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A high ORDER SCHEME FOR FRACTIONAL DIFFERENTIAL EQUATIONS WITH THE CAPUTO-HADAMARD DERIVATIVE

Journal of Computational Mathematics 2025年第3期43卷 615-640页

作者： Xingyang Ye Junying Cao Chuanju Xu School of Science Jimei UniversityXiamen 361021China School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

In this paper,we consider numerical solutions of the fractional diffusion equation with theαorder time fractional derivative defined in the Caputo-Hadamard sense.A high order time-stepping scheme is constructed,analyzed,and numerically *** contribution of the paper is twofold:1)regularity of the solution to the underlying equation is investigated,2)a rigorous stability and convergence analysis for the proposed scheme is performed,which shows that the proposed scheme is 3+αorder *** numerical examples are provided to verify the theoretical statement.

关键词： Caputo-Hadamard derivative Fractional differential equations high order scheme Stability and convergence analysis

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Hybrid forecasting of demand flexibility: A top-down approach for thermostatically controlled loads

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Energy and AI 2025年 20卷

作者： Massidda, Luca Marrocu, Marino CRS4 Center for Advanced Studies Research and Development in Sardinia Italy National Research Center in High Performance Computing Big Data and Quantum Computing Italy

Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified. Historically, this balancing has been managed from the supply side. However, the shift towards renewable energy sources limits the controllability of traditional fossil fuel plants, increasing the importance of demand response (DR) techniques to achieve the required flexibility. Aggregators participating in flexibility markets need to accurately forecast the adaptability they can offer, a task complicated by numerous influencing variables. Based on a top-down approach, this study addresses the problem of forecasting electricity demand in the presence of flexibility from thermostatically controlled loads. We propose a hybrid model that combines data-driven techniques for probabilistic estimation of electricity consumption with a disaggregation of electricity consumption to identify the fraction of thermal loads, subject to flexibility, which is simulated by a virtual battery model. The technique is applied to a synthetic dataset that simulates the response of a European neighborhood to demand response interventions. The results demonstrate the model's ability to accurately predict both the reduction in electricity demand during DR events and the subsequent rebound in consumption. The model achieves a mean absolute percentage error (MAPE) lower than 17.0%, comparable to the accuracy without flexibility. The results obtained are compared with a direct data-driven approach, demonstrating the validity and effectiveness of our model. © 2025

关键词： Demand response

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Efficient simulation of discrete galaxy populations and associated radiation fields during the first billion years

arXiv

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arXiv 2025年

作者： Davies, James E. Mesinger, Andrei Murray, Steven Scuola Normale Superiore di Pisa Piazza dei Cavallieri 7 Pisa56126 Italy Centro Nazionale"High Performance Computing Big Data and Quantum Computing" Italy

Understanding the epochs of cosmic dawn and reionisation requires us to leverage multi-wavelength and multi-tracer observations, with each dataset providing a complimentary piece of the puzzle. To interpret such data, we update the public simulation code, 21cmFASTv4, to include a discrete source model based on stochastic sampling of conditional mass functions and semi-empirical galaxy relations. We demonstrate that our new galaxy model, which parametrizes the means and scatters of well-established scaling relations, is flexible enough to characterize very different predictions from hydrodynamic cosmological simulations of high-redshift galaxies. Combining a discrete galaxy population with approximate, efficient radiative transfer allows us to self-consistently forward-model galaxy surveys, line intensity maps (LIMs), and observations of the intergalactic medium (IGM). Not only does each observable probe different scales and physical processes, but cross-correlation will maximise the information gained from each measurement by probing the galaxy-IGM connection at high-redshift. We find that a stochastic source field produces significant shot-noise in 21cm and LIM power spectra. Scatter in galaxy properties can be constrained using UV luminosity functions and/or 21cm power spectra, especially if astrophysical scatter is higher than expected (as might be needed to explain recent JWST observations). Our modelling pipeline is both flexible and computationally efficient, facilitating high-dimensional, multi-tracer, field-level Bayesian inference of cosmology and astrophysics during the first billion years. © 2025, CC BY.

关键词： Dark Matter

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Detecting galaxy – 21-cm cross-correlation during reionization

arXiv

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arXiv 2025年

作者： Gagnon-Hartman, Samuel Davies, James Mesinger, Andrei Scuola Normale Superiore di Pisa Piazza dei Cavallieri 7 Pisa56126 Italy Centro Nazionale "High Performance Computing Big Data and Quantum Computing" Italy

The cosmic 21-cm signal promises to revolutionize studies of the Epoch of Reionization (EoR). Radio interferometers are aiming for a preliminary, low signal-to-noise (S/N) detection of the 21-cm power spectrum. Cross-correlating 21-cm with galaxies will be especially useful in these efforts, providing both a sanity check for initial 21-cm detection claims and potentially increasing the S/N due to uncorrelated residual systematics. Here we self-consistently simulate large-scale (1 Gpc3) galaxy and 21-cm fields, computing their cross-power spectra for various choices of instruments as well as survey properties. We use 1080h observations with SKA-low AA* and HERA-350 as our benchmark 21-cm observations. We create mock Lyman alpha narrow-band, slitless and slit spectroscopic surveys, using benchmarks from instruments such as Subaru HyperSupremeCam, Roman grism, VLT MOONS, ELT MOSAIC, and JWST NIRCam. We forecast the resulting S/N of the galaxy–21-cm cross-power spectrum, varying the galaxy survey area, depth and level of 21-cm foreground contamination for each pair of instruments. We find that the highest S/N is achievable through slitless, wide-area spectroscopic surveys, with the proposed Roman HLS survey resulting in a ∼ 55σ (∼ 13σ) detection of the cross power with 21-cm as observed with SKA-low AA* (HERA-350), for our fiducial model and assuming ∼500 sq. deg. of overlap. Narrow-band dropout surveys are unlikely to result in a detectable cross-power, due to their poor redshift localization. Slit spectroscopy can provide a high S/N detection of the cross power for SKA-low AA* observations. Specifically, the planned MOONRISE survey with MOONS on the VLT can result in a ∼ 3σ detection, while a survey of comparable observing time using MOSAIC on the ELT can result in a ∼ 4σ detection. Our results can be used to guide survey strategies, facilitating the detection of the galaxy–21-cm cross-power spectrum. © 2025, CC BY.

关键词： Signal to noise ratio

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IMPRESS: an importance-informed multi-tier prefix KV storage system for large language model inference 25

IMPRESS: an importance-informed multi-tier prefix KV storage...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Ruidong Zhang Siling Yang Ping Chen Yi Zheng Baoxing Huai Gang Chen The State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing The State Key Laboratory of Blockchain and Data Security Zhejiang University Huawei Cloud

ISBN: (纸本)9781939133458

Modern advanced large language model (LLM) applications often prepend long contexts before user queries to improve model output quality. These contexts frequently repeat, either partially or fully, across multiple queries. Existing systems typically store and reuse the keys and values of these contexts (referred to as prefix KVs) to reduce redundant computation and time to first token (TTFT). When prefix KVs need to be stored on disks due to insufficient CPU memory, reusing them does not always reduce TTFT, as disk I/O latency is high. In this paper, we propose IMPRESS, an importance-informed multi-tier prefix KV storage system to reduce I/O delay for LLM inference by only loading important prefix KVs. IMPRESS first leverages the insight that there is significant similarity in important token index sets across attention heads and introduces an I/O-efficient important KV identification algorithm. It then optimizes prefix KV storage and caching through importance-informed KV management, reducing TTFT during model inference. Our experimental results show that IMPRESS can reduce TTFT by up to 2.8× compared to state-of-the-art systems, while maintaining comparable inference accuracy.

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Robust and digital hyper-polarization protocol of nuclear spins via magic sequential sequence

arXiv

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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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LeapGNN: accelerating distributed GNN training leveraging feature-centric model migration 25

LeapGNN: accelerating distributed GNN training leveraging fe...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Xuechen Zhang The State Key Laboratory of Blockchain and Data Security Zhejiang University and Zhejiang Lab and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3.

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Federated Domain Generalization for Fault Diagnosis: Cross-Client Style Integration and Dual Alignment Representation

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IEEE Internet of Things Journal 2025年

作者： Wang, Yu Zhang, Guowei Kong, Xianguang Du, Jingli Ma, Hongbo Yunnan Agricultural University College of Big Data Kunming650500 China Xidian University College of Mechanical and Electrical Engineering Xian710071 China State Key Laboratory of Electromechanical Integrated Manufacturing of High-performance Electronic Equipments China

Federated learning (FL) enables the collaborative training of intelligent diagnostic models across multiple clients while preserving data privacy. However, the complex and dynamic working conditions often cause federated diagnostic models to suffer from performance degradation when applied to unseen clients, exposing deficiencies in model generalization. Domain generalization fault diagnosis methods aim to enhance model adaptability under unknown working conditions, but most existing algorithms rely on shared data, making them unsuitable for direct application in federated learning environments. To address this challenge, we propose a federated domain generalization fault diagnosis method based on Cross-Client Style Integration and Dual Alignment representation (CCSIDA). This method significantly enhances model generalization in FL settings while ensuring data privacy. Specifically, CCSIDA integrates three key strategies: cross-client style data enhancement, domain-sensitive feature suppression, and predictive alignment. For cross-client style integration, we design a stochastic mixed-style sample generation module to overcome the limitations of conventional domain generalization data enhancement methods in FL. This module generates diverse samples by constructing a shared style repository, enabling inter-client style information transfer. For cross-client dual alignment representation, we introduce a domain-sensitive feature suppression strategy to reduce domain discrepancies between original and enhanced samples. Additionally, Jensen-Shannon divergence is employed to ensure prediction consistency between these samples. Finally, extensive experiments on five benchmark datasets validate the effectiveness of the proposed method. © 2014 IEEE.

关键词： Federated learning

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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IEEE Symposium on high-performance Computer Architecture

作者： Siling Yang Shuibing He Wenjiong Wang Yanlong Yin Tong Wu Weijian Chen Xuechen Zhang Xian-He Sun Dan Feng The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Lab Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver Illinois Institute of Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work.

关键词： Training Accuracy Graph convolutional networks Pipelines Energy conservation Computer architecture Machine learning Parallel processing Optimization

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A Method for Constructing Research data Provenance in high-performance computing Systems 19

A Method for Constructing Research Data Provenance in High-P...

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Namiki, Yuta Hosomi, Takeo Tanushi, Hideyuki Yamashita, Akihiro Date, Susumu Osaka University Jt. Res. Laboratory for Integrated Infrastructure of High Performance Computing and Data Analysis Cybermedia Center Osaka Japan Nec Corporation Tokyo Japan

ISBN: (纸本)9798350322231

Research must be reproducible to be verifiable. Provenance, which describes how data was produced, is one of the metadata that can improve reproducibility. In this paper, we propose a method to construct the provenance of research data produced in high-performance computing (HPC) systems. Our method can construct a high-level and user-perspective provenance by integrating information available in HPC systems, such as a workload manager, with low-level data about running programs' behavior captured in an operating system kernel. The method enables users of HPC systems to collect the provenance without modifying assets such as programs and scripts. © 2023 IEEE.

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