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Towards Computational Performance Engineering for Unsupervised Concept Drift Detection: Complexities, Benchmarking, Performance Analysis 13

Towards Computational Performance Engineering for Unsupervis...

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13th international conference on Data Science, Technology and applications, DATA 2024

作者： Werner, Elias Kumar, Nishant Lieber, Matthias Torge, Sunna Gumhold, Stefan Nagel, Wolfgang E. Technische Universität Dresden Germany Dresden Leipzig Germany Technische Universität Dresden Germany Technische Universität Dresden Germany

ISBN: (纸本)9789897587078

Concept drift detection is crucial for many AI systems to ensure the system’s reliability. these systems often have to deal with large amounts of data or react in real-time. thus, drift detectors must meet computational requirements or constraints with a comprehensive performance evaluation. However, so far, the focus of developing drift detectors is on inference quality, e.g. accuracy, but not on computational performance, such as runtime. Many of the previous works consider computational performance only as a secondary objective and do not have a benchmark for such evaluation. Hence, we propose and explain performance engineering for unsupervised concept drift detection that reflects on computational complexities, benchmarking, and performance analysis. We provide the computational complexities of existing unsupervised drift detectors and discuss why further computational performance investigations are required. Hence, we state and substantiate the aspects of a benchmark for unsupervised drift detection reflecting on inference quality and computational performance. Furthermore, we demonstrate performance analysis practices that have proven their effectiveness in High-Performance computing, by tracing two drift detectors and displaying their performance data. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： real time systems

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Envyr: Instant Execution with Smart Inference 15

Envyr: Instant Execution with Smart Inference

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15th international conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / the 7th international conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Chaudhry, Tanmay Garg, Abhay Pathak, Yogesh Adobe Systems Pvt Ltd Noida India

this paper introduces a novel framework that eliminates the often cumbersome "build and install" step when running software. Our framework packages a collection of techniques to automatically infer and generate sandboxes, specifically Linux containers, for running applications directly from their source code. this approach significantly simplifies software execution, improving developer productivity and promoting reuse. We demonstrate the framework's effectiveness through real-world examples and use cases implemented in Python, NodeJS, and Bash. © 2024 Elsevier B.V.. All rights reserved.

关键词： Python

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PSeer: Performance Prediction for Partially Co-located Distributed Deep Learning 23

PSeer: Performance Prediction for Partially Co-located Distr...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ding, Weijia Ding, Ziwei Zhao, Laiping Qiu, Tie College of Intelligence and Computing Tianjin University Tianjin Key Laboratory of Advanced Networking China

ISBN: (纸本)9781665494571

this paper studies the problem of predicting the deep learning (DL) jobs' training time which is the fundamental guide for training resources allocating and job scheduling. the existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2×. thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93% when employing RFR in the training speed submodel, and the prediction overhead is © 2021 IEEE.

关键词： Training Deep learning Graphics processing units Interference Predictive models Encoding Resource management

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A method for identifying customer business application scenarios of cloud dedicated access 23

A method for identifying customer business application scena...

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作者： Lv, Tiantian Wu, Yanqin Zhang, Le Yuan, Jingjing Research Institue of China Telecom Corporation Limited Cloud Network Operation Department Beijing China

ISBN: (纸本)9781665494571

In order to reasonably allocate bandwidth in different service application scenarios to meet customer requirements, this paper proposes a method for identifying customer business application scenarios. First, multiple traffic features and time features are constructed to analyze service application scenarios of cloud dedicated access better. then, feature set is selected based on the method of Pearson correlation coefficient. Finally, Tri-training model is used to identify customer service application scenarios of cloud dedicated access. Compared with other models, the accuracy, precision and recall of Tri-training model are all higher than those of the other two models, which proves the reliability of the Tri-training model. © 2021 IEEE.

关键词： Correlation coefficient Cloud computing Smart cities Customer services Standardization Bandwidth Feature extraction

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Accelerating Permute and N-Gram Operations for Hyperdimensional Learning in Embedded systems 29

Accelerating Permute and N-Gram Operations for Hyperdimensio...

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29th IEEE international conference on Embedded and real-time computing systems and applications, RTCSA 2023

作者： Vergés, Pere Nunes, Igor Heddes, Mike Givargis, Tony Nicolau, Alexandru University of California Department of Computer Science Irvine United States

ISBN: (纸本)9798350337860

Hyperdimensional computing (HDC) is a novel computing framework that has gained significant attention for its ability to accelerate machine learning algorithms. Its fast learning and inference capabilities make it an ideal technique for various fields, including machine learning. HDC utilizes high-dimensional holographic vectors, which are vectors with independent and identically distributed dimensions, to represent information. this unique representation allows HDC to leverage highly parallelizable arithmetic operations such as bundling, binding and permute. these simple and highly optimizable operations make HDC an efficient framework for classification in embedded systems. HDC has demonstrated remarkable accuracy in learning patterns from sequenced data. In this paper, we propose a method to enhance the permute operation, which is crucial for maintaining the order of symbols or measures in real-time data. Our method enhances the efficiency of HDC's permute operations by a factor of 10×. Furthermore, by applying the same idea to n-gram encoding, we achieve a speedup of 14×, resulting in up to 26.8× speedup on a real application, compared to a state-of-the-art HDC prototyping library. To achieve this improvement, we utilized SIMD operations and shifted entire SIMD data blocks rather than individual elements. As a result, we demonstrate that real-time inference can be conducted rapidly in applications that are utilized in embedded systems with constrained computational and memory resources, such as those for recognizing emotions, gestures, and language. © 2023 IEEE.

关键词： Embedded systems

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QR-Based Efficient Waste Management System 4

QR-Based Efficient Waste Management System

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4th international conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Anuratha, K. Akshaya, S. Rajaselvi, C. Akshaya, M. Sri Sairam Institute of Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

this research study provides an overview of the QR-based Trash Management System, which emerges as a technology- driven process that aims at optimizing processes within the waste management process within urban environments. Traditional waste management systems usually fail on proper tracking, timely collections, and effective communication. Implementation of Quick Response code technology enabled this system to meet its challenges toward an organized and sustainable waste management framework. the basis for this system is in the integration of QR codes and mobile applications, which will enhance segregation practices, community engagement, and so forth. this QR-based Waste Management System is built to increase operational processes, sustainability initiatives, lower costs, and help towards making the cities cleaner and healthier. this scalable solution empowers communities to take active roles in waste management and hence forms a very critical innovation for metropolitan areas. © 2024 IEEE.

关键词： Waste management Technological innovation Urban areas QR codes Ubiquitous computing real-time systems Mobile applications Waste disposal Sustainable development Information systems

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Design of a High-Performance Parallel computing Platform for Spaceborne Intelligent Programmable PSOC 7

Design of a High-Performance Parallel Computing Platform for...

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7th international conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2024

作者： Duan, Weiming Zhang, Peng Zhang, Xinglong Tan, Chao Zhu, Qixing Electronic Information Engineering Department Shandong Aerospace Electronic Technology Research Institute Shandong Yantai264006 China

ISBN: (纸本)9781510681866

the "satellite data acquisition and ground processing" mode is difficult to meet the characteristics of detection and recognition in high real-time tasks such as target tracking and disaster detection caused by the large width, high resolution, and massive data of remote sensing images themselves. Propose a master-slave parallel computing architecture based on switching nodes. this architecture uses domestically produced PSOC (Programmable System-On-Chip) intelligent heterogeneous AI(Artificial Intelligence) chips, combined with SRIO(Serial Rapid IO) and Ethernet buses to achieve decoupling between broadcast image data distribution and parallel scheduling of MPI(Message Passing Interface) systems. On the basis of better utilizing the computing performance of single chip processors, it explores parallel computing modes with higher computing power and reliability. this mode enables image detection algorithms with large-scale and complex operator characteristics to be deployed and applied faster and more conveniently in spaceborne environments, which has certain reference significance and research value. © 2024 SPIE.

关键词： Message passing

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Optimizing Attendance: real-time Mobile Notifications through Facial Recognition with LBPH 10

Optimizing Attendance: Real-Time Mobile Notifications Throug...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Rani, R. Suneetha Manya, V. Parimi, Sridevi Nelavelli, Sai Manogna Bvrit Hyderabad College of Engineering for Women Department of Cse Hyderabad India

ISBN: (纸本)9798350384369

the conventional methods for tracking attendance often involve manual processes, which can be inefficient and error-prone. this abstract introduces a real-time Smart Atten- dance System utilizing Face Recognition technology to overcome these challenges. the primary objective is to create an intuitive system that automates attendance tracking through computer vision and machine learning. this solution involves the development of a robust system capable of real-time face capture and recognition. Users can interact with a user-friendly interface to input essential information such as ID, name, and phone number. the system utilizes a face recognition model trained on a dataset of facial images, aiming to enhance accuracy and efficiency in attendance tracking. It employs the Local Binary Pattern Histogram (LBPH) Face Recognizer for training, learning distinctive facial patterns. the real-time face recognition system captures live video frames, identifies individuals, and records attendance alongside associated IDs and timestamps. this technology has wide applications in educational institutions, work- places, and events, enhancing precise and efficient attendance tracking for management and security purposes. By automating attendance management, this paper offers a contemporary and reliable alternative to traditional manual methods. the real- time Smart Attendance System using Face Recognition addresses the limitations of existing attendance tracking systems, combining user-friendly design with machine learning and computer vision to revolutionize attendance management. © 2024 IEEE.

关键词： Face recognition

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MemFlex: A Hybrid Memory System to boost Cost of Ownership in Data Centers 48

MemFlex: A Hybrid Memory System to boost Cost of Ownership i...

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48th Annual IEEE international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Chakrabortti, Chandranil Islam, Mohammad Maminur Trinity Coll Dept Comp Sci Hartford CT 06106 USA

ISBN: (纸本)9798350376975;9798350376968

Modern large-scale computing clusters face scalability challenges with traditional DRAM-based memory systems due to issues like increasing cell leakage current and reduced reliability. To overcome these limitations, alternative memory solutions have emerged, including 3D-stacked DRAM and emerging non-volatile memory (NVM) technologies. However, these alternatives are unlikely to fully replace DRAM due to capacity constraints and higher cost-per-bit. Hybrid memory systems, combining DRAM with NVM technologies, offer a cost-effective solution by leveraging the strengths of both memory types. Effective data placement decisions are crucial for optimizing hybrid memory systems. this paper introduces MemFlex, an machine learning (ML)-driven approach for migrating pages between memory tiers in a hybrid memory system based on predicted page lifetimes. MemFlex utilizes application-specific ML models to predict death-time ranges with high accuracy, guiding placement decisions to the appropriate storage tier. Evaluation results demonstrate MemFlex's superiority over state-of-the-art techniques, achieving an average performance improvement of 19% over evaluated baselines, with minimal performance degradation. this paper contributes to hybrid memory management research, leveraging real-world traces for evaluation, and introducing an ML based approach for optimized data placement decisions.

关键词： Hybrid memory systems Page migration Data placement Memory management Death-time prediction

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Proceedings - 2024 IEEE 8th international conference on Fog and Edge computing, ICFEC 2024

Proceedings - 2024 IEEE 8th International Conference on Fog ...

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8th IEEE international conference on Fog and Edge computing, ICFEC 2024

ISBN: (纸本)9798350361353

the proceedings contain 16 papers. the topics discussed include: computation offloading for precision agriculture using cooperative inference;AMBIT: an efficient pruning technique in federated learning for edge computing systems;asynchronous federated split learning;federated learning deployments of industrial applications on cloud, fog, and edge resources;VATE: edge-cloud system for object detection in real-time video streams;enabling adaptive video streaming via content steering on the edge-cloud continuum;thinking out of replication for geo-distributing applications: the sharding case;WoW-IO: a gaming-based storage trace generator for edge computing;knowledge distillation based on monoclass teachers in edge infrastructure using unlabeled data;migration of isolated application across heterogeneous edge systems;and synergizing fuzzy-based task offloading with machine learning-driven forecasting for IoT.

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