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25th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

This paper examines how co-locating multiple VMs on a single physical server with shared storage impacts I/O performance, specifically focusing on latency of I/O operations, and the overall throughput. We introduce a Proportional-Integral-Derivative (PID) controller to maintain I/O latency close to a predefined target by dynamically adjusting I/O requests based on real-time measurements. Through a series of experiments, we evaluated the effectiveness of our PID controller in comparison to VMware’s default QoS mechanism within a private cloud environment. The results indicate that the PID controller significantly enhances performance for latency-critical workloads, achieving substantially higher compliance with latency targets compared to the default mechanism. However, this prioritization of high-priority VMs comes at the expense of increased latency and reduced throughput for lower-priority workloads during periods of I/O contention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three term control systems

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Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

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25th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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Containerized data-Flow Processing for Scalable Real-Time Analytics on Edge Devices 25th

Containerized Data-Flow Processing for Scalable Real-Time A...

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25th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Y. Zomaya, Albert School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9789819642069

The increasing demand for real-time data analysis in Internet of Things (IoT) ecosystems has created several challenges, particularly in environments where resources are limited, and minimizing data processing latency is critical. This paper investigates the use of Linux Containerization (LXC) as a lightweight, resource-efficient alternative to traditional virtual machine managers for accelerating data-flow processing applications. We propose an architecture that integrates Linux Containerization (LXC) with a Proportional-Integral-Derivative (PID) controller to dynamically manage I/O resource allocation, ensuring Quality of Service (QoS) for I/O-intensive data analytics workloads. The proposed PID controller continuously monitors system performance and dynamically adjusts I/O resource allocation to mitigate performance degradation. Through a series of experiments, we demonstrate that LXC-based solutions outperform traditional methods in managing I/O-intensive data analytics workloads in IoT and edge ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Situational Scene Graph for Structured Human-Centric Situation Understanding

Situational Scene Graph for Structured Human-Centric Situati...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura College of Computing and Data Science Nanyang Technological University Singapore Centre for Frontier Ai Research Agency for Science Technology and Research Singapore Institute of High-Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331510831

Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. Code and data are available at https://***/LUNAProject22/SSG. © 2025 IEEE.

关键词： Human engineering

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

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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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Can LLMs Perform Structured Graph Reasoning Tasks? 27th

Can LLMs Perform Structured Graph Reasoning Tasks?

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Agrawal, Palaash Vasania, Shavak Tan, Cheston Center for Frontier AI Research Agency for Science Technology and Research Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore UWCSEA Dover High School Singapore Singapore

ISBN: (纸本)9783031781711

Pretrained Large Language Models (LLMs) have demonstrated various reasoning capabilities through language-based prompts alone, particularly in unstructured task settings (i.e. tasks purely based on language semantics). However, LLMs often struggle with structured tasks, because of the inherent incompatibility of input representation. Reducing structured tasks to uni-dimensional language semantics often renders the problem trivial. Keeping the trade-off between LLM compatibility and structure complexity in mind, we design various graph reasoning tasks as a proxy to semi-structured tasks in this paper, in order to test LLMs’ ability to use representations beyond plain text. In particularly, we design 10 distinct problems of graph traversal, each representing increasing levels of complexity, and benchmark 5 different instruct-finetuned LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2 and Palm-2) on the aforementioned tasks. Further, we analyse the performance of models across various settings such as varying sizes of graphs, as well as different forms of k-shot prompting. We highlight various limitations, biases and properties of LLMs through this benchmarking process, such as an inverse relation to the average degrees of freedom of traversal per node in graphs, the overall negative impact of k-shot prompting on graph reasoning tasks, and a positive response bias which prevents LLMs from identifying the absence of a valid solution. Finally, we introduce a new prompting technique specially designed for graph traversal tasks (PathCompare), which demonstrates a notable increase in the performance of LLMs in comparison to standard prompting techniques such as Chain-of-Thought (CoT) (The code for reproducing the results of this paper can be found at https://***/PalaashAgrawal/LLMGraphTraversal). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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swDarknet: A Heterogeneous Parallel Deep Learning Framework Suitable for SW26010 Pro Processor 20th

swDarknet: A Heterogeneous Parallel Deep Learning Framework ...

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20th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2024

作者： Liu, Huazeng Tian, Min Wu, Xiaoming Yang, Meihong Ren, Zenghui Li, Yanlong Jinan China Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819628292

As the size of AI models grows dramatically to achieve higher accuracy, more and more models are trained on supercomputers with massive computing and memory. However, it is still challenging to accelerate deep learning applications on supercomputer systems with many-core architectures. In this paper, we propose swDarknet: a Darknet-based heterogeneous parallel deep learning framework on the SW26010 Pro processor. First, We designed three heterogeneous parallel DNN layers: adaptive block-splitting strategy for matrix-multiplication layer, optimized memory access for convolutional layer, and block-based parallel pooling layer. Second, we proposed a model averaging (MA) method to efficiently scale swDarknet across many processors. Finally, we evaluated the performance of swDarknet on a core group. As compared with darknet, the convolutional layers achieved an average speedup of 32x. When training AlexNet and VGG-16, swDarknet can achieve up to 36.79× and 181.35× speedup, respectively. © IFIP International Federation for Information Processing 2025.

关键词： Supercomputers

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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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Benchmarking data Heterogeneity Evaluation Approaches for Personalized Federated Learning

Benchmarking Data Heterogeneity Evaluation Approaches for P...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Li, Zhilong Wu, Xiaohu Tang, Xiaoli He, Tiantian Ong, Yew-Soon Chen, Mengmeng Liu, Qiqi Lao, Qicheng Yu, Han Beijing University of Posts and Telecommunications Beijing China College of Computing and Data Science Nanyang Technological University Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Singapore School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9783031822391

There is growing research interest in measuring the statistical heterogeneity of clients’ local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors are taking place in silos and there is a lack of a unified benchmark to provide a fair and convenient comparison among various approaches in common settings. We aim to bridge this important gap in this paper. The proposed benchmarking framework currently includes six representative approaches. Extensive experiments have been conducted to compare these approaches under five standard non-IID FL settings, providing much needed insights into which approaches are advantageous under which settings. The proposed framework offers useful guidance on the suitability of various data divergence measures in FL systems. It is beneficial for keeping related research activities on the right track in terms of: (1) designing PFL schemes, (2) selecting appropriate data heterogeneity evaluation approaches for specific FL application scenarios, and (3) addressing fairness issues in collaborative model training. The code is available at https://***/Xiaoni-61/DH-Benchmark. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Enhancing Medical Diagnosis with Fine-Tuned Large Language Models: Addressing Cardiogenic Pulmonary Edema (CPE) 24

Enhancing Medical Diagnosis with Fine-Tuned Large Language M...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Chiu, Yen-Jung Chuang, Chao-Chun Hwa, Kuo-Yuan Department of Biomedical Engineering Ming Chuan University Taipei Taiwan National Center For High-Performance Computing Hsinchu Taiwan Department of Molecular Science and Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798400718274

Large Language Models (LLMs) have revolutionized natural language processing (NLP) with significant advancements in text generation. LLMs often struggle with complex domain-specific tasks, such as medical report analysis, despite their capabilities. This study focuses on enhancing LLM performance for medical applications, particularly in diagnosing and managing cardiogenic pulmonary edema (CPE). This research explores fine-tuning LLMs to develop a real-time CPE chatbot for Intensive Care Units (ICUs). The chatbot aims to provide diagnostic suggestions and explanations based on patient data. In the results, the LLaMa3-8B model performed better in predicting patients' CPE stage and keyword extraction. The accuracies achieved 72% and 86%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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