Multiple Threads Research Articles

Morphodynamics and depositional architecture of mid‐channel bars in large Amazonian rivers

AbstractLarge rivers are characterized by large water discharges, high suspended sediment fluxes and low slope, and typically display multiple channels that are separated by large complex bars. The most common channel style found in these rivers is characterized by the alternating presence of single and multiple channel threads. Mid‐channel compound bars separate a main deeper channel from shallower secondary channels, leading to low‐order braiding and low to moderate sinuosity. Despite the importance of this type of river for global terrestrial sediment transport and the interpretation of ancient fluvial successions, integrated depositional models for large multi‐channel rivers are still not fully developed. This paper interprets the channel morphodynamics and depositional architecture of such large rivers by investigating the distinctive features of their laterally‐accreting mid‐channel bars in the Solimões–Amazonas River. This is achieved by examination of temporal series of satellite images, quantification of bedforms using multibeam echosounding surveys, analysis of shallow seismic and ground penetrating radar surveys of selected areas, and on‐site field observations and sample collections. Such mid‐channel bars produce directional variability in planform scroll bar accretion of up to 180°. As these bars – and their associated channels – evolve, they become progressively shallower, due to the bifurcation of the two channels and the resultant partitioning of discharge that leads to a gradual reduction in transport capacity within the outer channel. The preserved successions of this process in the alluvial plain are characterized by fining‐upward trends, with larger cross‐strata sets at mid‐depths and internal erosional surfaces marking distinct cycles of bar development with potentially differing palaeocurrent trends. The new depositional models proposed for these mid‐channel bars and channels constitute a basis for the recognition of large multi‐channel river deposits in the ancient rock record.

Assessment Automation of Complex Student Programming Assignments

Grading student programming assignments is not an easy task. This task is even more challenging when talking about complex programming assignments at university graduate level. By complex assignments, we mean assignments where students have to program a complete application from scratch. For example, building a complete web application with a client and server side, whereby the application uses multiple threads that gather data from some external service (like the REST service, IoT sensors, etc.), processes these data and store them in some storage (e.g., a database), implements a custom protocol over a socket or something similar, implements their own REST/SOAP/GraphQL service, then sends or receives JMS/MQTT/WebSocket messages, etc. Such assignments give students an inside view of building real Internet applications. On the other hand, assignments like these take a long time to be tested and graded manually, e.g., up to 1 h per student. To speed up the assessment process, there are different automation possibilities that can check for the correctness of some application parts without endangering the grading quality. In this study, different possibilities of automation are described that have been improved over several years. This process takes advantage of unit testing, bash scripting, and other methods. The main goal of this study is to define an assessment process that can be used to grade complex programming assignments, with concrete examples of what and how to automate. This process involves assignment preparation for automation, plagiarism (i.e., better said similarity) detection, performing an automatic check of the correctness of each programming assignment, conducting an analysis of the obtained data, the awarding of points (grading) for each programming assignment, and other such activities. We also discuss what the downsides of automation are and why it is not possible to completely automate the grading process.

Multicore workflow characterisation methodology for payloads running in the ALICE Grid

Multicore workflow characterisation methodology for payloads running in the ALICE Grid

Reframing the police staffing challenge: A systems approach to workforce planning and managing workload demand

Abstract Policing agencies in the United States and elsewhere continue to face staff shortages. Calls for greater diversity and different skills among police officers further complicate staffing efforts. Attempts to address these issues typically focus on recruitment and retention. This oversimplifies the problems and leads to piecemeal solutions. We draw on multiple threads of staffing research and field experience to highlight lessons for building effective police workforces. We illustrate the interconnected nature of staffing allocation, workload demand, performance objectives, staff supply and demand, workforce attributes, cohort management, demographic and generational shifts, and systemic circumstances. We suggest that agencies consider staffing from a systems perspective. We illustrate the elements of a police staffing system, discuss the broader ecosystem police staffing must consider, and present a six-step approach for managing police staffing and workload demand. Our lessons should be of interest to all who must address police staffing challenges and community expectations.

The Generator Matrix, the Expanded Embryo, and the Multiple Dramaturgical Threads in Pedro Almodóvar

Este artigo investiga os processos e os procedimentos de criação do cineasta espanhol Pedro Almodóvar, refletindo sobre as tessituras dramatúrgicas criadas pelo cineasta no processo de construção do seu projeto poético, observando os procedimentos que envolvem o seu pensamento e a sua prática como roteirista e diretor para, assim, refletir sobre processos criativos, de autoria e comunicação. A hipótese é que o seu projeto poético é construído através da seleção e transformação de elementos temáticos e estéticos presentes na sua própria obra e na obra de outros autores, cujos trabalhos lhe afetaram a sensibilidade, fazendo com que temas e personagens se repitam, sob diferentes perspectivas, ao longo do tempo, concebendo um universo marcado pela constante reinvenção de textos e filmes para criar seus próprios sistemas fílmicos. Este estudo foi feito utilizando entrevistas do diretor e a análise de alguns de seus filmes que mantêm uma relação dramatúrgica, que funcionam como matrizes geradoras, deflagradoras de novos processos de criação. A análise foi realizada a partir das teorias de Cecilia Almeida Salles sobre processo de criação.

Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning.

Behavioral Modification of Leucauge mariana Induced by an Ichneumonid Spider-Parasitoid, Hymenoepimecis castilloi, in the Colombian Andes

Hymenoepimecis is a genus of Darwin wasps in the Polysphincta group of genera (Hymenoptera: Ichneumonidae: Pimplinae) known as ectoparasitoids of a broad spectrum of spiders. The parasitoid induces production of a web known as cocoon web, which provides shelter and support for the wasp pupa. In this study, we describe for the first time the interaction between Hymenoepimecis castilloi Pádua & Sääksjärvi (Hymenoptera: Ichneumonidae) and its host spider Leucauge mariana (Taczanowski) (Araneae: Tetragnathidae) in the Colombian Andes, provide new records of wasp genus distribution, and described the behavioral modifications induced in the spider. Web modifications occurred in the webs of both solitary and aggregated individuals. Adhesive spirals were lacking, and webs were connected to vegetation by multiple threads in all cocoon webs, which was not seen attached to webs of non-parasitized spiders. All parasitoid cocoons were observed hanging on a vertical line in the hub of the cocoon web. As previously described for other species, we believe that this modified web design results in increased web strength and favors parasitoid development during the pupal stage.

Dynamic Tunable Chiral Plasmonic Properties via Self‐Assembly on Helical Threads

AbstractTailorable chiral plasmonic nanostructures have attracted great interest due to their potential applications in photonics and sensing. However, there are limited straightforward fabrication routes to modulate the morphology of chiral materials with an external stimulus. Here, a helical assembly of gold nanospheres (AuNSs) is successfully obtained via self‐assembly using glycopeptide helical nanofibers as templates based on electrostatic interactions. The assembly morphology can be readily tailored from short debris to single‐/double‐ and multiple helical threads by changing pHs, resulting in well‐controlled modulation of circular dichroism (CD) in the visible range. Further, this tunable assembly and corresponding chiroptical properties are fully reversible in the pH range from 6 to 10. The electromagnetic simulation revealed how the structural geometries of AuNS helices changed the electromagnetic field propagation pattern to facilitate corresponding chiral optical properties. This approach is a unique and facilely obtained example of chiral nanomaterials with in situ tailoring morphology and chiral properties through external stimulus, demonstrating a potential application for biosensing.

A Parallel Algorithm to Parse STEP Files Based on Multiple Threads

A Parallel Algorithm to Parse STEP Files Based on Multiple Threads

Write Buffer Partitioning with Rename Register Capping in Multithreaded Cores

In simultaneous multithreaded systems, there are several pipeline resources that are shared amongst multiple threads concurrently. Some of these mutual resources to mention are the register-file and the write buffer. The Physical Register file is a critical shared resource in these types of systems due to the limited number of rename registers available for renaming. The write buffer, another shared resource, is also crucial since it serves as an intermediary between the retirement of a store instruction and the writing of its value to cache. Both components, if not configured accurately, can serve as a bottleneck in inefficient usage of the resources and output undesirable performance. However, when configuring both shared components concurrently, there is potential to all eviate common performance congestion. This paper shows that when implementing a static register capping algorithm (limiting the number of physical register entries for each thread), there is a byproduct of increased variety in source for the write buffer. This also presents an opportunity for the write buffer to have a higher variety to potentially choose for a better suitable thread asit’s source at certain clock cycles. With this presented opportunity, this paper proposes a technique to allow the write buffer to both prioritize and enforce the choice for low-latency threads by partitioning the write buffer in two sections; cache-hit priority and cachehit only partitions, showing that system performance and resource efficiency can be further improved by using this technique in a modified SMT environment.

A scheduling algorithm based on critical factors for heterogeneous multicore processors

SummaryAs the development of chip manufacturing technology slows down, high‐performance processors often have high energy consumption and high heat generation. Therefore, heterogeneous multi‐core processors become more and more popular, and the heterogeneous multi‐core processors is adopted to execute programs. At present, the general program consists of multiple threads. To reach goals of accelerating program execution and reducing energy consumption and heat generation of system, a suitable thread scheduling algorithm for heterogeneous multi‐core processors is needed. In this article, a thread scheduling algorithm based on multiple critical scheduling factors is proposed. First, a prediction model of thread performance and energy consumption is used to predict the core sensitivity of threads. Then, critical threads are judged and accelerated by collecting the synchronization information between threads. Finally, the load balancing method based on the computing power of cores and the core sensitivity of threads is employed to perform system load balancing, which ensures the fairness of the scheduling. Several experiments are provided, and the results show that the proposed algorithm can obtain better performance of thread schedule.

Multi-Threaded Data Communication in Java for Advanced Computing Environments

The performance of operating systems like computers requires the proper functioning of the computer language interpreter. This interpreter follows various types of programming languages that make the performing of computer programming easy and effective. The application of the programming language of Java helps in processing multiple tasks at once. This research analyzes the novelty of Java-based data communication models in advanced computing services. This performance makes the saving of the resources used for the development of the programming language. All these development includes the performing of the multiple threads communication data processing. These multiple threads help distribute the single processed input in the multi-channel language processing, thus helping the work competition in time. It also reduces the cost of maintaining the programming languages reduced. Thus, the implementing cost of resources required for programming performance is reduced. Therefore, this implementation impacts the programmer to become more indented to use the language transformation process of Java. Moreover, it creates a more effective representation of the audio or visual content represented by a multi-tasking operating system. In this process of development in the language transformation of the operating system, the ability of the operating system for data processing improves. The systematic process of this language transformation helps in systematically transforming multiple programs at once.

ChatGPT: Revolutionizing User Interactions with Advanced Natural Language Processing

Significant advancements in artificial intelligence (AI) have unexpectedly improved the standard of living globally. One recent development garnering attention is ChatGPT, a natural language processing (NLP) model created by OpenAI. ChatGPT combines OpenAI's GPT-2 language model with supervised and reinforcement learning techniques, leveraging the extensive language patterns in the GPT-3 corpus. This enables natural text-based interactions between users and AI systems, making it suitable for customer service applications and the creation of voice and text-based virtual assistants. ChatGPT offers features such as topic detection, sentiment detection, sentiment analysis, and the ability to generate multiple threads, enhancing users' understanding and enabling realistic interactions. This paper explores the challenges in AI development and proposed strategies to overcome them. It further examines how ChatGPT can enhance sectors such as chat e-commerce, education, entertainment, finance, health, news, and productivity, highlighting current use cases and potential future applications. The article emphasizes ChatGPT's potential to generate personalized content, making it a promising technology for improving user experiences and advancing AI-driven interactions.

Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data

Summary Previous implementation of the distributed Gauss-Newton (DGN) optimization algorithm ran multiple optimization threads in parallel, employing a synchronous running mode (S-DGN). As a result, it waits for all simulations submitted in each iteration to complete, which may significantly degrade performance because a few simulations may run much longer than others, especially for time-consuming real-field cases. To overcome this limitation and thus improve the DGN optimizer’s execution, we propose two asynchronous DGN (A-DGN) optimization algorithms in this paper. The two A-DGN optimization algorithms are (1) the local-search algorithm (A-DGN-LS) to locate multiple maximum a-posteriori (MAP) estimates and (2) the integrated global-search algorithm with the randomized maximum likelihood (RML) method (A-DGN + RML) to generate hundreds of RML samples in parallel for uncertainty quantification. We propose using batch together with a checking time interval to control the optimization process. The A-DGN optimizers check the status of all running simulations after every checking time frame. The iteration index of each optimization thread is updated dynamically according to its simulation status. Thus, different optimization threads may have different iteration indices in the same batch. A new simulation case is proposed immediately once the simulation of an optimization thread is completed, without waiting for the completion of other simulations. We modified the training data set updating algorithm using each thread’s dynamically updated iteration index to implement the asynchronous running mode. We apply the modified QR decomposition method to estimate the sensitivity matrix at the best solution of each optimization thread by linear interpolation of all or a subset of the training data to avoid the issue of solving a linear system with a singular matrix because of insufficient training data points in early batches. A new simulation case (or search point) is generated by solving the Gauss-Newton (GN) trust-region subproblem (GNTRS) using the estimated sensitivity matrix. We developed a more efficient and robust GNTRS solver using eigenvalue decomposition (EVD). The proposed A-DGN optimization methods are tested and validated on a 2D analytical toy problem and a synthetic history-matching problem and then applied to a real-field deepwater reservoir model. Numerical tests confirm that the proposed A-DGN optimization methods can converge to solutions with matching quality comparable to those obtained by the S-DGN optimizers, saving on the time required for the optimizer to converge by a factor ranging from 1.3 to 2 when compared to the S-DGN optimizer depending on the problem. The new A-DGN optimization algorithms improve efficiency and robustness in solving history-matching or inversion problems, especially for uncertainty quantification of subsurface model parameters and production forecasts of real-field reservoirs by conditioning production data.

Movement fracturing “the international” —or, what does it mean to give primacy to movement?

Movement fracturing “the international” —or, what does it mean to give primacy to movement?

Towards a ROS2-based software architecture for service robots

This paper presents a scalable software architecture based on robot operating system 2 (ROS2) for service robots. ROS2 supports the data distribution service (DDS) protocol that provides benefits such as real-time operation and security and performance enhancements. However, ROS2 still lacks task management capabilities, essential for practical robotic applications consisting of multiple threads and processes. Moreover, integrating new devices into ROS2 requires additional development effort to create specific drivers for specific devices. The proposed software architecture addresses these drawbacks and provides a simple and user-friendly programming interface for easier integrating of various devices and existing ROS2 applications. Moreover, it is designed using python with multi-processing to avoid issues related to the python global interrupt lock (GIL). To verify the developed software architecture, an application for a custom-made service robot called the SeoulTech service robot (SSR) is implemented on a Jetson Xavier NX board with various features, such as ROS2 navigation and SLAM, text-to-speech (TTS), speech recognition, and face recognition.

Reference-based genome compression using the longest matched substrings with parallelization consideration

BackgroundA large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms.ResultsWe proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs.ConclusionsExperiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.

Analysis of Multi-Threading and Cache Memory Latency Masking on Processor Performance Using Thread Synchronization Technique

Multithreading is a process in which a single processor executes multiple threads concurrently. This enables the processor to divide tasks into separate threads and run them simultaneously, thereby increasing the utilization of available system resources and enhancing performance. When multiple threads share an object and one or more of them modify it, unpredictable outcomes may occur. Threads that exhibit poor locality of memory reference, such as database applications, often experience delays while waiting for a response from the memory hierarchy. This observation suggests how to better manage pipeline contention. To assess the impact of memory latency on processor performance, a dual-core MT machine with four thread contexts per core is utilized. These specific benchmarks are chosen to allow the workload to include programs with both favorable and unfavorable cache locality. To eliminate the issue of wasting the wake-up signals, this work proposes an approach that involves storing all the wake-up calls. It asserts the wake-up calls to the consumer and the producer can store the wake-up call in a variable. An assigned value in working system (or kernel) storage that each process can check is a semaphore. Semaphore is a variable that reads, and update operations automatically in bit mode. It cannot be actualized in client mode since a race condition may persistently develop when two or more processors endeavor to induce to the variable at the same time.
 This study includes code to measure the time taken to execute both functions and plot the graph. It should be noted that sending multiple requests to a website simultaneously could trigger a flag, ultimately blocking access to the data. This necessitates some computation on the collected statistics. The execution time is reduced to one third when using threads compared to executing the functions sequentially. This exemplifies the power of multithreading.

Flash entropy search to query all mass spectral libraries in real time.

Public repositories of metabolomics mass spectra encompass more than 1 billion entries. With open search, dot product or entropy similarity, comparisons of a single tandem mass spectrometry spectrum take more than 8 h. Flash entropy search speeds up calculations more than 10,000 times to query 1 billion spectra in less than 2 s, without loss in accuracy. It benefits from using multiple threads and GPU calculations. This algorithm can fully exploit large spectral libraries with little memory overhead for any mass spectrometry laboratory.

A multithreaded CUDA and OpenMP based power‐aware programming framework for multi‐node GPU systems

SummaryIn the article, we have proposed a framework that allows programming a parallel application for a multi‐node system, with one or more graphical processing units (GPUs) per node, using an OpenMP+extended CUDA API. OpenMP is used for launching threads responsible for management of particular GPUs and extended CUDA calls allow to transfer data and launch kernels on local and remote GPUs. The framework hides inter‐node MPI communication from the programmer. For optimization, the implementation takes advantage of the MPI_THREAD_MULTIPLE mode allowing: multiple threads handling distinct GPUs as well as overlapping communication and computations transparently using multiple CUDA streams. The solution allows data parallelization across available GPUs in order to minimize execution time and supports a power‐aware mode in which GPUs are automatically selected for computations using a greedy approach in order not to exceed an imposed power limit. We have implemented and benchmarked three parallel applications including: finding the largest divisors; verification of the Collatz conjecture; finding patterns in vectors. These were tested on three various systems: a GPU cluster with 16 nodes, each with NVIDIA GTX 1060 GPU; a powerful 2‐node system—one node with 8 NVIDIA Quadro RTX 6000 GPUs, the second with 4 NVIDIA Quadro RTX 5000 GPUs; a heterogeneous environment with one node with 2 NVIDIA RTX 2080 and 2 nodes with NVIDIA GTX 1060 GPUs. We demonstrated effectiveness of the framework through execution times versus power caps within ranges of 100–1400 W, 250–3000 W, and 125–600 W for these systems respectively as well as gains from using two versus one CUDA streams per GPU. Finally, we have shown that for the testbed applications the solution allows to obtain high speed‐ups between 89.3% and 97.4% of the theoretically assessed ideal ones, for 16 nodes and 2 CUDA streams, demonstrating very good parallel efficiency.