ABSTRACT In urban road scenes, the cross‐domain data distribution differences caused by light and weather changes make the generalization performance of single‐domain trained object detectors in unknown weather scenarios significantly degraded (e.g., daytime‐sunny trained models in dusk‐rainy scenarios reduce the detection accuracy by more than 46% on average); moreover, faster R‐CNN suffers from insufficient generalization capability and inefficient real‐time inference due to architectural constraints. To address the above challenges, this paper proposes the CEN‐RTDETR method to improve the single‐domain generalization capability through a collaborative enhancement strategy: CP‐Mix color channel permutation dynamically simulates the RGB color channel permutation to simulate the multi‐sky color bias phenomenon, and enhances the color robustness of the input data; NP feature normalization perturbation applies a random feature perturbation to the channel statistics of the shallow feature map to optimize the extraction of texture, color and other basic style features; CORAL Loss minimizes the difference in feature distribution between the source domain and the virtual target domain through second‐order statistical matching. The experimental results show that CEN‐RTDETR achieves significant performance improvement on the cross‐weather scenario dataset DWD: The mean average precision ([email protected]) across different weather scenarios increases from 39.66% to 42.72% (+3.06%), especially for dusk‐rainy and night‐rainy scenarios, where [email protected] rises from 32.9% and 18.6% to 39.0% (+6.1%) and 24.1% (+5.5%) in extreme weather. The method in this paper effectively solves the cross‐domain generalization and efficiency problems in single‐domain generalized object detection, which provides new technical possibilities for real‐time detection in complex urban road scenes.

With the rapid advancement of artificial intelligence technologies such as ChatGPT, AI agents, and video generation, contemporary mobile systems have begun integrating these AI capabilities on local devices to enhance privacy and reduce response latency. To meet the computational demands of AI tasks, current mobile SoCs are equipped with diverse AI accelerators, including GPUs and Neural Processing Units (NPUs). However, there has not been a comprehensive characterization of these heterogeneous processors, and existing designs typically only leverage a single AI accelerator for LLM inference, leading to suboptimal use of computational resources and memory bandwidth.

Timely detection and treatment of road cracks are crucial to prevent further deterioration of pavement. An accurate road crack detection algorithm can significantly reduce the human resources required for pavement maintenance. However, existing CNN-based object detectors, such as the You Only Look Once (YOLO) series of algorithms, face challenges such as receptive field fixation and information loss during feature extraction, resulting in lower accuracy in road crack detection. Therefore, we propose the Selective Dynamic Feature Compensation-YOLO (SDFC-YOLO) algorithm for pavement distress detection. Firstly, we introduce the Dynamic Downsampling Module (DDM), which adaptively adjusts the sampling positions of the convolutional kernel during the feature extraction process, addressing the issue of a fixed receptive field. Secondly, we propose a novel feature fusion method compensating for lost feature information in the path aggregation network. Lastly, we design a multi-scale weight selection module based on the above feature fusion method. It aims to utilize the channel weights of high-level features to guide bottom-level features and select more important features for compensation, thereby further enhancing detection accuracy. Experimental results demonstrate that compared to the benchmark model YOLOv8s, our method improves Precision (P), Recall (R), mean Average [email protected] ([email protected]), and F1 score on the UAPD dataset by 6.8%, 2.4%, 5.3%, and 4.3%, respectively. Similarly, on the UAV-PDD2023 dataset, the aforementioned metrics are enhanced by 3.1%, 4.4%, 2.7%, and 3.8%, respectively. Furthermore, our method takes only 10.53ms to process a$1280\times 1280$resolution image, which fully meets the requirement of real-time detection.

This paper implements a Smoothed Particle Hydrodynamics simulation code and distributes it on a heterogeneous cluster. The theoretical analysis results show that treating GPU as equivalent peer of CPU rather than an assistant or a substitute is the most efficient way of using a CPU+GPU compute node. However, it raises complex challenges of heterogeneous cooperation. Our strategies of hybrid-level domain decomposition, multi-stage thread pool and data transfer optimization are addressed and the respective effects are showed. Steady balance ratio of 1.1 between 6 nodes and GPU workload ratio of 0.43 between 2CPU+1GPU cards are observed. Average speedups of 2.9x, 1.81x, 1.1x over the single GPU version are obtained on three heterogeneous systems. The weak scaling tests are carried out on 1 to 16384 CPU-GPU nodes and the overall parallel efficiency of 70.3% is achieved.

Although there exist much work on GPU acceleration on the SPH method, the focus so far has been on the Euler equations in fluid mechanics. This paper presents GPU acceleration on the SPH method for the Navier-Stokes equations for both solid and fluid mechanics. We investigate and compare three CPU-GPU coupling models in terms of one large-scale parallel application code: (1) CPU?GPU (to only run hotspots on GPU), (2) GPU-alone (to run the whole of simulation on GPU), and (3) CPU||GPU (to treat CPU and GPU as equivalent processors). A common issue to the three models, "easy code transplant onto GPU", is emphasized. Optimizations on particle indexing and particle interaction on GPU, which are of unique importance to a SPH code, are addressed. Numerical experiments are finally performed and 4x, 10x, 16x speedups are observed for the three coupling models, respectively, with reference to single CPU core. Among the three, the fastest model -- Xthe "CPU||GPU" model -- Xfurther undergoes scalability tests on a cluster of 6 heterogeneous nodes and shows 90+% parallel efficiency.

Since high performance computing sustained petaflops in 2008, numerical simulation entered a new era to use 10K to 100K processor cores in one single run of parallel computing. In pursuit of petascale computing, the challenges of scalability must be addressed. Petapar is a highly scalable simulation framework which implements two popular meshfree/particle methods, the smoothed particle hydrodynamics (SPH) and the material point method (MPM). The parallelization starts from the regular-grid-based domain decomposition. The scalability of the code is assured by fully overlapping of communication and computation, and a dynamic load balancing strategy. Petapar supports both flat MPI and MPI+Pthreads hybrid parallelization. The code is tested on Titan, which ranked first on the Top500 supercomputer list when our research work has been done in November 2012. Experiment results show that petaPar linearly scales up to 260K CPU cores with an excellent parallel efficiency of 100% and 96% for the SPH and MPM, respectively.