Parallel Processing Research Articles

Distributed Aerial Image Stitching on Multiple Processors using Message Passing Interface

This study investigates the potential of using Message Passing Interface (MPI) parallelization to enhance the speed of the image stitching process. The image stitching process involves combining multiple images to create a seamless panoramic view. This research explores the potential benefits of segmenting photos into distributed tasks among several identical processor nodes to expedite the stitching process. However, it is crucial to consider that increasing the number of nodes may introduce a trade-off between the speed and quality of the stitching process. The initial experiments were conducted without MPI, resulting in a stitching time of 1506.63 seconds. Subsequently, the researchers employed MPI parallelization on two computer nodes, which reduced the stitching time to 624 seconds. Further improvement was observed when four computer nodes were used, resulting in a stitching time of 346.8 seconds. These findings highlight the potential benefits of MPI parallelization for image stitching tasks. The reduced stitching time achieved through parallelization demonstrates the ability to accelerate the overall stitching process. However, it is essential to carefully consider the trade-off between speed and quality when determining the optimal number of nodes to employ. By effectively distributing the workload across multiple nodes, researchers and practitioners can take advantage of the parallel processing capabilities offered by MPI to expedite image stitching tasks. Future studies could explore additional optimization techniques and evaluate the impact on speed and quality to achieve an optimal balance in real-world applications.

Navigating the Terrain: Scaling Challenges and Opportunities in AI/ML Infrastructure

Navigating the complexities of scaling AI/ML infrastructure unveils a terrain rife with challenges and opportunities. This exploration delves into the multifaceted landscape, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, and security. By embracing innovation and resilience, organizations can effectively harness the potential of AI and ML technologies while mitigating scalability hurdles.

Navigating the Terrain: Scaling Challenges and Opportunities in AI/ML Infrastructure

Navigating the complexities of scaling AI/ML infrastructure unveils a terrain rife with challenges and opportunities. This exploration delves into the multifaceted landscape, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, and security. By embracing innovation and resilience, organizations can effectively harness the potential of AI and ML technologies while mitigating scalability hurdles.

THE POSSIBILITIES OF USING PARALLEL PROGRAMMING USING PYTHON

This article explores the development of parallel software applications using the Python programming language. Parallel programming is becoming increasingly important in the information technology world as multi-core processors and distributed computing become more common. Python provides developers with a variety of tools and libraries for creating parallel applications, including threads, processes, and asynchronous programming. This topic covers the basics of parallel programming in Python, including the principles of thread and process management, error handling, synchronization mechanisms, and resource management. He also considers asynchronous programming using the asyncio library, which allows you to efficiently handle asynchronous tasks. In addition, this topic raises issues of optimization and profiling of parallel applications, as well as explores distributed parallel programming using third-party libraries and frameworks. He also emphasizes the importance of testing and debugging in the context of parallel programming. Research and experiments in parallel programming using Python help developers create high-performance and efficient applications that can effectively use multi-core systems and distributed computing. This article offers an in-depth study that examines how Python is suitable for teaching parallel programming to inexperienced students. The results show that there are obstacles that prevent Python from maintaining its advantages in the transition from sequential programming to parallel.

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.

Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707043 cells and 1154611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.

Power Optimization in Wireless Sensor Network Using VLSI Technique on FPGA Platform

Nowadays, the demand for high-performance wireless sensor networks (WSN) is increasing, and its power requirement has threatened the survival of WSN. The routing methods cannot optimize power consumption. To improve the power consumption, VLSI based power optimization technology is proposed in this article. Different elements in WSN, such as sensor nodes, modulation schemes, and package data transmission, influence energy usage. Following a WSN power study, it was discovered that lowering the energy usage of sensor networks is critical in WSN. In this manuscript, a power optimization model for wireless sensor networks (POM-WSN) is proposed. The proposed system shows how to build and execute a power-saving strategy for WSNs using a customized collaborative unit with parallel processing capabilities on FPGA (Field Programmable Gate Array) and a smart power component. The customizable cooperation unit focuses on applying specialized hardware to customize Operating System speed and transfer it to a soft intel core. This device decreases the OS (Operating System) central processing unit (CPU) overhead associated with installing processor-based IoT (Internet of Things) devices. The smart power unit controls the soft CPU’s clock and physical peripherals, putting them in the right state depending on the hardware requirements of the program (tasks) being executed. Furthermore, by taking the command signal from a collaborative custom unit, it is necessary to adjust the amplitude and current. The efficiency and energy usage of the FPGA-based energy saver approach for sensor nodes are compared to the energy usage of processor-based WSN nodes implementations. Using FPGA programmable architecture, the research seeks to build effective power-saving approaches for WSNs.

Assigning and Scheduling Generalized Malleable Jobs Under Subadditive or Submodular Processing Speeds

Handling Heterogeneous Machines in Malleable Scheduling Parallelization is an important and widespread technique to speed up the completion of time-critical tasks, not only in high-speed computing, but also in operations planning in production and logistics. A fundamental model in this context is that of malleable jobs, each of which can be assigned to a subset of machine for parallel processing. In “Assigning and Scheduling Generalized Malleable Jobs Under Submodular or Subadditive Processing Speeds,” Fotakis, Matuschke, and Papadigenopoulos go beyond the by now well-understood identical-machine setting in malleable scheduling and develop algorithmic approaches for scheduling malleable jobs under various discrete concavity assumptions on the joint processing speeds of the assigned (possibly very heterogeneous) machines. They show that under these assumptions, the task of finding a schedule of small makespan can be reduced to that of finding an assignment with small maximum machine load. For this latter problem, numerous efficient approximation algorithms are derived and their practical performance explored in a computational experiments. These results indicate that the computational challenges posed by parallelization in heterogeneous environments can indeed be overcome, enabling the optimization of heavily parallelized schedules in the aforementioned applications.

ACCELERATING ORE SINTERING MATHEMATICAL MODEL USING GPU

The study aims to enhance the efficiency and computational speed of the ore sintering model through the utilization of graphics processing units (GPUs). The purpose of this research is to address the growing demand for faster and more scalable simulations in the field of ore sintering, a crucial process in the production of iron and steel. Methodology involves the integration of parallel computing capabilities offered by GPUs into the existing ore sintering model. By leveraging the parallel processing power of GPUs, the computational workload is distributed across multiple cores, significantly reducing the simulation time. Results demonstrate a substantial acceleration in the ore sintering simulation process. Comparative analyses between CPU and GPU implementations reveal a remarkable reduction in computation time, thereby enabling real-time or near-real-time simulations. The achieved speedup not only enhances the efficiency of ore sintering modeling but also opens avenues for exploring larger and more complex scenarios. This is the successful integration of GPU parallel computing into the ore sintering model, showcasing the adaptability of advanced computational technologies to traditional industrial processes. The study contributes to the field by bridging the gap between computational power and metallurgical simulations, demonstrating the potential for GPU acceleration in other areas of metallurgical processes. Practical significance of this research is underscored by its potential to revolutionize the ore sintering industry. Faster simulations facilitate quicker decision-making in process optimization, leading to improved energy efficiency and reduced environmental impact. This research sets the stage for the broader adoption of GPU acceleration in metallurgical modeling, signaling a paradigm shift towards more efficient and sustainable industrial practices

Healthcare provider Communication and Data-Informed Message Strategies for Encouraging Older Adults to Stay up to Date on COVID-19 Vaccination.

Older adults are a high in risk of severe COVID-19 disease outcomes and experience "vaccine hesitancy." Guided by health communication models such as the Reasoned Action Approach and the Extended Parallel Process Model, online surveys designed to inform statewide public health communication efforts were administered in West Virginia, a primarily rural and fully Appalachian state, in Winter 2021 (n = 272) and Fall 2022 (n = 248). Adults ages 50+ years (N = 520) reported attitudes, intentions, and behaviors regarding COVID-19 vaccination and their related patient-provider communication. Vaccine usefulness/safety, perceived effectiveness, and subjective norms predicted behavior while perceived severity, efficacy, and usefulness/safety predicted intention to get a booster, specifically the updated Fall 2022 Omicron (bivalent) booster. As patient-provider communication (which a minority reported receiving) was a strong predictor of behavioral outcomes, characteristics of effective healthcare provider communication and considerations for public message construction are discussed.

Techniques to alleviate blockchain bloat: Potentials, challenges, and recommendations

Blockchain (BC) bloat, characterized by excessive data growth on BC networks, presents a pressing challenge. The need for a comprehensive study addressing BC bloat solutions arises due to the scarcity of knowledge in this emerging area. This study advances knowledge along with practical insights and inspires innovative approaches to address BC bloat. This research explores various techniques proposed to combat BC bloat. Two technique themes are identified: on-chain and off-chain techniques. On-chain techniques focus on updating the consensus mechanism, block size, or database partition. This study identified five on-chain techniques: structural modification, pruning, sharding, ephemeral BC, and zk-SNARKs. Off-chain techniques focus on processing in parallel, bundling transactions, or handling transactions off-chain. Further, three off-chain techniques identified are off-chain storage, historical data storage, and light clients’ techniques. These recommendations aim to optimize resources use, enhance scalability, and maintain data integrity. However, these may compromise several BC capabilities, such as security, privacy, and data loss. The applicability of these techniques for resource-constrained devices is also explored. Future research should include further investigation into the performance and applicability of these techniques, especially in resource-constrained environments like IoT and fog computing.

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware.

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

Setting the context for a complex dental intervention of role substitution in care homes: Initial process evaluation findings.

SENIOR (uSing rolE-substitutioN In care homes to improve oRal health) is a randomised controlled trial designed to determine whether role substitution could improve oral health for this population. A parallel process evaluation was undertaken to understand context. This paper reports on the first phase of the process evaluation. The oral health and quality-of-life of older adults residing in care homes is poorer than those in the community. Oral health care provision is often unavailable and a concern and challenge for managers. The use of Dental Therapists and Dental Nurses rather than dentists could potentially meet these needs. Semi-structured interviews were conducted with 21 key stakeholders who either worked or had experience of dependent care settings. Questions were theoretically informed by the: Promoting Action on Research Implementation in Health Services (PAHRIS) framework. The focus was on contextual factors that could influence adoption in practice and the pathway-to-impact. Interviews were fully transcribed and analysed thematically. Three themes (receptive context, culture, and leadership) and 11 codes were generated. Data show the complexity of the setting and contextual factors that may work as barriers and facilitators to intervention delivery. Managers are aware of the issues regarding oral health and seek to provide best care, but face many challenges including staff turnover, time pressures, competing needs, access to services, and financial constraints. Dental professionals recognise the need for improvement and view role substitution as a viable alternative to current practice. Although role substitution could potentially meet the needs of this population, an in-depth understanding of contextual factors appeared important in understanding intervention delivery and implementation.

1st International Conference on Security, Parallel Processing, Image Processing and Networking

1st International Conference on Security, Parallel Processing, Image Processing and Networking

High-speed laser centerline extraction and heterogeneous measurement architectures for low-cost hardware

Due to its real-time efficiency, stability, and reliability, structured light 3D measurement is widely used in measuring surface quality and manufacturing accuracy of industrial products. With the structured light measurement system's slow acquisition speed and low efficiency, it is difficult to meet the needs of online real-time detection, resulting in sparse 3D contour data and loss of details. To improve the algorithm's running speed and balance hardware resource consumption, a structured light extraction algorithm adapted to hardware acceleration is proposed, which executes the three low-coupling computational modules at eight-pixel parallel speed and in a pipelined manner. In order to further reduce the overall computational efficiency and deployment cost of high-speed structured light measurement tasks, an FPGA camera architecture with parallel acquisition and processing is designed, and the FPGA camera is cooperated with the CPU to form a more efficient heterogeneous measurement system. For samples of different materials and shapes, the standard deviation of sub-pixel coordinates extracted is less than 0.65. With this architecture, it is possible to process video stream with one million pixels at 500FPS, and the power consumption is 2.566w. Experimental results of measuring blocks and steel pipes show that the measurement error is below 0.15 mm. The proposed algorithm and heterogeneous processing measurement architecture are applicable in engineering, especially for high-speed real-time 3D measurement.

Co-developmental trajectories of suicidal ideation and non-suicidal self-injury among Chinese adolescents: Transdiagnostic predictors and association with suicide attempts.

Adolescent suicidal ideation (SI) and non-suicidal self-injury (NSSI) are crucial public health issues, yet their co-developmental trajectories during early adolescence and their associations with predictors and outcomes are unclear. This study aimed to (a) identify heterogeneous co-developmental trajectories of SI and NSSI, (b) explore associations between transdiagnostic predictors and trajectories, and (c) assess suicide attempt risk across trajectories. Four hundred fifty-three adolescents (Mage = 12.35 years, 48.3% boys) completed surveys at 6-month intervals across 2 years. At Time 1 (Nov 2020), participants completed surveys encompassing SI, and NSSI, along with family, peer, and individual predictors. Subsequent surveys (Times 2-4) measured SI and NSSI, with suicide attempts queried at Time 4. Parallel process latent class growth models revealed three co-developmental groups (i.e., Stable low NSSI and SI; Moderate-NSSI and high-SI, parallel decreasing; High-NSSI and moderate-SI, parallel increasing). Multivariate logistic regression indicated that group membership was predicted by parental rejection, parental warmth, bullying victimization, depressive and anxiety symptoms, thwarted belongingness, and perceived burdensomeness. Adolescents in the "High-NSSI and moderate-SI, parallel increasing" group reported the highest suicide attempt frequency. These findings underscore subgroup distinctions and transdiagnostic predictors in comprehending SI and NSSI progression, emphasizing the necessity of dynamic monitoring and tailored interventions for distinct subgroup characteristics.

Implementing a Hybrid Method for Shack–Hartmann Wavefront Spots Labeling on FPGA

This paper presents a real-time implementation of a hybrid connected component labeling method for processing the Shack–Hartmann wavefront sensor’s images for an adaptive optics (AO) system. The output image of a wavefront sensor is an image of spots. During the sensor’s operation, it can happen that highly distorted wavefronts (WF) may cause the spots to shift outside of their sub-aperture, which may lead to the reduction of the AO system performance. This article explains the benefits of high-performance computing and parallel processing of a field programmable gate array (FPGA). The objective is to calculate the centroids of these spots. A hybrid labeling method was investigated to fulfill this purpose. First, this method was implemented using a forward and backward scan with a respective mask for each scan. Additionally, a relabeling process is applied after labeling each line, and it is carried out in both directions. After labeling, several processing units were implemented in parallel to calculate centroids. Each unit is responsible for calculating the centroid of one label. The system runs in real time with a latency of one frame, which means the output image is a fusion of a current frame and the centroids of the previous frame. Forward and backward labeling requires a large amount of memory, which is the reason for limiting the investigation to forward labeling only. The forward labeling was successfully implemented, and the centroids were detected under minimum spot distortion conditions. This forward labeling implementation also runs in real time with significant latency reduction to calculate the centroids, which leads to minimizing the overall AO system latency, enabling faster computation and correction in addition to reducing the memory usage to 1% when compared to the forward and backward labeling usage of 81% as an advantage for the hardware implementation.

Analysis and prospects of automobile intelligent assisted driving characteristics based on FPGA technology

This article provides a comprehensive exploration of the pivotal role that field-programmable gate arrays (FPGAs) play in the advancement of autonomous driving technology. FPGAs, which made their debut in the early 1980s, have emerged as a crucial component in this field, owing to their robust parallel processing capabilities, real-time data analysis capabilities, and exceptional customizability. With the ever-increasing demand for autonomous driving solutions, the adoption of FPGAs has become indispensable in meeting the requirements for high-speed data processing and instantaneous response times. Within these pages, we delve into the significant role that FPGAs assume in elevating intelligent driving systems, offering a deep dive into this subject through meticulous case studies and technical insights. This article casts a spotlight on several compelling instances where FPGAs shine, notably in adaptive cruise systems, obstacle recognition, automatic emergency braking, and intelligent parking assistance, achieved through seamless integration with YOLO technology. These real-world examples serve to underscore the pivotal role that FPGAs play in ensuring road safety and propelling technological advancement in the realm of autonomous driving.

Incorporating Serverless Computing into P2P Networks for ML Training: In-Database Tasks and Their Scalability Implications (Student Abstract)

Distributed ML addresses challenges from increasing data and model complexities. Peer to peer (P2P) networks in distributed ML offer scalability and fault tolerance. However, they also encounter challenges related to resource consumption, and communication overhead as the number of participating peers grows. This research introduces a novel architecture that combines serverless computing with P2P networks for distributed training. Serverless computing enhances this model with parallel processing and cost effective scalability, suitable for resource-intensive tasks. Preliminary results show that peers can offload expensive computational tasks to serverless platforms. However, their inherent statelessness necessitates strong communication methods, suggesting a pivotal role for databases. To this end, we have enhanced an in memory database to support ML training tasks.

BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains significant improvement gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed.