improve the performance of cross-matching algorithm. In this paper, we propose a 3d-tree-based cross-matching algorithm that converts the angular distance formula into an equivalent 3d Euclidean distance and uses 3d-tree method to reduce the overall computational complexity and to avoid boundary issues. Furthermore, we demonstrate the superiority of the 3d-tree approach over the 2d-tree method and implement it using a multi-threading technique during both the construction and querying phases. We have experimentally evaluated the proposed 3d-tree cross-matching algorithm using publicly available catalog data. The results show that our algorithm applied on two 32-core CPUs achieves equivalent performance than previous experiments conducted on a six-node CPU-GPU cluster.

I/O-efficient GPU-based acceleration of coherent dedispersion for pulsar observation

Coherent dedispersion is a frequency domain filtering method extensively used in big data stream processing to achieve high time resolution and pulse-profile precision in pulsar observation. However, the computational complexity and challenges associated with processing large-scale data streams have hindered its widespread adoption in modern telescopes. Overcoming these hurdles is crucial to realizing wide-band real-time coherent dedispersion. In this paper, we propose a highly parallel and high-I/O throughput implementation of coherent dedispersion on GPU, specifically designed to support consecutive dispersion measure trials. Our primary objective is to achieve real-time coherent dedispersion, enabling precise high-time resolution and pulse-profile analysis in modern telescopes. To accomplish this, we leverage the power of GPU parallelism and employ advanced multi-threading techniques to optimize both computation and I/O throughput. Experiments show that our method achieves performance gains of 3.96x and 4.65x in processing time and IO throughput, respectively, when compared with the baseline work, the Coherent Dispersion Measure Trials (CDMT). Finally, the proposed techniques for improving computation parallelism and I/O efficiency are general that can provide valuable insights and serve as a reference for optimizing related algorithms in the field of big data stream processing.

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

Time series analysis acceleration with advanced vectorization extensions

Time series analysis is an important research topic and a key step in monitoring and predicting events in many fields. Recently, the Matrix Profile method, and particularly two of its Euclidean-distance-based implementations—SCRIMP and SCAMP—have become the state-of-the-art approaches in this field. Those algorithms bring the possibility of obtaining exact motifs and discords from a time series, which can be used to infer events, predict outcomes, detect anomalies and more. While matrix profile is embarrassingly parallelizable, we find that auto-vectorization techniques fail to fully exploit the SIMD capabilities of modern CPU architectures. In this paper, we develop custom-vectorized SCRIMP and SCAMP implementations based on AVX2 and AVX-512 extensions, which we combine with multithreading techniques aimed at exploiting the potential of the underneath architectures. Our experimental evaluation, conducted using real data, shows a performance improvement of more than 4times with respect to the auto-vectorization.

Hybrid Cryptography: Secure File Storage on using AES, RC6 and Blow Fish Algorithm

In this era, cloud computing is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data. Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer. But the major concern regarding storage of data online that is on the cloud is the Security. This Security concern can be solved using various ways, the most commonly used techniques are cryptography and steganography. But sometimes a single technique or algorithm alone cannot provide high-level security. So we have introduces a new security mechanism that uses a combination of multiple cryptographic algorithms of symmetric key and steganography. In this proposed system, RC6 (Rivest Cipher 6), AES (Advanced Encryption Standard) and Blowfish algorithms are used to provide security to data. All the algorithms use 128-bit keys. LSB steganography technique is used to securely store the key information. Key information will contain the information regarding the encrypted part of the file, the algorithm and the key for the algorithm. File during encryption is split into three parts. These individual parts of the file will be encrypted using different encryption algorithm simultaneously with the help of multithreading technique. The key information is inserted into an image using the LSB technique. Our methodology guarantees better security and protection of customer data by storing encrypted data on a single cloud server, using AES, RC6 and Blowfish algorithm.

NBLG: A Robust Legalizer for Mixed-Cell-Height Modern Design

With the increasing complexity of modern design, mixed-cell-height designs have become more popular, which makes the legalization problem more challenging. In this article, a robust negotiation-based legalizer (NBLG) is proposed to reduce average displacement and maximum displacement for mixed-cell-height circuits with considering the fence region and technology constraints. By dissecting the main components of the negotiation-based method, we divide the placement grid in terms of placement sites and reformulate the legalization problem as a resource allocation task. We then allow all movable cells to gradually remove overlaps in a surrounding window with two individual techniques: 1) isolation point and 2) adaptive penalty function. We also adopt a deterministic multithreading technique to accelerate the convergence of our algorithm. Experimental results show that our legalizer achieved the minimal average displacement and maximum displacement in a reasonable runtime compared with state-of-the-art methods.

Validation of Monte Carlo multithreading TOpas 3.6 software in order to simulate the 6 MV Elekta Synergy MLCi2 platform linac.

Monte Carlo simulation is generally appreciated as an extraordinary technique to investigate particle physics processes and interactions in nuclear medicine and Radiation Therapy. The present task validates a new methodology of Monte Carlo simulation based on the Multithreading technique to reduce CPU time to simulate a 6 MV photon beam provided by the Elekta Synergy MLCi2 platform medical linear accelerator treatment head utilizing TOpas version 3.6 Monte Carlo software and the Slurm Marwan cluster. The simulation includes the linear accelerator (LINAC) major components. Calculations are performed for the photon beam with several treatment field sizes varying from 3 × 3 to 10 × 10 cm2 at a 100 cm of distance from the source to the surface of the IBA dosimetry water box. The simulation was wholly approved by comparison with experimental distributions. To evaluate simulation accuracy, gamma index formalism for (2%/2mm) and (3%/2mm) criteria, Distance To Agreement DTA, and the estimator standard error ɛ and ɛ max are used. Good agreement between simulations and measurements was observed for depth doses and lateral dose profiles, respectively. The gamma index comparisons also highlighted this agreement; more than 97% of the points for all simulations satisfy the quality assurance criteria of (2%/2mm). Regarding calculation performance, the event processing speed is faster using Gate-[mp] compared to TOpas-[mt] mode when running the identical simulation code for both. Consequently, according to the achieved results, the proposed methodology shows the first validation of TOpas in radiotherapy linacs simulations and a reduction in calculation time, capping simulation accuracy as much as possible. For this reason, this software is recommended to be serviceable for Treatment Planning Systems (TPS) purposes.

A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices.

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages.

Secure File Storage on Cloud Using Cryptography

Abstract: Hacking became a serious drawback lately. Transference of secure knowledge or communication through the web turns out to be difficult because of security considerations. to anticipate these security hurdles, we tend to use Cryptography, and Image Steganography. Day’s cloud computing is currently employed in several areas like business, colleges, and Universities to store a great amount of knowledge. We will extract knowledge from the cloud for the asking of users. To store knowledge on the cloud we've to face several errors and issues. Cryptography and steganography techniques area unit well-liked currently a day’s for knowledge security. using one algorithmic rule isn't effective for prime-level security to knowledge in cloud computing. during this paper, we initiated a new security mechanism using symmetrical key cryptography algorithmic rules and steganography. during this projected system AES, blowfish, RC6, and 3DES algorithms area units were used to supply blockwise security to knowledge. All algorithms have a key size of 128 bits. Key data contains that a part of the file is encrypted using that algorithmic rule and key. The file is split into eight components. every part of the file is encrypted using different algorithmic rules. All components of the file area unit are encrypted at the same time with the assistance of the multithreading technique. encoding Keys area unit inserted into cowl image using LSB technique. Steganography image is sent to a valid receiver using email. For file secret writing purposes reverse method of cryptography is applied. Keywords-Hacking, Steganography, Cryptography, Cloud service provider (CSP), cloud server (CS), Encode, Decode, Delay, Integrity

Statistical terrain model with geometric feature detection based on GPU using LiDAR on vehicles

Digital terrain models based on light detection and ranging (LiDAR) are widely used in ground robots to achieve autonomous navigation. Very few studies have been conducted on terrain modelling for preview information, which acts as an input for a vehicle’s active suspension system. In this study, we proposed an algorithm to construct a real-time terrain model from multi-frame LiDAR points according to the applications for preview control. The terrain model consisted of an elevation map with confidence intervals and feature detection. First, the statistical confidence interval of the elevation map represented the reliability of the mean height. Further, we used spatial signal processing techniques to detect the uneven geometric features of the terrain. The key point feature on the trajectory was detected by the Laplacian of Gaussian method. Finally, the raw point cloud data processing procedure was based on parallel multi-thread techniques combined with GPU programming, which significantly promotes computation efficiency to enable real-time implementation. Thus, the proposed terrain model integrates reliability issues and geometric features, which act as inputs for preview controllers or motion planning. Experimental results proved that our method has a competitive performance (in terms of precision and efficiency) compared with the state-of-the-art methods, and runs in real-time at 50 Hz.

Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

Improved Software Implementation for Montgomery Elliptic Curve Cryptosystem

The last decade witnessed rapid increase in multimedia and other applications that require transmitting and protecting huge amount of data streams simultaneously. For such applications, a high-performance cryptosystem is compulsory to provide necessary security services. Elliptic curve cryptosystem (ECC) has been introduced as a considerable option. However, the usual sequential implementation of ECC and the standard elliptic curve (EC) form cannot achieve required performance level. Moreover, the widely used Hardware implementation of ECC is costly option and may be not affordable. This research aims to develop a high-performance parallel software implementation for ECC. To achieve this, many experiments were performed to examine several factors affecting ECC performance including the projective coordinates, the scalar multiplication algorithm, the elliptic curve (EC) form, and the parallel implementation. The ECC performance was analyzed using the different factors to tune-up them and select the best choices to increase the speed of the cryptosystem. Experimental results illustrated that parallel Montgomery ECC implementation using homogenous projection achieves the highest performance level, since it scored the shortest time delay for ECC computations. In addition, results showed that NAF algorithm consumes less time to perform encryption and scalar multiplication operations in comparison with Montgomery ladder and binary methods. Java multi-threading technique was adopted to implement ECC computations in parallel. The proposed multithreaded Montgomery ECC implementation significantly improves the performance level compared to previously presented parallel and sequential implementations.