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Training Computer Scientists for the Challenges of Hybrid Quantum-Classical computing

Training Computer Scientists for the Challenges of Hybrid Qu...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Vincenzo De Maio Meerzhan Kanatbekova Felix Zilk Nicolai Friis Tobias Guggemos Ivona Brandic HPC Laboratory Institute of Information Systems Engineering TU Wien Vienna Austria Atominstitut TU Wien Vienna Austria Faculty of Physics Vienna Center for Quantum Science and Technology (VCQ) University of Vienna Vienna Austria Christian Doppler Laboratory for Photonic Quantum Computer Faculty of Physics University of Vienna Vienna Austria

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.

关键词： Computer science Training Bridges Quantum system Cloud computing Quantum computing Scientific computing Hybrid power systems Standards

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Machine Learning based Outlier Detection in IoT Greenhouse

Machine Learning based Outlier Detection in IoT Greenhouse

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international symposium on Object-Oriented Real-Time distributed computing

作者： Aymen Abid Omar Cheikhrouhou Ghada Zaïbi Abdennaceur Kachouri CES-Lab ENIS University of Sfax Sfax Tunisia Higher Institute of Computer Science of Mahdia University of Monastir Tunisia National Engineering School of Monastir University of Monastir Tunisia AFD2E-Lab ENIS University of Sfax Sfax Tunisia

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Data Monitoring becomes mandatory for several IoT applications including smart greenhouse. It aims to increase the quality of information by identifying existing errors and anomalies, especially using outlier detection process by machine learning-data classification. Existing approaches require the knowledge of data characteristics in advance. However, this requirement is not always possible in IoT due to the heterogeneity of devices. Therefore, this paper provides VoteIoT: a new monitoring method based on data analytic and vote clustering outcome. In this way, we increase the probability of making a good decision and guaranteeing a good harvest of greenhouses. To evaluate the proposed solution, we used a real database extended by augmented data. The results show a good response time, below 0.01 seconds, as well as a good detection accuracy of 97%. Additionally, the false alarms are below 3% and therefore, a low useful data loss. Moreover, we have managed to increase the probability of a good decision compared to existing solutions.

关键词： Temperature measurement Data analysis Decision making distributed databases Machine learning Real-time systems Internet of Things

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Automating GPU Scalability for Complex Scientific Models: Phonon Boltzmann Transport Equation

Automating GPU Scalability for Complex Scientific Models: Ph...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Eric Heisler Siddharth Saurav Aadesh Deshmukh Sandip Mazumder Hari Sundar Kahlert School of Computing University of Utah Salt Lake City USA Mechanical and Aerospace Engineering The Ohio State University Columbus USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Heterogeneous computing environments combining CPU and GPU resources provide a great boost to large-scale scientific computing applications. Code generation utilities that partition the work into CPU and GPU tasks while considering data movement costs allow researchers to develop high-performance solutions more quickly and easily, and make these resources accessible to a larger user *** present developments for a domain-specific language (DSL) and code generation framework for solving partial differential equations (PDEs). These enhancements facilitate GPU-accelerated solution of the Boltzmann transport equation (BTE) for phonons, which is the governing equation for simulating thermal transport in semiconductor materials at sub-micron scales. The solution of the BTE involves thousands of coupled PDEs as well as complicated boundary conditions and solving a nonlinear equation that couples all of the degrees of freedom at each time step. These developments enable the DSL to generate configurable hybrid GPU/CPU code that couples accelerated kernels with user-defined code. We observed performance improvements of around 18X compared to a CPU-only version produced by this same DSL with minimal additional programming effort.

关键词： Codes Boltzmann equation Semiconductor materials Graphics processing units Phonons Switches Programming

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The Innovation Development of Data Prediction and Clustered Compressive Sensing (CCS) in Environmental Application

The Innovation Development of Data Prediction and Clustered ...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international Conference on

作者： Sonia Kukreja Garima Jain Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Business System Noida Institute of Engineering and Technology Greater Noida India

Data prediction in environmental applications is a quickly evolving field that provides tremendous potential for improving environmental management and decision-making. By leveraging the power of data, predictive models can be developed to identify potential environmental risks, mitigate the impacts of climate change, and improve the efficiency of environmental management. Data prediction in environmental applications can be used to better understand the relationships between environmental phenomena and the environment. For instance, predictive models can be used to identify areas that are more likely to experience extreme weather events and inform the development of strategies for responding to such events. Predictive models can also be used to analyze the effects of changing climate on ecosystems and help inform decisions regarding the management of natural resources. Data prediction in environmental applications can also be used to develop more effective management strategies. Predictive models can be used to determine which areas are more likely to experience water scarcity or air pollution, and which areas are more likely to benefit from certain conservation practices. By leveraging data, predictive models can also help predict how certain management practices will affect the environment and inform decision-makers on how to best allocate resources.

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Advanced Graph Convolutional Networks for Semantic Relationship Mining in Large-Scale Ontologies

Advanced Graph Convolutional Networks for Semantic Relations...

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Trends in Quantum computing and Emerging business Technologies (TQCEBT), international Conference on

作者： R Anuradha Swathi B Rakesh Kumar Amandeep Nagpal Ravi Kalra Hayder Saadoon Abdulaali Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering Applications GLA University Mathura Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research introduces novel methodologies in graph convolutional networks (GCNs) to enhance semantic relationship mining within large-scale ontologies. Traditional approaches to understanding and extracting meaningful patterns from ontological structures often struggle with the complexity and scale of data. The proposed framework leverages advanced GCNs, incorporating a series of innovative mechanisms designed to optimize performance, accuracy, and scalability. Firstly, an enhanced graph convolutional layer is introduced, specifically tailored to capture the nuanced hierarchical and relational data typical of extensive ontological datasets. This layer employs a multidimensional feature extraction technique, significantly improving the depth and quality of semantic relationship identification. Secondly, the paper presents a unique embedding algorithm that efficiently maps semantic and relational data into a lower-dimensional space, facilitating faster computation while retaining critical information. To address the challenges of scalability, a distributed processing architecture is also proposed, enabling the handling of ontologies at an unprecedented scale. Empirical evaluations demonstrate the superiority of the approach in terms of precision, recall, and computational efficiency compared to existing methods. This research contributes to the field by providing a robust tool for experts and practitioners involved in knowledge discovery, data mining, and semantic web technologies, paving the way for more intelligent and efficient ontology management.

关键词： Semantic Web distributed processing Scalability Semantics Computer architecture Ontologies Feature extraction

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Towards Accelerating k-NN with MPI and Near-Memory Processing

Towards Accelerating k-NN with MPI and Near-Memory Processin...

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IEEE international symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hooyoung Ahn Seonyoung Kim Yoomi Park Woojong Han Nick Contini Bharath Ramesh Mustafa Abduljabbar Dhabaleswar K. Panda Supercomputing Technology Research Center Electronics and Telecommunications Research Institute Department of Computer Science and Engineering The Ohio State University

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Message Passing Interface (MPI) is a common parallel programming model in High-Performance computing fields. Recently, it has been widely used in Artificial Intelligence (AI) applications. However, the performance of those applications is limited by the memory wall problem, which is the performance gap between the processor and memory. To address these problems, we propose a novel computing architecture for accelerating k-NN query, which is the key operation of AI applications. The proposed computing architecture involves two or more computing nodes within a rack using the memory of an NMP device as a communication buffer and the accelerator of the NMP device for MPI collective communication. The superiority of the proposed method is that it reduces data copying during local computation and eliminates the network cost of gathering local computation results for global computation. Furthermore, it can enhance performance by processing data in the device's memory through near-memory processing. However, since these NMP devices are still in development, with no commercially available products currently, we have undertaken the task of developing them ourselves. Despite these circumstances, to demonstrate the feasibility of our proposed approach, we have implemented it in a single-node environment without making any unreasonable assumptions and have compared it with conventional approaches. Although we are using a single-node environment, we have designed a more sophisticated architecture, analyzed the time complexity, and compared the estimated performance of both the conventional and proposed approaches. We can conclude that the proposed computing architecture is feasible, and we anticipate an even more performance enhancement in a multi-node environment.

关键词： Performance evaluation Costs Parallel programming Message passing High performance computing Conferences Computer architecture

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Low-Latency State Management for Real-Time Tasks in Edge Serverless

Low-Latency State Management for Real-Time Tasks in Edge Ser...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Yanyan Wen Guangping Xu Jianshe Wang Wei Hao School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Stateful serverless systems commonly adopt an architectural paradigm characterized by compute and storage separation within cloud data centers. Nevertheless, guaranteeing prompt response for real-time tasks at the edge becomes challenging due to network overheads. This paper introduces a Low-Latency state management framework for real-time tasks in edge serverless systems called LoLa, which adaptively places states proximate to functions, thereby mitigating delays in accessing states within edge serverless systems. Our approach aims at mitigating network latency and optimizing resource utilization by co-locating functions and states, thereby enhancing the system’s overall efficiency. We introduce an adaptive strategy to coordinate the migration of states. It dynamically adjusts the positions of states based on historical data and real-time feedback. Additionally, we designed an in-memory state storage mechanism to facilitate low-latency access and implement a lightweight and fine-grained state management to ensure stored state consistency. Evaluation results showcase the efficacy of LoLa in reducing state read and write latency within edge serverless systems. Specifically, the average response latency is observed to decrease by 65.2% and 38.1% in the best and worst-case scenarios, respectively.

关键词： Technological innovation distributed processing Data centers Heuristic algorithms Serverless computing Adaptive algorithms Real-time systems Delays Low latency communication Optimization

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Mining Relational Similarity in Social Networks for Enhanced Recommendations

Mining Relational Similarity in Social Networks for Enhanced...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Pengcheng Guo Qingyang Wang Jielin Jiang Siyu Wu Haolong Xiang Xinyue Ji Shengjun Xue Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China Silicon Lake College Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Social perception recommendation systems can effectively alleviate the user cold start problem by leveraging the side information of social networks. However, most social perception recommendation systems treat user relations as independently existing entities for learning, thereby overlooking potential connections between relations. Additionally, as the number of relations increases, it inevitably imposes a significant computational burden on the servers. To address these issues, we propose the Social perception recommendation based on relational clustering(SPRRC). SPRRC conducts relational clustering of social networks and projects knowledge graphs, effectively mining information about the similarity between relations. First, we cluster relationships in item knowledge graphs and social networks through unsupervised learning. After that, we use local weighted smoothing to aggregate the map information of the clustered items and social network virtual subgraphs respectively, and use the attention networks to learn the representation of users and items in the interactive bipartite graph. Finally, a large number of experiments have verified the high accuracy of our method, compared with the latest methods.

关键词： distributed processing Smoothing methods Social networking (online) Aggregates Knowledge graphs Bipartite graph Servers Artificial intelligence Recommender systems Unsupervised learning

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ViTa: Optimizing Vision Transformer Operators on Domain-Specific Accelerators

ViTa: Optimizing Vision Transformer Operators on Domain-Spec...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Pengyu Mu Yi Liu Yue Zhou Rui Wang School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Domain-specific devices accelerate the inference of deep learning models, whose performance is sensitive to hardware resources. Large artificial intelligence models, especially those involving self-attention, have extensive computational demands that challenge efficient execution on accelerators. Due to restricted search space on resource-constrained hardware, some approaches require significant engineering effort to develop platform-specific optimization code or find suboptimal programs with search-based automatic *** paper proposes ViTa, a framework for deploying vision Transformer models on resource-constrained hardware. First, ViTa adopts an analytical approach to modify the model structure, reducing computational load without sacrificing accuracy. Secondly, we divide computations into tiles and map them to computing units to enhance memory access and computational efficiency. Providing an abstract hardware layer to guide tiling and mapping can improve the model deployment efficiency. Experiments demonstrate that ViTa can reduce memory bandwidth by 43% and FLOPs by 56% while maintaining accuracy. Compared with the Vendor library, the inference time on various accelerators is reduced by an average of 24%.

关键词： Deep learning Computer vision Computational modeling Memory management Bandwidth Transformers Hardware Computational efficiency Optimization Load modeling

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Fatigue detection for public transport drivers under the normalization of epidemic prevention

Fatigue detection for public transport drivers under the nor...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Zhe Yu Lei Li Lijun Xu Kansong Chen School of Computer Science and Information Engineering Hubei University Wuhan P. R. China

ISBN: (纸本)9781665454636

Several studies have shown that fatigue driving is one of the important causes of public transport safety accidents. With the outbreak of the COVID-19, the wearing of masks by public transport drivers presents new challenges for computer-based visual fatigue detection. In order to achieve the goal of accurately capturing the landmark information of the face even when the face is occluded by a large area, we adopt the DNN-based face detection method which has the highest accuracy and the best occlusion resistance. When the driver's face is blocked, the landmark information of the blocked face can be accurately detected by using our optimized face landmark detector. The accuracy rate of landmark recognition can reach 97.80%. On this basis, we calculate the driver's eye information, mouth information and the driver's head deflection angle information in real time as the judgment indicators of the degree of fatigue to comprehensively evaluate the driver's fatigue state. And use mathematical methods to fuse indicators in real time, classify the driver's fatigue state according to the value of the fusion indicators, and adopt different early warning methods for different levels of fatigue. In addition, in order to further improve the accuracy of the detection results and exclude the influence of other facial behaviors on our fatigue judgment indicators, we propose a kinetic energy calculation formula for facial organs based on the improved optical flow method. According to the different kinetic energy of facial organs in different states, which can accurately distinguish the different behaviors of the same facial organs such as blinking and closing eyes, yawning and speaking, which significantly increases the robustness and generalization ability of the detection program. The final experimental results show that the correct rate of the method for determining the degree of fatigue of the driver and passengers can reach 98.40% and 92.30% respectively when the driver does no

关键词： Resistance Visualization Face recognition Fatigue Real-time systems Robustness Behavioral sciences

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