Parallel Algorithm Research Articles

Enhancing Solar Convection Analysis With Multi‐Core Processors and GPUs

ABSTRACTIn the realm of astrophysical numerical calculations, the demand for enhanced computing power is imperative. The time‐consuming nature of calculations, particularly in the domain of solar convection, poses a significant challenge for Astrophysicists seeking to analyze new data efficiently. Because they let different kinds of data be worked on separately, parallel algorithms are a good way to speed up this kind of work. A lot of this study is about how to use both multi‐core computers and GPUs to do math work about solar energy at the same time. Cutting down on the time it takes to work with data is the main goal. This way, new data can be looked at more quickly and without having to practice for a long time. It works well when you do things in parallel, especially when you use GPUs for 3D tasks, which speeds up the work a lot. This is proof of how important it is to adjust the parallelization methods based on the size of the numbers. But for 2D math, computers with more than one core work better. The results not only fix bugs in models of solar convection, but they also show that speed changes a little based on the gear and how it is processed.

Scalable O(log2n) Dynamics Control for Soft Exoskeletons

Robotic exoskeletons are being actively applied to support the activities of daily living (ADL) for patients with hand motion impairments. In terms of actuation, soft materials and sensors have opened new alternatives to conventional rigid body structures. In this arena, biomimetic soft systems play an important role in modeling and controlling human hand kinematics without the restrictions of rigid mechanical joints while having an entirely deformable body with limitless points of actuation. In this paper, we address the computational limitations of modeling large-scale articulated systems for soft robotic exoskeletons by integrating a parallel algorithm to compute the exoskeleton’s dynamics equations of motion (EoM), achieving a computation with O(log2n) complexity for the highly articulated n degrees of freedom (DoF) running on p processing cores. The proposed parallel algorithm achieves an exponential speedup for n=p=64 DoF while achieving a 0.96 degree of parallelism for n=p=256, which demonstrates the required scalability for controlling highly articulated soft exoskeletons in real time. However, scalability will be bounded by the n=p fraction.

Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior

The brain’s memory system is extraordinarily complex, evidenced by the multitude of neurons involved and the intricate electrochemical activities within them, as well as the complex interactions among neurons. Memory research spans various levels, from cellular and molecular to cognitive behavioral studies, each with its own focus, making it challenging to fully describe the memory mechanism. Many details of how biological neuronal networks encode, store, and retrieve information remain unknown. In this study, we model biological neuronal networks as active directed graphs, where each node is self-adaptive and relies on local information for decision-making. To explore how these networks implement memory mechanisms, we propose a parallel distributed information access algorithm based on the node scale of the active directed graph. Here, subgraphs are seen as the physical realization of the information stored in the active directed graph. Unlike traditional algorithms with global perspectives, our algorithm emphasizes global node collaboration in resource utilization through local perspectives. While it may not achieve the global optimum like a global-view algorithm, it offers superior robustness, concurrency, decentralization, and biological feasibility. We also tested network capacity, fault tolerance, and robustness, finding that the algorithm performs better in sparser network structures.

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

Robot skill acquisition for precision assembly of flexible flat cable with force control

Abstract The flexible flat cable (FFC) assembly task is a prime challenge in electronic manufacturing. Its characteristics of being prone to deformation under external force, tiny assembly tolerance, and fragility impede the application of robotic assembly in this field. To achieve reliable and stable robotic automation assembly of FFC, an efficient assembly skill acquisition strategy is presented by combining a parallel robot skill learning algorithm with adaptive impedance control. The parallel robot skill learning algorithm is proposed to enhance the efficiency of FFC assembly skill acquisition, which reduces the risk of damaging FFC and tackles the uncertain influence resulting from deformation during the assembly process. Moreover, FFC assembly is also a complex contact-rich manipulation task. An adaptive impedance controller is designed to implement force tracking during the assembly process without precise environment information, and the stability is also analyzed based on the Lyapunov function. Experiments of FFC assembly are conducted to illustrate the efficiency of the proposed method. The experimental results demonstrate that the proposed method is robust and efficient.

Numerical Modeling of Two-Phase Fluid Filtration for Carbonate Reservoir in Two-Dimensional Formulation

This work considers the isothermal process of incompressible viscous fluid filtration in an oil-saturated, fractured-porous reservoir. A study of the pressure and water saturation distribution process is carried out for a case in which a production well is put into operation. For this problem, i.e., a mathematical model in a two-dimensional formulation, a numerical method and a parallel algorithm are proposed. The mathematical model of two-phase filtration is written in accordance with the classical laws of continuum mechanics and Darcy’s law and also includes a function of fluid exchange between low-permeability pores and high-permeability natural fractures within the framework of the Warren–Root model. The numerical solution is based on the finite-difference method and a splitting scheme of physical processes and spatial coordinates. For a split system with respect to piezoconductivity, an implicit finite-difference scheme with fixed saturations is constructed, and with respect to saturation transfer, explicit and implicit difference schemes are constructed. For parallel implementation of the developed numerical approach, a method based on geometric parallelism is selected. Testing of the developed method is performed using the example of calculating liquid mass transfer for a wide range of parameters. To verify the model, the obtained calculated pressure curves are compared with field data recorded by a deep-well measuring device. The results allow for estimation of the distribution of reservoir pressure and water saturation depending on the permeability of the fracture set and the pore part. The obtained results allow for monitoring of well operations, reducing unexpected accident risks and optimizing the development system in order to increase oil production in fractured-porous reservoirs. Computational experiments confirm the efficiency of the developed numerical algorithm and its parallel implementation.

An Evaluation of Massively Parallel Algorithms for DFA Minimization

An Evaluation of Massively Parallel Algorithms for DFA Minimization

Heat Pump-Assisted Extractive Pressure-Swing Distillation with Integrated Feed Preconcentration/Solvent Recovery for Isopropanol-Benzene-Water Separation

Recently, heat-integrated pressure-swing distillation (HIPSD) has been explored for recovering isopropanol and benzene from wastewater, but the process remains highly energy-intensive. Given the dilute nature of the feed, intensified extractive distillation with an integrated preconcentration column (IED) is more suitable. However, previous researches have often overlooked the critical role of pressure and lacked rigorous optimization of operating pressure in such systems. In this article, we developed novel extractive pressure-swing distillation with integrated feed preconcentration/solvent recovery column (IEPSD) by introducing a pressure-swing configuration and incorporate energy-saving technologies to achieve more sustainable and cost-effective separation processes. First, thermodynamic analysis is conducted to explore the effect of pressure on extractive pressure-swing distillation. Then, a parallel genetic algorithm is applied for rigorous optimization, followed by the application of heat integration and heat pump. The IEPSD is superior to IED in terms of total annual cost (TAC) and CO2 emission. With the inclusion of energy-saving technologies, IEDHI and IEPSDHI further reduced TAC by 5.29% and 7.40%, and cut CO2 emissions by 25.18% and 24.03%, respectively, compared to their base processes. The use of a heat pump is particularly advantageous for IEPSD due to its lower boiling temperatures under vacuum pressure, leading to the development of IEPSDHIHP, which offered an additional 22.36% CO2 reduction and marginally lower TAC than IEPSDHI. Ultimately, IEPSDHIHP proves to be the best option, offering a 51.15% TAC reduction and 68.42% CO2 emissions reduction compared to HIPSD. In summary, the developed IED and IEPSD processes, especially with heat integration and heat pump technologies, offer significant economic and environmental advantages over conventional HIPSD.

Per Segment Plane Sweep Line Segment Intersection on the GPU

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.

Retraction Note: Redundant data high-efficiency compression based on distributed parallel algorithm

Retraction Note: Redundant data high-efficiency compression based on distributed parallel algorithm

Parallel linearized ADMM with application to multichannel image restoration and reconstruction

Many large-scale regularized inverse problems in imaging such as image restoration and reconstruction can be modeled as a generic objective function involves sum of nonsmooth but proximable terms, which are usually linear-operator-coupled. For the solution of these problems, a parallel linearized alternating direction method of multipliers (PLADMM) is proposed in this paper. At each step of the proposed algorithm, the proximity operators of the nondifferential terms are called individually. This leads to a highly parallel algorithm structure, where most sub-steps can be simultaneously solved. Profiting from the linearization step, the linear inverse operation is excluded. The convergence property of the proposed method is analyzed. The image deblurring, inpainting, and pMRI reconstruction experiments show that the proposed method has vast applicable vistas. Compared with the state-of-the-art methods, such as PADMM [21], FTVD-v4 [22], PPDS [33], FUSL [8], LADM [36], and ALADMM [27], it gains competitive results both in terms of quantitative indicators, such as PSNR or SSIM, and in terms of visual impression.

Modeling of Multibody Lubrication Dynamics in Cylindrical Roller Bearings

As a typical multibody system, rolling bearing consists of several lubricated frictional pairs which transmit motions and forces among components. In this paper, a multibody lubrication dynamics (MLD) model of cylindrical roller bearings (CRBs) is developed by coupling the bearings dynamics sub-model and several lubrication sub-models of each frictional pairs in real-time. The lubrication sub-models are discretized by using finite difference and solved by numerical iteration methods of Gauss-Seidel with line relaxation and discrete convolution-fast Fourier transform (DC-FFT) technique. What’s more, a parallel algorithm is also designed to enhance the calculation efficiency. Its results are validated by the experimental results in literature, and further compared by the results from other dynamics models. It is revealed that the film formation changes not only friction but also geometrical constraints and pressure distribution, which may introduce additional load inside bearing and increase rolling friction under high-speed conditions. As the dynamic oil film stiffness and damper are took into account by MLD, the cage has a lower whirl frequency and forces. The curve-fitting formulas of EHL film thickness are overestimates the thickness value, and thus change the load distribution and friction between roller and raceway.

Construction of parallel algorithms for the study of complex systems objects with a hierarchical-network structure

The procedure for complexite evaluation of complex systems objects that have a hierarchical-network structure has been formalized.In order to realization this procedure in real time mode, parallel computation algorithms are proposed.Speed up estimates were obtained for the mentioned algorithms, which confirm their high efficiency. The proposed parallel algorithms are oriented for execution on modern computing means with shared and distributed memory: computers with multi-core processors, clusters, hybrid architectures and in high performance distributed environments.

An Ultrawide Bandwidth Digital Backend System Based on PFB Algorithm for QTT

For the planning of the QiTai radio Telescope ultrawide bandwidth low-frequency pulsar receiving system, we designed and implemented a Field-Programmable Gate Array (FPGA)+CPU/GPU hybrid architecture digital backend system based on the Polyphase FilterBank (PFB) channeling algorithm. We used the FPGA signal acquisition and processing platform to implement ultrawide bandwidth signal sampling and designed the PFB algorithm to realize the digital channelization of multiple analog bandwidth signals. We also developed data encapsulation and multichannel parallel distribution firmware algorithms and realized the real-time parallel transmission of high-speed astronomical data streams based on the VLBI Data Interchange Format. We developed the Ultra Wide bandwidth Low-frequency pulsar data process PIPEline, which realized the real-time processing and data packaging of massive pulsar signals. Using the L-band (964–1732 MHz bandwidth) receiving system of the Nanshan 26 m radio telescope, we conducted a systematic test on the designed digital backend system and obtained high-quality observation data. By using the professional pulsar data processing software DSPSR to process the observation data, we obtained high signal-to-noise ratio pulse profiles.

Sequential and Parallel Algorithms to Compute Turbulent Coherent Structures

The behavior of turbulent flows remains a significant unsolved problem in physics. Recently, a large quantity of effort has been directed toward understanding the non-linear interactions of the different flow structures in order to address this challenge. In this paper, different implementations of one exact method for identifying these structures are analyzed. This includes two sequential algorithms and a parallelizable one, developed to handle large-scale data efficiently. The new parallel algorithm offers significant advantages in handling the computational demands of large simulations, providing a more scalable solution for future research.

NUMA-aware parallel sparse LU factorization for SPICE-based circuit simulators on ARM multi-core processors

In circuit simulators that resemble the Simulation Program with Integrated Circuit Emphasis (SPICE), one of the most crucial steps is the solution of numerous sparse linear equations generated by frequency domain analysis or time domain analysis. The sparse direct solvers based on lower-upper (LU) factorization are extremely time-consuming, so their performance has become a significant bottleneck. Despite the existence of some parallel sparse direct solvers for circuit simulation problems, they remain challenging to adapt in terms of performance and scalability in the face of rapidly evolving parallel computers with multiple NUMA hardware based on ARM architecture. In this paper, we introduce a parallel sparse direct solver named HLU, which re-examines the performance of the parallel algorithm from the viewpoint of parallelism in pipeline mode and the computing efficiency of each task. To maximize task-level parallelism and further minimize the thread waiting time, HLU devises a fine-grained scheduling method based on an elimination tree in pipeline mode, which employs depth-first search (DFS-like) to iteratively search for parent tasks and then place dependent tasks in the same task queue. HLU also suggests two NUMA node affinity strategies: thread affinity optimization based on NUMA nodes topology to guarantee computational load balancing and data affinity optimization to enable effective memory placement when threads access data. The rationality and effectiveness of the sparse solver HLU are validated by the SuiteSparse Matrix Collection. In comparison with KLU and NICSLU, the experimental results and analysis show that HLU attains a speedup of up to 9.14× and 1.26x (geometric mean) on a Huawei Kunpeng 920 Server, respectively.

Tripartite interaction representation algorithm for crystal graph neural networks

Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In the field of materials science, there is a current research emphasis on exploring techniques for characterizing material structures to achieve precise descriptions. This paper proposes a crystal graph convolution neural network model that incorporates a tripartite interaction approach. The model not only incorporates atomic information, bond lengths, and bond angles but also offers a method for updating atoms and bond lengths, facilitating accurate descriptions of crystal structures by capturing implicit structural information. Focusing on predicting the formation energy of crystalline compounds, our results demonstrate improved predictive accuracy compared to existing representation algorithms. The average error of the formation energy in the random dataset, demonstrating robust generalization, is merely 0.048eV/atom, with an impressive R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} value of 0.994. Additionally, this paper establishes a crystal graph neural network framework for predicting algorithm performance. By integrating automatic parallel algorithms and an automated process, we achieve a synthesis of these techniques, enhancing computational efficiency and streamlining algorithm usage.

Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology

Pretrained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pretraining + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.

Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP

The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95% faster than the Central Processing Unit (CPU) benchmark algorithm using 33% less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64s. Given the same amount of memory, MESHED can solve 50% larger problems than the CPU benchmark algorithm can.

Tolerance to asynchrony in algorithms for multiplication and modulo

In this article, we study some parallel processing algorithms for multiplication and modulo operations. We demonstrate that the state transitions that are formed under these algorithms satisfy lattice-linearity, where these algorithms induce a lattice among the global states. Lattice-linearity implies that these algorithms can be implemented in asynchronous environments, where the nodes are allowed to read old information from each other. It means that these algorithms are guaranteed to converge correctly without any synchronization overhead. These algorithms also exhibit snap-stabilizing properties, i.e., starting from an arbitrary state, the sequence of state transitions made by the system strictly follows its specification.