Accurate radio map construction is essential for 6 G wireless network optimization, yet faces significant challenges due to sparse real-world measurements and dynamic environmental obstacles. This paper presents RMF, a novel one-step generative model based on mean flow matching that enables direct mapping from noise to radio map distribution in a single forward pass. Our approach integrates a multi-feature U-Net architecture with specialized branches for processing building layouts, base station configurations, sparse measurements, and dynamic obstacles through cross-attention fusion. Extensive evaluations on the RadioMapSeer dataset demonstrate that RMF achieves state-of-the-art performance, reducing RMSE by 7.5–12.2% compared to diffusion-based methods while maintaining competitive SSIM scores of 0.9557–0.9674. In challenging zero-measurement scenarios, RMF attains PSNR improvements of 1.45–1.65 dB over existing approaches, showcasing robust performance in both static and dynamic environments. The model's balance of accuracy and efficiency makes it particularly suitable for real-time 6 G applications including coverage optimization and dynamic resource management.

To improve the performance of the sea ice model in the Community Earth System Model (CESM) under a heterogeneous computing environment, this work conducts an in-depth study on the memory access optimization of the Elastic-Viscous-Plastic (EVP) dynamics model in Community Ice Code(CICE) on the SW39000 heterogeneous many-core processor, which is deployed in the new-generation Sunway supercomputer. The processor’s complex on-chip heterogeneous architecture, multi-level memory hierarchy, and unique inter-core communication mechanism present significant challenges for the parallel optimization of the sea ice dynamics simulation. To address the inefficiencies caused by diverse data access patterns, a differentiated processing strategy based on data read/write characteristics is proposed to reduce unnecessary data transfers. In addition, to alleviate load imbalance arising from the sparsity of sea ice boundary update data, a local dynamic compression method incorporating the probability density of data sparsity is designed. This method dynamically compresses data according to the probability density of Direct Memory Access (DMA) data transfers, thereby reducing communication volume and balancing the workload across slave cores. Finally, to enhance the computational intensity of the slave cores and reduce data dependencies between master and slave cores, an operator fusion algorithm based on Remote Memory Access (RMA) communication is introduced to achieve efficient data caching and transmission between operators. Experimental results demonstrate that, under the standard gx3 grid configuration, the optimized EVP model achieves a 27.54×speedup over the serial version running on a single master core when executed with a single core group. Multi-core group parallel tests validate the excellent scalability of the proposed optimization strategies, achieving up to a 123.93×speedup with a 10-core group, while also exhibiting effective load balancing in terms of both clock cycles and instruction counts across the slave-core array.

The escalating disparity between the supply and demand of maritime radio spectrum resources necessitates the construction of high-fidelity Radio Environment Maps (REM) for effective spectrum situational awareness and dynamic management. However, this task is severely hampered by distinctive maritime challenges, including extreme data sparsity, highly dynamic electromagnetic propagation characteristics, and complex spatio-temporal correlations, which significantly degrade conventional terrestrial REM reconstruction methods. To overcome these limitations, this paper proposes a hierarchical REM reconstruction framework that synergistically integrates Spiking Neural Networks with physical constraints. Our contributions are threefold. First, we devise an adaptive Unmanned Aerial Vehicle sampling strategy based on a refined Ant Colony Optimization algorithm, incorporating a hierarchical priority decision mechanism and joint heuristic function to improve data collection efficiency under sparse sampling. Second, we architect a Frequency-Spatio-Temporal Attention (FSTA) -enhanced Spiking Neural Network (SNN) model that captures spatio-temporal dynamics from sparse observations for high-precision 2D REM completion. Third, we introduce a physics-guided knowledge distillation paradigm that embeds maritime electromagnetic propagation models as multi-stage soft constraints through three coordinated mechanisms, which direct input correction via height-weighted physical deviation terms, and loss-level supervision penalizing physically inconsistent 3D reconstructions. Extensive simulations conducted in a high-fidelity maritime scenario demonstrate, which is constructed using real geographic environments, GMTED digital elevation data, and representative meteorological conditions. Our framework outperforms tensor completion U-Net and PINN baseline across various sampling rates. Notably, at sampling rates of 20%, 50%, and 80%, the proposed framework consistently attains superior Root Mean Square Error (RMSE) and Normalized Mean Square Error (NMSE), with the Physical Residual Metric (PRM) further serving as a diagnostic indicator confirming internalization of physical priors.

Approximate computing is emerging as a promising paradigm of High-Performance Computing (HPC) to increase application performance, with mixed-precision computing

Radiology report generation that aims to accurately describe medical findings for given images, is pivotal in contemporary computer-aided diagnosis. Recently, despite considerable progress, current radiology report generation models still struggled to achieve consistent quality across difficult and easy samples, which dramatically impacts their clinical value. To solve this problem, we explore the difficult samples mining in radiology report generation and propose the Linear Hybrid-Reward based Reinforced Focal Learning (LHR-RFL) to effectively guide the model to allocate more attention towards some difficult samples, thereby enhancing its overall performance in both general and intricate scenarios. In implementation, we first propose the Linear Hybrid-Reward (LHR) module to better quantify the learning difficulty, which employs a linear weighting scheme that assigns varying weights to three representative Natural Language Generation (NLG) evaluation metrics. Then, we propose the Reinforced Focal Learning (RFL) to adaptively adjust the contributions of difficult samples during training, thereby augmenting their impact on model optimization. The experimental results demonstrate that our proposed LHR-RFL improves the performance of the base model across all NLG evaluation metrics, achieving an average performance improvement of 20.9% and 13.2% on IU X-ray and MIMIC-CXR datasets, respectively. Further analysis also proves that our LHR-RFL can dramatically improve the quality of reports for difficult samples. The source code will be available at https://github.com/ SKD-HPC/LHR-RFL.

Recently, automatic radiology report generation, which targets to generate multiple sentences that can accurately describe medical observations for given X-ray images, has gained increasing attention. Existing methods commonly employ the attention mechanism for accurate word generation. However, such attention-based methods fail to leverage useful image-level global features, thereby limiting the model's reasoning ability. To tackle this challenge, we propose two-stage global enhancement layers to facilitate the Transformer to generate more reliable reports from a global perspective. Specifically, the 1st Global Enhancement Layer (1st GEL) is designed to capture the global visual context features by establishing the relationships between image-level global features and previously generated words. The 2nd Global Enhancement Layer (2nd GEL) is devised to capture the region-global level features by building the relationships between image-level global features and region-level information. The experiments demonstrate that by integrating the aforementioned two-stage global enhancement layers into the Transformer model, our proposal achieves state-of-the-art (SOTA) performance on various Natural Language Generation (NLG) evaluation metrics. Further Clinical Efficacy (CE) evaluations also validate that our proposal is able to predict more critical information.

Abstract Compared with CUDA and OpenCL, OpenACC has the advantages of simple programming, openness, and good portability for GPU acceleration. An OpenMP/OpenACC implementation for molecular dynamics of silicon crystal on GPUs is proposed. First, to make effective use of vectorization and streaming, data structure conversion and data dependence elimination are designed. Second, the parallel version on the single GPU is realized by adding OpenACC guidance sentences, with very few modifications. Third, a patch block strategy is proposed to realize the parallel version on single machine multi‐GPUs using OpenMP+OpenACC, which greatly simplifies the construction of shadow area and the exchange of shadow area data. Experimental results show that 23 to 25 speedup is achieved for the single GPU at different scales over the serial program on Intel(R) Xeon(R) CPU E5‐2690 v4, and 6.37 speedup is achieved over the single GPU when the number of atoms reaches 2,097,152 on 8GPUs on single machine.

Raman spectroscopy provides chemical and compositional information that can serve as a structural fingerprint for various materials. Therefore, simulations of Raman spectra, including both quantum perturbation analyses and ground-state calculations, are of significant interest. However, highly accurate full quantum mechanical (QM) simulations of Raman spectra have previously been confined to small systems. For large systems such as biological materials, full QM simulations have an extremely high computational cost and remain challenging. In this work, robust new algorithms and advanced implementations on many-core architectures are employed to enable fast, accurate, and massively parallel full ab initio simulations of the Raman spectra of realistic biological systems containing up to 3006 atoms, with excellent strong and weak scaling. Up to a performance of 468.5 PFLOP/s in double-precision and 813.7 PLOPS/s in mixed-half precision is achieved on the new-generation Sunway high-performance computing system, suggesting the potential for new applications of the QM approach to biological systems.