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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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A Graph-Based Method for AI Chip Operator Optimization Using Deep Learning 6

A Graph-Based Method for AI Chip Operator Optimization Using...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Yan, Jiapeng Zheng, Qilong School of Computer Science Hefei China National High Performance Computing Center Hefei China

ISBN: (纸本)9798350316520

AI operators refer to reusable code programs encapsulated in AI chip frameworks that implement specific functions. To achieve high performance, it is necessary to combine hardware characteristics efficiently when programming. However, for operators with multiple computational steps, it is challenging to develop excellent scheduling strategies. To address this issue, this paper proposes a graph-based method for AI chip operator optimization. Firstly, establish bidirectional transformation relationship between operators and corresponding computation graphs. Then, use deepwalk and word2vec to convert operators' computation graphs into embedding representations, and optimize corresponding operators by annotating the nodes using graph neural network. Also, simple operator fusion can be achieved by fusing graphs of multiple operators and optimizing the fused operator. Through the creation of an operator dataset and related experiments within the Cambricon community framework, this method demonstrates superior optimization and fusion of element-wise operators compared to other simple tuning methods. © 2024 IEEE.

关键词： Graphic methods

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Prediction of job characteristics for intelligent resource allocation in HPC systems:a survey and future directions

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Frontiers of computer science 2022年第5期16卷 17-33页

作者： Zhengxiong HOU Hong SHEN Xingshe ZHOU Jianhua GU Yunlan WANG Tianhai ZHAO Center for High Performance Computing School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China

Nowadays,high-performance computing(HPC)clusters are increasingly *** volumes of job logs recording many years of operation traces have been *** the same time,the HPC cloud makes it possible to access HPC services *** executing applications,both HPC end-users and cloud users need to request specific resources for different workloads by *** users are usually not familiar with the hardware details and software layers,as well as the performance behavior of the underlying HPC *** is hard for them to select optimal resource configurations in terms of performance,cost,and energy ***,how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC *** of job characteristics plays a key role for intelligent resource *** paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC *** first review the existing techniques in obtaining performance and energy consumption data of *** we survey the techniques for single-objective oriented predictions on runtime,queue time,power and energy consumption,cost and optimal resource configuration for input jobs,as well as multi-objective oriented *** conclude after discussing future trends,research challenges and possible solutions towards intelligent resource allocation in HPC systems.

关键词： high-performance computing performance prediction job characteristics intelligent resource allocation cloud computing machine learning

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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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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Geo-Distributed Analytical Streaming Architecture for IoT Platforms

Geo-Distributed Analytical Streaming Architecture for IoT Pl...

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2024 IEEE International Conference on Cluster computing, CLUSTER 2024

作者： Hoseiny Farahabady, M. Reza Zomaya, Albert Y. The University of Sydney School of Computer Science Center for Distributed and High Performance Computing SydneyNSW2006 Australia

ISBN: (纸本)9798350358711

The surge in real-time IoT data introduces scalability and computational challenges, necessitating advanced architectural and technological solutions. Streamed data processing is increasingly adopted across industries to enhance operational efficiency by extracting insights from vast, unstructured datasets. However, complex analytical tasks, such as multi-join queries, often require stateful iterative calculations on high-volume, high-velocity data, which challenges conventional programming models like MapReduce. This paper introduces an architectural model enabling application developers to create intricate streaming computational logic within an IoT platform. Our architecture supports scalable applications across distributed edge-tier nodes, particularly for iterative analytical operations on streamed data. We discuss core concepts and a timestamp model (borrowed from the timely data-flow concept) attached to data items circulating between computational blocks, which can execute concurrently on different edge-tier nodes. Additionally, we detail a buffer management mechanism that dynamically adjusts memory size in each computational block on nodes with limited capacity. This mechanism considers application performance requirements and runtime conditions to optimize buffer sizes. performance evaluation against cloud-tier alternatives confirms the effectiveness of our solution. Experimental results show a significant reduction in p-99 delay compared to cloud-tier deployment with a database engine for analytical applications involving multi-join operations. © 2024 IEEE.

关键词： MapReduce

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Caterpillar-like 3D graphene nanoscrolls@CNTs hybrids decorated with Co-doped MoSe_(2)nanosheets for electrocatalytic hydrogen evolution

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Journal of Materials science & Technology 2023年第5期136卷 43-53页

作者： Hao Luo Hongfei Gao Xudong Zhang Fan Yang Chen Liu Kewei Xu Dagang Guo State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049China

Hydrogen is a clean and flexible energy carrier that has the promising to satisfy urgent demands of the energy crisis and environmental *** hydrogen evolution reaction(HER),a critical half-reaction in water splitting,is one of the greenest and most common methods to obtain high-purity *** preeminent activity and stability electrocatalysts for hydrogen precipitation reac-tion(HER)to reduce energy consumption is of great essential.3D carbon-based materials have attracted widespread concern as the potential scaffolds of highly active and durable electrocatalysts for *** boost the HER activity and prolong the lifespan of electrocatalysts,multifarious 3D carbon architectures make an appearance to be engineered for accelerating electronic/mass transfer and maximizing the expo-sure of active ***,we designed and fabricated high-performance electrocatalysts based on a spe-cial caterpillar-like 3D graphene nanoscrolls@CNTs(GNS@CNTs)scaffold decorated with Co-doped MoSe_(2)nanosheets for *** the caterpillar-like hierarchical structure,CNTs were seamlessly co-bonded and dilated the interlayer and outer spacing of GNS through CVD growth technology,and nickel nanoparticles were covered by the CNTs *** advantage of the plentiful hierarchical pore,larger specific surface area,and higher chemical stability of the caterpillar-like structure,the catalysts exhibited enhanced elec-trocatalytic properties than some existing data *** functional theory calculations showed that the encapsulated nickel nanoparticle could tune the electronic structure of the outer anchored Co-doped MoSe_(2)and optimize itsG of H∗adsorption by electron traversing effect and doping *** indicate that caterpillar-like GNS@CNT is an ideal scaffold f or anchoring actives substance and is suit-able for high-efficient *** study provides new insights for designing hierarchical carbon composite nanostructures for catalysts,sensors,

关键词： Electron traversing effect 3D graphene Graphene nanoscrolls CNT MoSe_(2),Hydrogen evolution reaction

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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