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MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed systems 51

MAD-Max Beyond Single-Node: Enabling Large Machine Learning ...

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ACM/ieee 51st Annual international Symposium on Computer Architecture (ISCA)

作者： Hsia, Samuel Golden, Alicia Acun, Bilge Ardalani, Newsha DeVito, Zachary Wei, Gu-Yeon Brooks, David Wu, Carole-Jean FAIR Meta Menlo Pk CA 94025 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350326598;9798350326581

Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large model training on datacenter-scale infrastructures, reveals that 14 similar to 32% of all GPU hours are spent on communication with no overlapping computation. To minimize this outstanding communication latency and other inherent at-scale inefficiencies, we introduce an agile performance modeling framework, MAD-Max. This framework is designed to optimize parallelization strategies and facilitate hardware-software co-design opportunities. Through the application of MAD-Max to a suite of real-world large-scale ML models on state-of-the-art GPU clusters, we showcase potential throughput enhancements of up to 2.24x for pre-training and up to 5.27x for inference scenarios, respectively.

关键词： Data Center Distributed Inference Distributed Training GPU Hardware-Software Co-Design Machine Learning Parallelization Performance Model Simulator

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge computing systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 ieee.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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Investigating Scope of real-time Data Processing for IoT in 5G Revolution 15

Investigating Scope of Real-time Data Processing for IoT in ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Padmapriya, S. Patil, Trupti Veeraiah, Vivek Namdev, Arpit Koujalagi, Ashok Gupta, Ankur SRM Trichy Arts and Science College Dept. of CS TN Irungalur India Bharati Vidyapeeth Deemed to be University Dept. of Engineering and Technology Maharashtra India Sri Siddharth Institute of Technology Sri Siddhartha Academy of Higher Education Karnataka Tumkur India University Institute of Technology RGPV Dept. of IT MP Bhopal India Dept. of CSE AP India Vaish College of Engineering Dept. of CSE Haryana Rohtak India

ISBN: (纸本)9798350370249

This research examines 5G-enabled IoT devices' real-time data processing capability and constraints. A thorough literary study helps understand current technologies, application situations, and industrial difficulties. Using clear research goals, the study discovers opportunities, obstacles, and solutions. Interviews, surveys, and literature gather primary and secondary data. A use case study examines the pros and cons of 5G -enabled IoT devices in real-world data processing situations. The technical examination evaluates 5G real-time data processing capabilities, concentrating on latency, reliability, and scalability. Case studies or simulations examine implementations, successes, problems, and best practices. The results suggest architectural and efficiency methods. Simulations or prototypes test solution efficacy and feasibility. Data analysis and interpretation show 5G-enabled IoT devices' real-time data processing capabilities and limitations. This method allows researchers to advance the area and significantly affect industry and academic research and practice. We will analyse the design, implementation, and optimisation of 5G-enabled IoT systems for real-time data processing. This technology has tremendous possibilities and problems, which we shall study. We wish to promote innovation and enable the next generation of networked applications and services. The prior model employed conventional methods and had 89.1 % accuracy and 11.9 % inaccuracy. When employing the hybrid Bert model, accuracy climbed to 92.3% and error rate reduced to 7.7%. © 2024 ieee.

关键词： 5G mobile communication systems

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Leveraging IoT for Enhanced Industrial Automation: Innovations and applications 2

Leveraging IoT for Enhanced Industrial Automation: Innovatio...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Chandu, Hiranmaye Sarpana India

ISBN: (纸本)9798331541217

The IoT is a relatively new and exciting field in computer science that is expanding rapidly worldwide. Background: IoT has seen substantial growth due to advancements in wireless networking, enhanced sensors, and IoT platforms, as well as the convergence of cloud and edge computing. Objective: The paper aims to assess the innovations in IoT, evaluate their applications in industrial automation, and identify key challenges and opportunities for further research. Methods: The review covers recent advancements, such as the application of Federated Learning for enhancing IoT security, the use of large-scale blockchains for data integrity, and the integration of digital twins and containerisation in robotics. Key areas of industrial application, including predictive maintenance, supply chain management, and real-time environmental control, are explored. Results: The findings highlight significant improvements in cost savings, reliability, and safety through IoT in industrial automation. However, challenges such as network generalization, real-world applicability, and efficient resource usage remain. Conclusion: More effective and dependable systems might be the result of future research that tackles these issues and increases the influence of IoT technology in industrial automation. © 2024 ieee.

关键词： Predictive maintenance

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CEDR-API: Productive, Performant Programming of Domain-Specific embedded systems

CEDR-API: Productive, Performant Programming of Domain-Speci...

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37th ieee international Parallel and Distributed Processing Symposium (IPDPS)

作者： Mack, Joshua Gener, Serhan Hassan, Sahil Suluhan, H. Umut Akoglu, Ali Univ Arizona ECE Tucson AZ 85721 USA

ISBN: (纸本)9798350311990

As the computing landscape evolves, system designers continue to explore design methodologies that leverage increased levels of heterogeneity to push performance within limited size, weight, power, and cost budgets. One such methodology is to build Domain-Specific System on Chips (DSSoCs) that promise increased productivity through narrowed scope of their target application domain. In previous works, we have proposed CEDR, an open source, unified compilation and runtime framework for DSSoC architectures that allows applications, scheduling heuristics, and accelerators to be co-designed in a cohesive manner that maximizes system performance. In this work, we present changes to the application development workflow that enable a more productive and expressive API-based programming methodology. These changes allow for more rapid integration of new applications without sacrificing application performance. Towards the design of heterogeneous SoCs with rich set of accelerators, in this study we experimentally study the impact of increase in workload complexity and growth in the pool of compute resources on execution time of dynamically arriving workloads composed of real-life applications executed over architectures emulated on Xilinx ZCU102 MPSoC and Nvidia Jetson AGX Xavier. We expand CEDR into the application domain of autonomous vehicles, and we find that API-based CEDR achieves a runtime overhead reduction of 19.5% with respect to the original CEDR.

关键词： heterogeneous programming models runtime systems resource management

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Automated Calibration of Parallel and Distributed computing Simulators: A Case Study

Automated Calibration of Parallel and Distributed Computing ...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： McDonald, Jesse Horzela, Maximilian Suter, Frederic Casanova, Henri Univ Hawaii Manoa Honolulu HI 96822 USA Karlsruhe Inst Technol Karlsruhe Germany Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9798350364613;9798350364606

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or "calibrated," based on ground truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.

关键词： Simulation of distributed computing platforms and applications Simulation accuracy and scalability Simulation calibration

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Guarding Against Command and Control (C2) Agents Utilizing real-World applications for Communication Channels 5

Guarding Against Command and Control (C2) Agents Utilizing R...

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5th ieee international conference for Emerging Technology, INCET 2024

作者： Vidhun, K. Kannimoola, Jinesh M. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350361155

Command and Control (C2) agents are a critical component of many cyberattacks, enabling adversaries to maintain covert control over compromised systems. In recent years, attackers have increasingly leveraged real-world applications, such as social media and collaborative platforms, as C2 channels to evade detection. This paper proposes a behavioural-based network-level filtering approach to enhance cybersecurity defences against these threats. The suggested method includes creating a way to look at network traffic and find patterns and oddities that point to C2 agent activity, as well as setting up network-level filtering to spot malicious traffic and attempts to communicate from C2 agents. This network-level filtering approach aims to provide a proactive and effective defence against C2 attacks, particularly those utilising non-traditional communication channels. Initial results, obtained through testing a Random Forest model on real-world traffic, demonstrate promising outcomes, with the model successfully distinguishing between benign and malicious activities. © 2024 ieee.

关键词： Cybersecurity

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Improving real-time Music Accompaniment Separation with MMDenseNet 27

Improving Real-Time Music Accompaniment Separation with MMDe...

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27th conference on the Oriental COCOSDA international Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Wang, Chun-Hsiang Wang, Chung-Che Wang, Jun-You Jang, Jyh-Shing Roger Chu, Yen-Hsun National Taiwan University Dept. of CSIE Taiwan Institute of Information Science Academia Sinica Taiwan Realtek Semiconductor Corp. Taiwan

ISBN: (纸本)9798331506032

Music source separation aims to separate polyphonic music into different types of sources. Most existing methods focus on enhancing the quality of separated results by using a larger model structure, rendering them unsuitable for deployment on edge devices. Moreover, these methods may produce low-quality output when the input duration is short, making them impractical for real-time applications. This challenge is akin to those in speech processing models and systems, where isolating and analyzing specific audio components is critical. Therefore, the goal of this paper is to enhance a lightweight model, MMDenstNet, to strike a balance between separation quality and latency for real-time applications. Different directions of improvement are explored or proposed in this paper, including complex ideal ratio mask, self-attention, band-merge-split method, and feature look back. Source-to-distortion ratio, real-time factor, and optimal latency are employed to evaluate the performance. To align with our application requirements, the evaluation process in this paper focuses on the separation performance of the accompaniment part. Experimental results demonstrate that our improvements achieve a low real-time factor and optimal latency while maintaining a comparable source-to-distortion ratio. © 2024 ieee.

关键词： Audio systems

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WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture 23

WeedVision: Multi-Stage Growth and Classification of Weeds u...

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23rd ieee international conference on Machine Learning and applications, ICMLA 2024

作者： Islam, Taminul Sarker, Toqi Tahamid Ahmed, Khaled R Rankrape, Cristiana Bernardi Gage, Karla School of Computing Southern Illinois University Carbondale United States Southern Illinois University Department of Plant Soils and Agricultural Systems Carbondale United States School of Agricultural Sciences School of Biological Sciences Southern Illinois University Carbondale United States

ISBN: (纸本)9798350374889

Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective management yet challenging for farmers, as it requires identifying different species at multiple growth phases. This research ad-dresses these challenges by utilizing advanced object detection models-specifically, the Detection Transformer (DETR) with a ResNet-50 backbone and RetinaNet with a ResNeXt-101 backbone-to identify and classify 16 weed species of economic concern across 174 classes, spanning their Ll-week growth stages from seedling to maturity. A robust dataset comprising 203,567 images was developed, meticulously labeled by species and growth stage. The models were rigorously trained and evaluated, with RetinaNet demonstrating superior performance, achieving a mean Average Precision (mAP) of 0.907 on the training set and 0.904 on the test set, compared to DETR's mAP of 0.854 and 0.840, respectively. RetinaNet also outperformed DETR in recall and inference speed of 7.28 FPS, making it more suitable for real-time applications. Both models showed improved accuracy as plants matured. This research provides crucial insights for developing precise, sustainable, and automated weed management strategies, paving the way for real-time species-specific detection systems and advancing AI-assisted agriculture through continued innovation in model development and early detection accuracy. © 2024 ieee.

关键词： Livestock

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Design and Optimization of High-Performance Health Monitoring systems Using Advanced Sensor Networks 2

Design and Optimization of High-Performance Health Monitorin...

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2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Mehta, Heena Singla, Mukesh Baba Mastnath University Dept. of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798331519056

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution. © 2024 ieee.

关键词： Electronic health record

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