In real-world scenarios, extreme cases where pedestrians suddenly emerge from blind spots or occlusions, leaving only a minimal amount of observable trajectory points, occur frequently. This presents a significant challenge for autonomous driving and robotic navigation, where pedestrian safety and timely response are critical considerations. To address this challenge, we propose a framework for instantaneous trajectory prediction using Latent bidirectional Cooperative Diffusion (LCD). It designs a complementary mechanism that constructs a coupled bidirectional cooperative diffusion model. LCD simultaneously and progressively generates unobserved past trajectories and future trajectories, feeding each other as conditions into the cross-attention module for mutual guidance. This framework employs CVAE as its encoder to map the observed multi-model trajectories into a high-dimensional latent space to enhance complex representations. Experiments conducted on the ETH/UCY and SDD datasets demonstrate the superiority of our framework.

Modeling stochastic multi-ship trajectories is vital for maritime safety and interaction efficiency. Recent researches show that diffusion models excel in trajectory prediction, surpassing GANs and VAEs in generation quality, diversity and stability. However, their slow sampling speed remains a major limitation, as producing high-quality trajectories typically requires hundreds of denoising steps. We introduce DDA, a novel method that accelerates multi-ship trajectory generation by distilling the reverse diffusion process, progressively reducing sampling steps by half while minimizing quality loss. We use CVAE-based encoder to map multimodal inputs into state embeddings in the latent space, and use distillation diffusion in the latent space to more quickly and better represent multi-ship trajectories. The diffusion model uses Transformer-based core, and we incorporate SO(2) invariance and equivariance to enhance model representation. Validation on real-world AIS datasets shows that the student model retains high-quality trajectory generation while sampling speed is approximately 30 times faster.

Aquatic environments confront mounting threats from diverse sources, among which the persistent migration of continuous objects (e.g., chemical contaminants) is a primary concern. While advances have been made in tracking these entities using underwater acoustic sensor networks (UASNs), full-scale monitoring is challenged by unpredictable sensor deployments. Though Autonomous underwater vehicles show promise, their prohibitive costs and operational complexities limit their broad adoption. To tackle these issues, this article introduces a novel search strategy named virtual mobile agent-assisted continuous object tracking (VMA-COT). Drawing inspiration from binary tree structures, VMA-COT refines the search from the entire network to targeted hierarchical boundary mapping cells. Each cell encompasses a Section of the object’s boundary. A designated node within each cell evaluates information entropy at specified locations using a meticulously designed search sequence. By utilizing a feedback mechanism, grounded in the information entropy and the search sequence, VMA-COT progressively pinpoints the boundary. This methodology can be likened to a central node dispatching mobile agents in each cell. These agents, limited by a specific step range, adjust their trajectories for precise boundary delineation. Empirical tests and simulations demonstrate the effectiveness of VMA-COT, highlighting its efficiency, accuracy, and boundary node utilization.

Tracking continuous objects in the ocean, such as oil spills, is a significant challenge, and one that underwater acoustic sensor networks (UASNs) are well suited to address. While prevalent approaches often employ complex, multiphase processes to delineate the boundaries of these objects, enabling effective monitoring, they may not be ideally suited for time-sensitive situations, where a rapid response often outweighs the need for extreme precision. To address this gap, we introduce a streamlined boundary tracking algorithm, the centroid computation of feasible region for continuous object tracking (CCFR-COT), intended for unmanned surface vessel (USV)-assisted UASNs. This algorithm utilizes a fundamental full binary tree-structured network configuration along with two of its variations, designed to handle cases where continuous objects disperse in either free space or two semi-infinite spaces. The chosen network configuration aids in the selection of boundary mapping cells, thereby sketching a coarse-grained feasible region that envelops the actual boundary. Sequentially visiting these cells, the USV systematically constructs and deconstructs convex hulls of sensor nodes to progressively reduce location uncertainty within the feasible region. Only the centroid of the final feasible region in each boundary mapping cell is output for boundary fitting. Both experimental and simulated results validate the streamlined design of the CCFR-COT, which ensures timely tracking results and provides a balance between rapid response and tracking accuracy.

Abstract Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

Frequent activities in marine energy exploration and transportation have led to the ongoing presence of continuous objects, such as oil spills and radioactive waste, in the ocean. This article focuses on enhancing the understanding of these objects’ boundaries for accurate assessment of their shape, coverage, and evolution. We introduce a continuous object tracking algorithm named JGL-COT, based on joint global-local binary tree topological transformations and specifically designed for underwater acoustic sensor networks. The contribution of JGL-COT lies in its ability to leverage the correlation between the morphologies of a continuous object's boundaries over time, alternating between global and local binary tree topological transformations. When the present boundary features a strong resemblance to its previous form, JGL-COT switches to a local transformation by establishing a semi-infinite region. Otherwise, it transitions to a global transformation. Following this, JGL-COT chooses a group of binary tree-structured cells for boundary mapping, creating virtual boundary nodes as sampling points for boundary fitting. Building upon graph theory, we derive the lower bound on the effectiveness and time complexity of the proposed joint global and local binary tree topological transformations when applied to object boundary tracking. Experiments in both realistic and simulated settings confirm that JGL-COT provides highly accurate tracking and significantly reduces network energy consumption.

Proactive caching in 6 G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6 G cloud-edge, we consider the issue of multi-objective joint optimization,i.e., maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the mosteffectivesample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture,i.e., multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

SpMV (Sparse Matrix-Vector multiplication), in its simplest form y = Ax, multiplies a sparse matrix with a dense vector and is a widely used computing primitive in the domain of HPC. On the newly SW26010 many-core platform, we propose a highly efficient CSR (Compressed Storage Row) based implementation of parallel SpMV, referred to as SWCSR-SpMV in the sequel. SpMV in the CSR format can be trivially parallelized but its performance is majorly impeded by memory access efficiency, and therefore to leverage high-throughput memory access mechanism while avoiding redundant bandwidth usage becomes the major goal of designing high performance SpMV on the target platform. The original problem is sequentially partitioned into row-slices, each of which can reside in the fast scratchpad memory, so that the loaded x'es can be reused; meanwhile, a dynamic look-ahead scheme is applied to avoid redundant memory access; we split the many-core mesh into smaller communication scope to facilitate the sharing of the common data across the working threads via the high speed on-mesh data bus. Beyond the above, to leverage massive parallelism balanced workload is ensured by both static and dynamic means. Performance evaluation is done on a benchmark of 36 frequently used sparse matrices in the fields of graph computing, data mining, computational fluid dynamics, etc.. While the performance upper-bound is defined by the ratio between the minimal data access volume required against the practically optimal bandwidth, ignoring the computing overhead, SWCSR-SpMV can achieve an efficiency of nearly 87%, maintaining over 75% for 1/3 of the testing matrices. SWCSR-SpMV is further applied in a PETSc based application, a 1.75x-2.6x speedup is sustained in a multi-process environment on the Sunway TaiHuLight supercomputer.

Stream compaction, frequently found in a large variety of applications, serves as a general primitive that reduces an input stream to a subset containing only the wanted elements so that the follow-on computation can be done efficiently. In this paper, we propose a fast parallel stream compaction for IA-based multi-/many-core processors. Unlike the previously studied algorithms that depend heavily on a black-box parallel scan, we open the black-box in the proposed algorithm and manually tailor it so that both the workload and the memory footprint is significantly reduced. By further eliminating the conditional statements and applying automatic code generation/optimization for performance-critical kernels, the proposed parallel stream compaction achieves high performance in different cases and for various data types across different IA-based multi/manycore platforms. Experimental results on three typical IA-based processors, including a quad-core Core-i7 CPU, a dual-socket 8-core Xeon CPU, and a 61-core Xeon Phi accelerator show that the proposed implementation outperforms the referenced parallel counterpart in the state-of-art library Thrust. On top of the above, we apply it in the random forest based data classifier to show its potential to boost the performance of real-world applications.

Numerical simulation of thermal convection in the Earth's outer core requires extreme-scale computing due to the large temporal and spatial disparity, extreme physical parameters, rapid rotation and spherical geometry. In this work, the numerical simulation of the thermal convection in the Earth's outer core for CPU-MIC heterogeneous many-core systems is studied. Firstly, starting from a legacy parallel code based on the PETSc software package, a framework of the numerical simulation built on CPU-MIC heterogeneous many-core systems has been developed. Secondly, a sparse linear solver for CPUMIC heterogeneous many-core systems, which focuses on solving the two linear systems of the simulation, is presented and optimized. Thirdly, some computational kernels of the simulation, including sparse matrix-vector multiplication (SpMV) and polynomial preconditioner on distributed memory Xeon Phiaccelerated systems are implemented and optimized. In addition, in order to reduce the cost of data movement, we use methods to minimize the memory access, the PCI-E data transfer, and the MPI communication. Finally, some optimized measures are taken to the extended code. Experiments on Tianhe-2 Supercomputer show that as compared to the original code, our Xeon Phiaccelerated design is able to deliver 6.93x and 6.00x speedups for single MIC device and 64 MIC devices, respectively.

There is an urgent demand in studying efficient methodologies to enable hybrid multi- and many-core accelerations in global climate simulations. The Model for Prediction Across Scales (MPAS) is a family of earth-system component models that receives increasingly more attention. Like many other models, MPAS, though features some emerging numerical algorithms, employs a pure MPI approach for parallel computing, which, to date, is in lack of support for multi-threaded parallelism, especially on many-core accelerated systems. In this work, we extend the shallow-water model in MPAS to demonstrate a pattern-driven approach for hybrid multi- and many-core accelerations of climate models. We first identify all basic computation patterns through a rigorous analysis of the MPAS code. Then for the whole model, we use the identified patterns as building blocks to draw a data-flow diagram, which serves as a perfect indicator to recognize data dependencies and exploit inherent parallelism. And finally, based on the data-flow diagram, a hybrid algorithm is designed to support concurrent computations done on both multi-core CPUs and many-core accelerators. We implement the algorithm and optimize it on an x86-based heterogeneous supercomputer equipped with both Intel Xeon CPUs and Intel Xeon Phi devices. Experiments show that our hybrid design is able to deliver an 8.35x speedup as compared to the original code and scales up to 64 processes with a nearly ideal parallel efficiency.

Developing an efficient and highly scalable ray tracer for the Metropolis light transport algorithm is becoming increasingly important as the request for photorealistic images becomes a common trend. Although the Metropolis light transport algorithm has produced some of the most realistic images to date, it usually takes a great amount of time to render an image. The development of an efficient and highly scalable ray tracer for the Metropolis light transport algorithm is hard due in large part to the irregular memory access patterns, the imbalanced workload of light-carrying paths and the complicated mathematical model and complex physical processes. In this paper, we present a highly scalable physically based parallel ray tracer for the Metropolis light transport algorithm. Firstly, we present the idea of snapshot and sub-snapshot, then propose a novel assignment partitioning algorithm for compute nodes and CPU cores since the demand-driven assignment partitioning algorithms don't work. Secondly, we propose a physically based parallel ray racing framework for the Metropolis light transport algorithm, which is based on a master-worker architecture. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, then a hybrid scheduling strategy combining a static and dynamic scheduling strategy is described. Experiments show that our physically based ray tracer almost reaches linear speedup by using 26,400 CPU cores on the Tianhe-2 supercomputer. Our ray tracer is more efficient and highly scalable.