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A high-performance matrix transposition for a new MIMD architecture processor PEZY-SC3s

CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2025年 1-13页

作者： Liang, Yaling Wang, Qinglin Yang, Shun Xia, Rui Guo, Weihao Liu, Jie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Matrix transposition is a vital kernel widely used in various fields. However, its memory-intensive nature leads to significant memory access conflicts, making it a performance bottleneck. Therefore, optimizing matrix transposition algorithms based on architectural features is crucial for improving the performance of related applications and enhancing system resource utilization. The PEZY-SC3s, a new MIMD (Multiple Instruction Multiple Data) architecture processor, possesses numerous cores and supports SIMD instructions, demonstrating tremendous potential for high-performance computing. However, no matrix transposition algorithm currently exists tailored to the PEZY-SC3s architecture to leverage its computing potential fully. We propose a high-performance matrix transposition algorithm for PEZY-SC3s. First, we block the matrix according to the cache architecture at the microkernel level to improve the memory access pattern. Then, we separate read and write operations by utilizing the PEZY-SC3s' Local Memory, solving the cache line contention. Finally, we design various processor-level parallel strategies and implement a dynamic selection strategy based on a performance heuristic algorithm for different matrix shapes, alleviating bank conflict and enhancing performance. Experimental results show that our implementation achieves an average speedup of 17.27 times across 60 matrices compared to the baseline algorithm, with a maximum bandwidth utilization of 87.7%.

关键词： Matrix transposition MIMD parallel algorithm PEZY-SC3s

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Advancing RFID Technology for Virtual Boundary Detection

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3407-3422页

作者： Li, Xiaoyu Liu, Jia Lin, Zihao Liu, Xuan Wang, Yanyan Zhang, Shigeng Ye, Baoliu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 210024 Peoples R China Cent South Univ Sch Comp Sci & Engn Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410017 Peoples R China

A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.

关键词： Image edge detection Robots Radiofrequency identification Calibration Mobile computing Radio frequency Robot sensing systems Location awareness Interference Wire Boundary detection Internet of Things RFID virtual boundary

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A pseudo-time stepping and parameterized physics-informed neural network framework for Navier-Stokes equations

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PHYSICS OF FLUIDS 2025年第3期37卷

作者： Zhang, Zhuo Xiong, Xiong Zhang, Sen Wang, Wei Yang, Xi Zhang, Shilin Yang, Canqun Natl Univ Def Technol Coll Comp Sci & Technol Changsha Hunan Peoples R China Northwestern Polythechn Univ Sch Math & Stat Xian 710129 Peoples R China Natl SuperComp Ctr Tianjin Tianjin Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Xiangtan Univ Sch Math & Computat Sci Xiangtan Peoples R China Haihe Lab ITAI West Middle Ring Expy & Binyu Ave Intersect Northw Tianjin Peoples R China

Physics-informed neural networks (PINNs) have emerged as a popular approach in scientific machine learning for solving both forward and inverse problems of partial differential equations (PDEs). However, complex physical systems are often characterized by parameters, such as viscosity and Reynolds number in fluid dynamics, which pose significant challenges for parameterized PDE solutions. The inherent limitations of PINNs include the need for repeated and time-consuming training under varying parameter conditions, and the minimization of PDE residuals with PDE-based soft constraints, which makes the "ill-conditioned" problem. To address these issues, this paper proposes an innovative framework: the pseudo-time stepping and parameterized physics-informed neural network (P2PINN). P2PINN leverages the explicit encoding of only two PDE parameters' latent representations to enable efficient interpolation and extrapolation across a wide range of parameters. By integrating the pseudo-time stepping method with deep learning, the framework significantly alleviates the ill-conditioned problem. We validated our method in the context of the Navier-Stokes equations, and experimental results demonstrate that P2PINN achieves solution speedups of up to 2-4 orders of magnitude compared to baseline PINNs and their variants, while also surpassing them in accuracy.

关键词： Navier Stokes equations

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A unified deep framework for peptide-major histocompatibility complex-T cell receptor binding prediction

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NATURE MACHINE INTELLIGENCE 2025年第4期7卷 1-11页

作者： Zhao, Yunxiang Yu, Jijun Su, Yixin Shu, You Ma, Enhao Wang, Jing Jiang, Shuyang Wei, Congwen Li, Dongsheng Huang, Zhen Cheng, Gong Ren, Hongguang Feng, Jiannan Beijing Inst Biotechnol Lab Adv Biotechnol Beijing Peoples R China Beijing Inst Pharmacol & Toxicol Beijing Key Lab Therapeut Gene Engn Antibody Beijing Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China Tsinghua Univ Sch Basic Med Sci Beijing Peoples R China Jilin Univ Coll Math Changchun Peoples R China Shenzhen Bay Lab Inst Infect Dis Shenzhen Peoples R China

Antigen peptides that are presented by a major histocompatibility complex (MHC) and recognized by a T cell receptor (TCR) have an essential role in immunotherapy. Although substantial progress has been made in predicting MHC presentation, accurately predicting the binding interactions between antigen peptides, MHCs and TCRs remains a major computational challenge. In this paper, we propose a unified deep framework (called UniPMT) for peptide, MHC and TCR binding prediction to predict the binding between the peptide and the CDR3 of TCR beta in general, presented by class I MHCs. UniPMT is comprehensively validated by a series of experiments and achieved state-of-the-art performance in the peptide-MHC-TCR, peptide-MHC and peptide-TCR binding prediction tasks with up to 15% improvements in area under the precision-recall curve taking the peptide-MHC-TCR binding prediction task as an example. In practical applications, UniPMT shows strong predictive power, correlates well with T cell clonal expansion and outperforms existing methods in neoantigen-specific binding prediction with up to 17.62% improvements in area under the precision-recall curve on experimentally validated datasets. Moreover, UniPMT provides interpretable insights into the identification of key binding sites and the quantification of peptide-MHC-TCR binding probabilities. In summary, UniPMT shows great potential to serve as a useful tool for antigen peptide discovery, disease immunotherapy and neoantigen vaccine design.

关键词： Peptides

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Research on application of heterogeneous resonance Integral for double heterogeneous system

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ANNALS OF NUCLEAR ENERGY 2025年 212卷

作者： Qin, Shuai Zhang, Qian Wang, Kai Huang, Dong Li, Song Liang, Yuechao Zhejiang Univ Zhejiang Inst Modern Phys Sch Phys Lab Adv Nucl Energy Theory & Applicat Hangzhou 310027 Zhejiang Peoples R China Xi An Jiao Tong Univ Sch Elect Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Naval Univ Engn Coll Nucl Sci & Technol Wuhan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China

The Double Heterogeneous (DH) system, where fuel particles are randomly dispersed in the non-fissile matrix, is challenging for the reactor physics calculation. The Sanchez-Pomraning method accurately handles the DH system, but integrating it into existing reactor physics code requires code development. This study adopts the Sanchez-Pomraning coupled Ultra-Fine-Group (SP-UFG) slowing-down calculation to generate the heterogeneous Resonance Integral (RI) for DH system treatment with simple volume homogenization. Fully Ceramic Microencapsulated (FCM) fuel pin-cells and plates with varying configurations are calculated for verification. Effective cross-sections (XSs) and keff calculated by the heterogeneous RI are compared with SP-UFG results. Results show that the maximum bias of XSs and keff caused by the XS biases are less than 5% and 200 pcm, respectively. The maximum bias of keff when compared with Monte Carlo calculated results is -213 pcm, demonstrating that only considering the DH effect in the resonance energy region is acceptable.

关键词： Double heterogeneous Heterogeneous resonance integral Sanchez-Pomraning method Ultra-fine-group Subgroup parameter

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G0S2 modulates normal vitreous-induced proliferation in endothelial cells

COMMUNICATIONS BIOLOGY

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COMMUNICATIONS BIOLOGY 2025年第1期8卷 1-12页

作者： Yin, Yiwei Pu, Li Yang, Xi Zhu, Ying Chen, Fang Wu, Chenkun Lei, Hetian Wu, Wenyi Cent South Univ Xiangya Hosp Dept Ophthalmol Hunan Key Lab Ophthalmol Changsha Peoples R China Xiangya Hosp Natl Clin Res Ctr Geriatr Disorders Changsha Peoples R China Shenzhen Childrens Hosp Dept Pharm Shenzhen Peoples R China Guiyang Aier Eye Hosp Dept Ophthalmol Guiyang Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha Hunan Peoples R China Cent South Univ Xiangya Hosp Sch Life Sci Huan Key Lab Mol Precis Med Changsha Peoples R China Cent South Univ Sch Life Sci Hunan Key Lab Med Genet Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha Hunan Peoples R China Xinxiang Med Univ Affiliated Hosp 3 Dept Ophthalmol Xinxiang Peoples R China

Abnormal blood vessel growth in the eye is a leading cause of vision loss globally, particularly in diseases like diabetic retinopathy where the vitreous plays a crucial but poorly understood role in disease progression. While we know the vitreous can stimulate blood vessel growth, the specific molecular mechanisms remain unclear. Here we show that a protein called G0S2 (G0/G1 switch gene 2) serves as a key regulator of blood vessel growth in response to normal vitreous. Through comprehensive gene analysis, we discovered that G0S2 levels increase significantly when blood vessel cells are exposed to normal vitreous. The importance of G0S2 is highlighted by our finding that uveal melanoma patients with higher G0S2 levels had poorer survival rates. When we removed G0S2 from blood vessel cells, they no longer responded to vitreous stimulation, confirming its critical role. Notably, we identified an existing drug that can target G0S2, potentially offering a new therapeutic approach. This discovery of G0S2's role and its potential therapeutic targeting opens new avenues for treating eye diseases characterized by abnormal blood vessel growth, while also providing a valuable biomarker for predicting disease progression in eye cancer patients.

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Cooperative Localization Using Expected Minimum Segment for Irregular Multi-Hop Networks

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IEEE TRANSACTIONS ON NETWORKING 2025年第1期33卷 369-381页

作者： Yan, Xiaoyong Cao, Jiannong Zhang, Shigeng Ding, Chuntao Wu, Chenhuang Liu, Alex X. Song, Aiguo Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China Putian Univ Key Lab Appl Math Putian 351200 Fujian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Sci & Technol Parallel & Distributed Proc Lab PDL Changsha 410003 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Putian Univ Key Lab Appl Math Putian 351200 Fujian Peoples R China Midea Grp Foshan 528311 Guangdong Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 211102 Peoples R China

For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minimum segments (LEMS, for short) is proposed in this paper. LEMS begins by measuring the distance between paired nodes, which is completed along with network initialization. Then, each unlocated node constructs its own sub-network, including it, based on the error characteristics among anchor nodes. Finally, each unlocated node searches for its estimated location in its sub-region based on the objective function generated by the chaotic mapping. Simulation results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art regarding efficiency, accuracy, and stability for various irregular networks. Specifically, our proposed algorithm achieves a median improvement in localization accuracy of 0.62 to 29.57 times and a reduction in the range of localization errors of 0.06 to 16.8 times.

关键词： Location awareness Accuracy Routing Spread spectrum communication Measurement uncertainty Linear programming Global navigation satellite system Geometry Estimation Particle swarm optimization Cooperative localization expected minimum segment error analysis chaotic mapping

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引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie Guangxi Univ Sch Comp Elect & Informat Nannig Peoples R China Network Technol Guangxi Key Lab Multimedia Commun Nanning Peoples R China Guangxi Univ Key Lab Parallel Distributed & Intelligent Comp Nanning Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Polish Acad Sci Syst Res Inst Warsaw Poland Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye Xidian Univ Sch Elect Engn Xian Peoples R China Cent South Univ Sch Elect Engn Changsha Peoples R China Dalian Maritime Univ Natl Engn Res Ctr Maritime Nav Syst Dalian Peoples R China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Communications satellite Satellite ground networking Feed-switching Similarity assessment Evolutionary algorithms

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VoiceStyle: Voice-Based Face Generation via Cross-Modal Prototype Contrastive Learning

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第9期20卷 1-23页

作者： Chen, Wuyang Zhu, Boqing Xu, Kele Dou, Yong Feng, Dawei Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

Can we predict a person's appearance solely based on their voice? This article explores this question by focusing on generating a face from an unheard voice segment. Our proposed method, VoiceStyle, combines cross-modal representation learning with generation modeling, enabling us to incorporate voice semantic cues into the generated face. In the first stage, we introduce cross-modal prototype contrastive (CMPC) learning to establish the association between voice and face. Recognizing the presence of false negative and deviate positive instances in real-world unlabeled data, we not only use voice-face pairs in the same video but also construct additional semantic positive pairs through unsupervised clustering, enhancing the learning process. Moreover, we recalibrate instances based on their similarity to cluster centers in the other modality. In the second stage, we harness the powerful generative capabilities of StyleGAN to produce faces. We optimize the latent code in StyleGAN's latent space, guided by the learned voice-face alignment. To address the importance of selecting an appropriate starting point for optimization, we aim to automatically find an optimal starting point by utilizing the face prototype derived from the voice input. The entire pipeline can be implemented in a self-supervised manner, eliminating the need for manually labeled annotations. Through extensive experiments, we demonstrate the effectiveness and performance of our VoiceStyle method in both cross-modal representation learning and voice-based face generation.

关键词： Cross-modal representation learning contrastive learning StyleGAN face generation

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Self-Heating Conductive Ceramic Composites for High Temperature Thermal Energy Storage

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ACS ENERGY LETTERS 2025年第2期10卷 1002-1012页

作者： Yang, Lin Peng, Peng Weger, Nathaniel Mills, Sean Messeri, Clement Menon, Akanksha K. Zeltmann, Steven Babbe, Finn Zheng, Qiye Dun, Chaochao Zhang, Chuan Urban, Jeffrey J. Minor, Andrew M. Prasher, Ravi Breunig, Hanna Lubner, Sean Peking Univ Dept Adv Mfg & Robot Coll Engn Beijing Peoples R China Lawrence Berkeley Natl Lab Energy Storage & Distributed Resources Div Berkeley CA 94720 USA Chinese Acad Sci Inst Proc Engn State Key Lab Mesosci & Engn Beijing 100190 Peoples R China Lawrence Berkeley Natl Lab Energy Anal & Environm Impacts Div Berkeley CA 94720 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Univ Calif Berkeley Dept Mat Sci & Engn Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Natl Ctr Electron Microscopy Berkeley CA 94720 USA Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Lawrence Berkeley Natl Lab Joint Ctr Artificial Photosynth Chem Sci Div Berkeley CA 94720 USA Hong Kong Univ Sci Dept Mech Engn Hong Kong 999077 Peoples R China Lawrence Berkeley Natl Lab Mol Foundry Berkeley CA 94720 USA Peking Univ Insitute Energy Beijing Peoples R China Boston Univ Dept Mech Engn Boston MA 02215 USA Boston Univ Div Mat Sci & Engn Boston MA 02215 USA

The absence of affordable and deployable large-scale energy storage poses a major barrier to providing zero-emission energy on demand for societal decarbonization. High temperature thermal energy storage is one promising option with low cost and high scalability, but it is hindered by the inherent complexity of simultaneously satisfying all of the material requirements. Here we design a class of ceramic-carbon composites based on co-optimizing mechanical, electrical, and thermal properties. These composites demonstrate stability in soak-and-hold tests and direct self-heating up to 1,936 degrees C and 750 thermal cycles from 500 to 1,630 degrees C without degradation. This thermal performance derives from their composition and microstructural design as verified by in situ high-temperature transmission electron microscopy and X-ray diffraction. They offer both higher energy density and lower cost than conventional storage technologies with a projected system Levelized Cost of Storage below the U.S. Department of Energy's 2030 target 5 cent/kWh (electric).

关键词： Carbon carbon composites

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