Research on parallel DBSCAN algorithm based on ternary optical computer

SurfaceNets is a powerful visualization technique typically used to contour non-continuous, discrete, volumetric scalar fields such as segmentation label maps. Label maps are ubiquitous to medical computing, biological studies, and materials characterization, used in applications ranging from anatomical atlas creation to nanotechnology analysis. Due to the uniform spacing of volume data, however, representing data with highly variable resolution is challenging. Consequently we have developed a generalized high-performance, parallel SurfaceNets algorithm that processes unorganized, labeled point clouds. Based on a scalable, meshless Voronoi approach, the algorithm independently processes each Voronoi hull in parallel using a hierarchical neighborhood point search metric. By employing novel topological constructs, the resulting meshless tessellation can be readily transformed into a connected conformal mesh, from which multiple, valid contour surfaces can be simultaneously extracted and smoothed. Additional contributions include a general API for locating points proximal to Voronoi hulls; the definition of topological coordinates used to detect and eliminate numerical degeneracies, merge coincident points, rapidly produce the dual Delaunay triangulation, and build smoothing stencils; and the construction of a Voronoi adjacency graph along with associated necessary conditions to ensure the generation of valid tessellations. Characterization of parallel performance is also quantified, including producing Voronoi and Delaunay tessellations of 128 million hulls and more than 750 million tetrahedra. A software implementation is available from the open source the Visualization Toolkit (VTK) system at vtk.org.

Merge trees are fundamental topological descriptors with broad theoretical and practical significance, yet their computation, particularly for augmented merge trees, incurs substantial computational and memory overhead. We present BiGMT, a highly scalable parallel algorithm for merge tree construction based on regions of the Morse-Smale segmentation. By exploiting boundary vertex characteristics and the dynamic merging of these regions, BiGMT preserves the essential topology of d-dimensional manifolds and represents the structure as a weighted bipartite graph. This formulation decomposes merge tree construction into fine-grained parallel tasks, significantly reducing traversal cost and memory consumption. Furthermore, BiGMT efficiently constructs augmented merge trees using reverse binary lifting search and parallel connection mechanisms, substantially improving augmentation performance. Extensive experiments on medium- to large-scale scalar field datasets demonstrate substantial GPU acceleration. BiGMT consistently outperforms ExTreeM, PPP, and FTM-Tree on RTX4090 and H200, achieving speedups of up to 23.95× and demonstrating superior parallel efficiency and scalability.

Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially with network size. We propose a decomposition framework based on directed convex subgraphs and introduce a minimal d-decomposition tree. Together, they provide a principled alternative to classical junction-tree constructions. The proposed framework represents the joint distribution by lower-dimensional sub-models that can be learned and stored separately. This decomposition reduces computational cost and naturally enables parallel computation. Based on a minimal d-decomposition tree, we further develop two parallel algorithms for parameter estimation and probabilistic inference. Experiments show that the proposed method substantially improves computational efficiency over junction-tree methods while maintaining inference accuracy, especially for low-dimensional queries.

This article presents the principles, software architecture, and performance analysis of the GPU port of the lattice Boltzmann software library Palabos [J. Latt et al., “Palabos: Parallel lattice Boltzmann solver”, Comput. Math. Appl. 81, 334–350, (2021)] . A hybrid CPU-GPU execution model is adopted, in which numerical components are selectively assigned to either the CPU or the GPU, depending on considerations of performance or convenience. This design enables a progressive porting strategy, allowing most features of the original CPU-based codebase to be gradually and seamlessly adapted to GPU execution. The new architecture builds upon two complementary paradigms: a classical object-oriented structure for CPU execution, and a data-oriented counterpart for GPUs, which reproduces the modularity of the original code while eliminating object-oriented overhead detrimental to GPU performance. Central to this approach is the use of modern C++, including standard parallel algorithms and template metaprogramming techniques, which permit the generation of hardware-agnostic computational kernels. This facilitates the development of user-defined, GPU-accelerated components such as collision operators or boundary conditions, while preserving compatibility with the existing codebase and avoiding the need for external libraries or non-standard language extensions. The correctness and performance of the GPU-enabled Palabos are demonstrated through a series of three-dimensional multiphysics benchmarks, including the laminar–turbulent transition in a Taylor–Green vortex, lid-driven cavity flow, and pore-scale flow in Berea sandstone. Despite the high-level abstraction of the implementation, the single-GPU performance is similar to CUDA-native solvers, and multi-GPU tests exhibit good weak and strong scaling across all test cases. Beyond the specific context of Palabos, the porting methodology illustrated here provides a generalizable framework for adapting large, complex C++ simulation codes to GPU architectures, while maintaining extensibility, maintainability, and high computational performance. Program Title: Palabos: Parallel Lattice Boltzmann Solver CPC Library link to program files: https://doi.org/10.17632/cx7j83pr69.1 Developer’s repository link: https://gitlab.com/unigespc/palabos Licensing provisions: AGPLv3 Programming language: C++17, using C++ parallel algorithms Supplementary material: A compressed file containing a snapshot of the described code version Nature of problem: The lattice Boltzmann method (LBM) is a widely used approach in computational fluid dynamics (CFD) for simulating flows in complex geometries, turbulence, multiphase systems, and thermal transport. The Palabos library was designed to provide the scientific community with a flexible and efficient parallel LBM platform, implementing a broad range of physical and numerical models. Until now, Palabos has been CPU-based, relying on distributed-memory parallelism through MPI. However, with modern simulation platforms and supercomputers increasingly dominated by GPU accelerators, a CPU-only implementation no longer provides competitive performance. This work presents a complete redesign of the Palabos architecture to run efficiently on multi-GPU systems. The new code supports hybrid execution: legacy CPU components remain functional, new GPU-optimized modules can be developed within a compatible programming model, and both can interact seamlessly. This version supersedes the original CPU-only implementation and will serve as the basis for all future development of Palabos. Solution method: The GPU version of Palabos introduces a new data container, the AcceleratedLattice , designed specifically for accelerator execution. It preserves the user interface of the CPU-based MultiBlockLattice , but replaces its array-of-structures layout and virtual-function polymorphism with a structure-of-arrays layout, thread-safe collision–streaming schemes, and integer tags for model selection. These changes enable efficient memory access, fine-grained parallelism, and compatibility with GPU execution models, while maintaining backward compatibility and supporting hybrid CPU/GPU workflows. Parallelism is expressed through C++17 standard parallel algorithms, compiled with the NVIDIA HPC SDK. Fundamental parallel algorithms such as for_each , transform_reduce , and exclusive_scan are used to implement the essential building blocks of the lattice Boltzmann method, including linear processing, reductions, and data packing for MPI communication. This data-oriented design yields a portable and maintainable GPU implementation, preserves modularity, and ensures that existing Palabos applications can be ported to GPU with minimal modifications. Additional comments including restrictions and unusual features: The GPU version currently relies on the platform-independent framework of C++ parallel algorithms but has, as of now, only been tested with NVIDIA GPUs (compiled with nvc++) supports NVIDIA architectures with CUDA-compatible compilers (tested with nvc++). The current version of the code does not compile with the 25.X series of the nvc++ compiler.

AutoParaTrans: An integrated workflow for CUDA acceleration and automated parallel algorithm transformation in electrical and thermal transport

We propose Probabilistic Inclusion Depth (PID) for the ensemble visualization of scalar fields. By introducing a probabilistic inclusion operator $\subset_{p}$, our method is a general data depth model supporting ensembles of fuzzy contours, such as soft masks from modern segmentation methods, and conventional ensembles of binary contours. We also advocate for extending contour extraction in scalar field ensembles to become a fuzzy decision by considering the probabilistic distribution of an isovalue to encode the sensitivity information. To reduce the complexity of the data depth computation, an efficient approximation using the mean probabilistic contour is devised. Furthermore, an order-of-magnitude reduction in computational time is achieved with an efficient parallel algorithm on the GPU. Our new method enables the computation of contour boxplots for ensembles of probabilistic masks, ensembles defined on various types of grids, and large 3D ensembles not studied by existing methods. The effectiveness of our method is evaluated through numerical comparisons with existing techniques on synthetic datasets, examples of real-world ensemble datasets, and expert feedback.

Hemodynamic analysis is an essential tool for predicting the behavior of blood flows and assessing the risk of renovascular diseases. In this paper, by employing a CFD-based finite element method coupled with an efficient parallel algorithm for the unsteady incompressible Navier-Stokes equations, we conduct a comprehensive investigation of renal hemodynamics and the impact of outflow boundary conditions in patient-specific models of normal, stenotic, and aneurysmal arteries featuring rich small-branch networks. Based on the hemodynamic analysis for the severe stenosis (area stenosis ) and the aneurysm (diameter mm), we observe a pressure drop exceeding 10mmHg and a distal-to-proximal pressure ratio below 0.9 for these lesions, which is considered hemodynamically significant and likely induces renovascular hypertension. Furthermore, we reveal that the low wall shear stress and complex vortices with bidirectional flow occur on the inner wall downstream of this stenosis, which play a critical role in driving atherosclerotic plaque formation. Through virtual aneurysm reconstruction and numerical simulation, we demonstrate that the presence of a renal aneurysm alters local flow patterns and pressure distributions. Numerical results for both healthy and pathological renal arteries show that outflow boundary conditions have a significant impact on the global distribution of pressure and local flow patterns near the outlets. Compared with constant pressure and resistance outflow boundary conditions, the two-element Windkessel model, through adjustments of its resistance and capacitance parameters, can provide more physiological flow and pressure distributions, particularly in capturing realistic pulsatile waveforms, pressure ranges, and distal flow patterns. Moreover, a sensitivity analysis of the resistance in the Windkessel boundary condition shows a negligible impact on the pressure drop and only a minor effect on the renal fractional flow reserve (a change of less than for a variation in resistance). When focusing solely on the hemodynamics within stenotic and aneurysmal lesions located far from the outlets, both the constant pressure and Windkessel boundary conditions yield comparable results for key lesion-specific hemodynamic indicators, including renal fractional flow reserve and pressure drop in the stenosis, and wall shear stress and oscillatory shear index in the aneurysm.

To comply with growing demands for pollution control and renewable energy, sophisticated intelligent optimization control schemes are explored to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, the inherent fluctuations in waste properties and dynamic operational conditions pose significant challenges in obtaining optimal set-points of key process parameters and implementing effective tracking control amidst substantial variations. In this article, a collaborative dynamic optimization control (CDOC) scheme is proposed for the MSWI process, featuring a multimodal optimization-based framework that seamlessly integrates optimization and control strategies to achieve optimal operational performance while minimizing difficulties of tracking control. A data-driven surrogate-assisted dynamic optimization scheme, including a parallel cell coordinate-based multimodal multiobjective competitive swarm optimization algorithm and a knowledge transfer-based dynamic response strategy, is proposed to obtain tradeoffs of performance indices and respond to irregular changes of the optimization environment. Then, an adaptive multivariable model predictive control strategy is proposed to derive the optimal control laws to achieve accurate and efficient tracking control of optimal set-points. Experimental studies are conducted on real industrial data to show the superb tracking control performance and promising optimization performance of the proposed CDOC scheme.

In this study, a variational model is proposed for filtering additive and multiplicative noise in wrapped phase maps. The model includes the Pythagorean identity, which is a fundamental requirement for phase maps, as well as the total variation regularizer to maintain discontinuities. We prove that our model is well-posed, compute the Euler-Lagrange equations, and present a fixed-point convergent algorithm for solving it. The quality of the proposed model is confirmed by conducting experiments on both synthetic and real data. Additionally, the implementation of the parallel fixed-point algorithm to accelerate the model's solution is presented. Implementations consist of OpenMP and CUDA execution on a multicore CPU and GPU, respectively. A comparison of the performance of parallel implementations with state-of-the-art methods is presented using synthetic and real data. According to the findings, our parallel implementations achieve speedups of 12x for multi-core CPUs and 125x for GPUs compared to the serial implementation.

An network that is closely interconnected by smaller units, forms the structural core in systems of parallel computing, integrating units like processors and their communication links for effective data exchange. Its performance is primarily governed by parameters such as wirelength, dilation, bandwidth and minimum cutwidth. The arrangement of these configurations in an optimal setup remains a key challenge, where the study of graph embedding offers an effective framework for enhancing parallel algorithm performance and overall system efficiency. This study employes an embedding function, which maps the guest graph, the family of ( K p − C p ) n onto suitable host structures.

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster.

Parallel compact artificial protozoa optimizer algorithm and its application in 3D coverage

Using inverse planning tools to create radiosurgery treatment plans is an iterative process, where clinical trade-offs are explored by changing the relative importance given to different objectives and rerunning the optimizer until a desirable plan is found. Simultaneously generating many plans corresponding to different objective weights, while the patient is awaiting treatment, would allow the planner to navigate clinical trade-offs interactively, without optimizing a new plan between each update. We seek to optimize hundreds of Gamma Knife radiosurgery treatment plans, corresponding to different weightings of objectives, fast enough to allow interactive Pareto navigation of clinical trade-offs to be incorporated into the clinicalworkflow. We apply the alternating direction method of multipliers (ADMM) to the linear-program formulation of the optimization problem used in the clinical Lightning optimizer. We implement both a CPU and a GPU version of ADMM in Matlab and compare them to Matlab's built-in, single-threaded dual-simplex solver. The ADMM implementation is adapted to the optimization procedure used in the clinical software, with a bespoke algorithm for maximizing the overlap between low-dose points for different objective weights. The method is evaluated on a test dataset consisting of 20 cases from three different indications, with between one and nine targets and total target volumes ranging from 0.66 to 52 cm3. The total optimization time to create 81 plans corresponding to different objective weightings varied from 63 to 520 s on CPU and from 1.8 to 40 s GPU, for the different test cases. As a reference, optimizing 81 plans using simplex took 100-51000 s, corresponding to ADMM speedups of 1.6-97 and 54-1500 times for the CPU and GPU, respectively. Increasing the number of plans to 441, corresponding to all combinations of slider values between 0.0 and 1.0 in steps of 0.05 in the clinical software, the total ADMM optimization time on GPU was between 3.0 and 110 s for the different testcases. Plan quality was evaluated by rerunning the ADMM optimization 20 times, each with a different random seed, for each test case and for nine objective weightings per case. The resulting relative differences in clinical metrics ( ) were 0.0 0.2%, 0.0 1.6%, 0.1 0.8%, and 0.1 3.0%, for coverage, selectivity, gradient index and beam-on time, respectively, compared to mean values for the corresponding reference simplex results. The standard deviations in these metrics closely mimicked those obtained when rerunning the simplex solver, verifying the validity of the method. We show how ADMM can be adapted for radiosurgery plan optimization, allowing hundreds of high-quality Gamma Knife treatment plans to be created in under two minutes on a single GPU, also for very large cases. The presented method would allow streamlined multicriteria optimization on the day of treatment, with interruption-free navigation of clinicaltrade-offs.

Real-time systems demand more than raw speed—they must guarantee response times even under heavy, continuously shifting data loads. The choice of data organization strategy is therefore critical: it shapes not only throughput but also timing predictability, memory footprint, and scalability. This paper surveys data structures suited to systems with strict timing requirements. Both foundational structures—arrays, linked lists, trees, hash tables—and advanced mechanisms such as kinetic data structures, bit-vector encodings, segment trees, and lock-free containers are examined. Time-space tradeoffs, worst-case versus average-case behavior, and suitability for hard versus soft real-time deadlines are analyzed for each. Domain-specific applications across network switching, game engines, autonomous vehicles, financial trading platforms, and AI inference pipelines are discussed, with a proposed hybrid architecture combining adaptive indexing, column-oriented memory layouts, and parallel algorithms to achieve genuine sub-millisecond latency at scale. Analysis is grounded in complexity theory and practical performance observations.

Advances in molecular epidemiology and computational modeling have improved our ability to track pathogen evolution, but accurate reconstruction of spatiotemporal transmission remains essential for epidemic preparedness and response. Structured coalescent models offer a phylogeographic framework by restricting coalescence to lineages within the same deme. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of localities and hundreds to thousands of genomes exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and partial likelihood vectors update. Here, we introduce an algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: i) independent calculation of deme-transition probability matrices across time intervals; ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7 to 8 fold, and parallelization further boosts performance to 10 to 26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the BEAST X and BEAGLE software packages, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

We consider the classic \(k\) -center problem in the constant dimensional Euclidean space under a parallel setting, on the low-local-space Massively Parallel Computation (MPC) model, with local space per machine of \(\mathcal{O}(n^{\delta})\) , where \(\delta\in(0,1)\) is an arbitrary constant. As a central clustering problem, the \(k\) -center problem has been studied extensively. Still, until very recently, all parallel MPC algorithms have been requiring \(\Omega(k)\) or even \(\Omega(kn^{\delta})\) local space per machine. While this setting covers the case of small values of \(k\) , for a large number of clusters these algorithms require large local memory, making them poorly scalable. The case of large \(k\) , \(k\geq\Omega(n^{\delta})\) , has been considered recently for the low-local-space MPC model by Bateni et al. (2021), who gave an \(\mathcal{O}(\log\log n)\) -round MPC algorithm that produces \(k(1+o(1))\) centers whose cost has multiplicative approximation of \(\mathcal{O}(\log\log\log n)\) . In this paper we extend the algorithm of Bateni et al. and design a low-local-space MPC algorithm that in \(\mathcal{O}(\log\log n)\) rounds returns a clustering with \(k(1+o(1))\) clusters that is an \(\mathcal{O}(\log^{*}n)\) -approximation for \(k\) -center.

In this paper, we introduce an immersive Spatial Augmented Reality (SAR) arena where multiple mobile robots interact with virtual sensors and actuators, enabling novice students to explore distributed and parallel algorithms. The arena uses overhead projection to display virtual objects (e.g., walls, targets, and sensor fields) onto a physical workspace, while robots (Smart Cutebot controlled by Micro:bit) operate under both physical and projected inputs. This framework is implemented via specialized software extensions for Microsoft MakeCode, offering varying levels of abstraction suitable for both novice learners and advanced researchers. The system’s effectiveness is validated through technical characterization of its positioning sensors (achieving an accuracy of over 99% and a precision margin of ±0.1 cm) and qualitative user studies with primary and university-level students. The findings suggest that the virtualization of robotic components through SAR significantly lowers the entry barrier for understanding distributed programming and collective robotics while fostering the development of computational thinking through immersive, collaborative interaction.

ABSTRACT Sequence alignment is one of the fundamental concepts in bioinformatics used in genome assembly, molecular evolution, and drug designing. Traditional algorithms like Needleman–Wunsch (NW) and Smith–Waterman (SW) are precise but computationally expensive. This renders them impractical whenever dealing with large‐scale datasets resulting from next‐generation sequencing (NGS) technologies. Towards addressing this challenge, we propose BioAlignNet, a novel GPU‐accelerated framework designed for efficient global sequence alignment of DNA/RNA/Protein sequences. BioAlignNet utilizes graph theory to model the alignments as shortest‐path problems in directed weighted graphs. Through incorporating an optimized and parallel edge weight computation and a parallel Shortest Path Faster Algorithm (SPFA), the framework achieves low computational complexity of where and are the lengths of the input sequences, and is the number of GPU threads per block. Rather than introducing a new alignment scoring scheme, BioAlignNet contributes a formally verified execution model for exact global alignment by recasting dynamic programming as a shortest‐path problem whose correctness is preserved under asynchronous GPU parallelism. Along with the low computational complexity, the framework also maintains biological integrity. Promising innovations include the implementation of the modular concept to accommodate different sequence types, substitution matrices, and scoring schemes, CUDA‐based parallelism of edge weight calculation, and traceback methods to build accurate alignments. BioAlignNet's evaluations conducted on various datasets show a remarkable runtime enhancement reaching up to 177 times speedup over CPU implementations, maintaining alignment optimality while giving better coverage and percentage identity. Experimental evaluations on real‐world datasets and validations through statistical significance tests demonstrate its scalability and robustness, making it suitable for genomics and proteomics. With its incorporation of biological relevance and computational optimization, BioAlignNet offers an efficient method for the alignment of large‐scale genome sequences to promote the development of molecular biology research.

{A novel Bayesian approach using horseshoe priors is developed for exponential random graph models (ERGMs), addressing degeneracy and improving inference. Simulation studies demonstrate superior performance over classical ERGMs.} Exponential Random Graph Models (ERGMs) are widely used for analyzing network data but often face challenges such as parameter estimation difficulties and model degeneracy. This paper introduces the Bayesian Horseshoe Prior ERGM (BHSERGM), a hierarchical Bayesian framework that leverages the adaptive shrinkage of the horseshoe prior to improve inference in sparse, high-dimensional networks. Implemented via a parallel adaptive direction sampling algorithm, BHSERGM enhances computational efficiency and convergence. Simulation studies and empirical applications demonstrate that BHSERGM shows improved estimation accuracy and enhanced recovery of network structure compared to conventional Bayesian ERGMs, while its adaptive regularization approach appears effective in mitigating degeneracy issues commonly encountered in standard ERGM frameworks.