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DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud

DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficien...

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

作者： Yoochan Kim Kihyun Kim Yonghyeon Cho Jinwoo Kim Awais Khan Ki-Dong Kang Baik-Song An Myung-Hoon Cha Hong-Yeon Kim Youngjae Kim Dept. of Computer Science and Engineering Sogang University Seoul Republic of Korea LG Electronics Oak Ridge National Laboratory TN USA ETRI Daejeon Republic of Korea

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

关键词： Training Deep learning Checkpointing Measurement Cloud computing Costs Artificial neural networks Linear programming

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XPySom: high-performance Self-Organizing Maps 32

XPySom: High-Performance Self-Organizing Maps

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Mancini, Riccardo Ritacco, Antonio Lanciano, Giacomo Cucinotta, Tommaso Scuola Super Sant Anna Pisa Italy Scuola Normale Super Pisa Pisa Italy

ISBN: (纸本)9781728199245

In this paper, we introduce XPySom, a new opensource Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives. Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error.

关键词： self-organizing maps (SOMs) performance comparison experimental evaluation GP-GPU acceleration

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FPGA-Based Hardware-in-the-Loop Real-Time Simulation Implementation

FPGA-Based Hardware-in-the-Loop Real-Time Simulation Impleme...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Farshideh Kordi Christian Barnard Paul Fortier Amine Miled Departement of Electrical and Computer Engineering Université Laval Québec QC Canada

Hardware-in-the-Loop (HIL) simulators have become an essential tool in the automotive industry as they allow comprehensive testing and validation of electronic control units (ECUs), Although HIL simulations are widely used in the vehicle, aerospace and other industries that rely on electronic control systems, they can also be quite challenging to develop. One of the main challenges of HIL simulators is creating a virtual environment that accurately replicates the behavior of the real-world system. Artificial intelligence (AI) algorithms can be trained on extensive real-world datasets to create more accurate virtual environments of complex systems for HIL simulations. Moreover, latency is a crucial factor in producing a reliable virtual environment for ECU in HIL simulations. FPGA (Field Programmable Gate Array) can help to minimize latency in HIL simulations by providing high-performance computing resources. this paper aims to address these challenges by presenting a machine learning-based HIL simulator design on FPGA. the proposed architecture uses FPGA to accelerate the computations of a temporal convolutional neural network (TCN). the paper also describes how increasing the number of input channels can improve the performance of the TCN model on an FPGA.

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Cache Register Sharing Structure for Channel-level Near-memory Processing in NAND Flash Memory

Cache Register Sharing Structure for Channel-level Near-memo...

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IEEE International symposium on Quality Electronic Design

作者： Hyunwoo Kim Hyundong Lee Jongbeom Kim Yunjeong Go Seungwon Baek Jaehong Song Junhyeon Kim Minyoung Jung Hyodong Kim Seongju Kim Taigon Song School of Electronic and Electrical Engineering Kyungpook National University (KNU) Daegu South Korea School of Electronics Engineering Kyungpook National University (KNU) Daegu South Korea

A vast number of data used for Artificial intelligence causes bottleneck between the processor and memory. To tackle this issue, a technology that embeds a processing unit in the memory (PIM: Processing-in Memory) has been proposed. However, SRAM/DRAM based PIM have a issue for lack of capacity. thus, we propose a NAND flash PIM scheme that shares the cache register. Our scheme significantly reduces the read latency and operation time by -22.8% and -43.7%, compared to the conventional memory system. the power-performance-area (PPA) was reduced by 17.2% by shortening the number of cycles. Our NAND PIM specializes in tasks requiring high-performance computing.

关键词： Registers Flash memories computer architecture Artificial intelligence Arrays Random access memory Microprocessors

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Optically Connected Memory for Disaggregated Data Centers 32

Optically Connected Memory for Disaggregated Data Centers

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Gonzalez, Jorge Gazman, Alexander Hattink, Maarten Palma, Mauricio G. Bahadori, Meisam Rubio-Noriega, Ruth Orosa, Lois Glick, Madeleine Mutlu, Onur Bergman, Keren Azevedo, Rodolfo Univ Estadual Campinas Campinas Brazil Columbia Univ New York NY 10027 USA Nokia Espoo Finland INICTEL UNI Lima Peru Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781728199245

Recent advances in integrated photonics enable the implementation of reconfigurable, high-bandwidth, and low energy-per-bit interconnects in next-generation data centers. We propose and evaluate an Optically Connected Memory (OCM) architecture that disaggregates the main memory from the computation nodes in data centers. OCM is based on micro-ring resonators (MRRs), and it does not require any modification to the DRAM memory modules. We calculate energy consumption from real photonic devices and integrate them into a system simulator to evaluate performance. Our results show that (1) OCM is capable of interconnecting four DDR4 memory channels to a computing node using two fibers with 1.07 pJ energyper-bit consumption and (2) OCM performs up to 5.5x faster than a disaggregated memory with 40G PCIe NIC connectors to computing nodes.

关键词： disaggregated computing disaggregated memory photonics data-centers DRAM memory systems

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A Robotic Communication Middleware Combining high performance and high Reliability 32

A Robotic Communication Middleware Combining High Performanc...

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Liu, Wei Wu, Hao Jiang, Ziyue Gong, Yifan Jin, Jiangming TuSimple San Diego CA 92122 USA

ISBN: (数字)9781728199245

ISBN: (纸本)9781728199245

With the significant advances of AI technology, intelligent robotic systems have achieved remarkable development and profound effects. To enable massive data transmission in an efficient and reliable way, both high performance and high reliability should be taken into account in system design. However, the conventional communication middleware used in the majority of autonomous robotic systems, is based on socked-based methods, which always lead to high latency. Moreover, some sophisticated communication middleware utilizes shared memory upon ring buffers for high performance without consideration of the reliability. To obtain both high performance and high reliability, we employ shared memory for performance improvement and propose a novel socket-based communication control algorithm to improve reliability during data transmission. Furthermore, based on the proposed algorithm, we implement a novel robotic communication middleware, named Robust-Z, combining both high performance and high reliability. Experimental results show that (1) Robust-Z is able to gain up to 41% and 5% performance improvement compared to ROS2 and Apollo CyberRT, respectively;(2) Robust-Z is able to provide crash safety and reduce 5.2% data missing rate compared with CyberRT.

关键词： Autonomous Navigation Systems Inter-process Communication high performance high Availability

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Real-Time Wrong-Way Vehicle Detection System with Automatic Number Plate Recognition for Enhanced Road Safety

Real-Time Wrong-Way Vehicle Detection System with Automatic ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Krish Gaur Miran Ahmad Siddique Krishna Beernally Nityam Madaan Sandhya Tarwani Artificial Intelligence-Machine Learning Vivekananda Institute of Professional Studies New Delhi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the pursuit of enhancing road safety, the development of real-time detection systems has become imperative. this paper presents a smart approach to wrong-way vehicle detection utilizing computer vision technology for enhanced road safety. the proposed system presents a combination of YOLO for vehicle and license plate detection and OpenCV for license plate recognition, specifically tailored for Indian vehicle number plate dataset. By integrating YOLO’s single-pass object detection capabilities, the system achieves real-time performance, in order to take immediate intervention in traffic violations. Utilizing methods such as centroid tracking and ‘entry-exit’ analysis, the system effectively differentiates between vehicles traveling in correct lanes and those in wrong lanes, enabling immediate enforcement measures. Centroid tracking assigns and tracks centre points of vehicle bounding boxes across frames, ensuring computational efficiency and simplicity in implementation. the ‘entry-exit’ approach utilizes designated lines in video frames to determine the direction of vehicle movement, leading towards accurate wrong-way detection. Moreover, the system leverages EasyOCR for text extraction, ensuring high accuracy in capturing license plate information. this integration of technologies allows for comprehensive monitoring of traffic patterns and swift identification of violations, contributing to proactive enforcement strategies and improved road safety. through extensive experimentation on the Indian vehicle number plate YOLO annotated dataset, the proposed system demonstrates robust performance in real-world scenarios, exhibiting high accuracy and reliability in wrong-way vehicle detection and automatic number plate recognition. this research represents a significant advancement in leveraging computer vision for proactive traffic management and underscores the potential of technology in enhancing road safety measures.

关键词： YOLO computer vision Accuracy Tracking Traffic control Road safety Real-time systems Reliability License plate recognition Monitoring

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Controlling Garbage Collection and Request Admission to Improve performance of FaaS Applications 32

Controlling Garbage Collection and Request Admission to Impr...

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32nd IEEE International symposium on computer architecture and high-performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core architectures (WAMCA)

作者： Quaresma, David Fireman, Daniel Pereira, thiago Emmanuel Univ Fed Campina Grande Campina Grande Paraiba Brazil Fed Inst Alagoas Arapiraca Brazil

ISBN: (纸本)9781728199245

Runtime environments like Java's JRE, .NET's CLR, and Ruby's MRI, are popular choices for cloud-based applications and particularly in the Function as a Service (FaaS) serverless computing context. A critical component of runtime environments of these languages is the garbage collector (GC). the GC frees developers from manual memory management, which could potentially ease development and avoid bugs. the benefits of using the GC come with a negative impact on performance;that impact happens because either the GC needs to pause the runtime execution or competes with the running program for computational resources. In this work, we evaluated the usage of a technique - Garbage Collector Control Interceptor (GCI) that eliminates the negative impact of GC on performance by controlling GC executions and transparently shedding requests while the collections are happening. We executed experiments simulating AWS Lambda's behavior and found that GCI is a viable solution. It benefited the user by improving the response time up to 10:86% at 99.9th percentile and reducing cost by 7:22%, but it also helped the platform provider by improving resource utilization by 14:52%

关键词： serverless faas garbage collector cold start gci cloud computing cloud services

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Bangla Hand-Written Digit Recognition Using Deep Learning Models

Bangla Hand-Written Digit Recognition Using Deep Learning Mo...

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International Conference on computing and Networking Technology (ICCNT)

作者： Md Shefat Al Mahmud Sadman Sadik Khan Md. Sadekur Rahman Nayeem Ahmed Nuzhat Noor Islam Prova Amit Kumar Gupta Department of CSE Daffodil International University Dahaka Bangladesh Department of Computer Science University of Memphis Tennessee USA Seidenberg School of CSIS Pace University New York USA Department of Computer Applications KIET Group of institutions Delhi NCR Ghaziabad U.P India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

there are a lot of works on English handwritten materials but not seen so much work on Bangla handwritten recognition. Bangla handwritten digits have more complex and diverse shapes and also have high inter-class similarities. So it is more challenging to recognize Bangla handwritten digits. the paper focuses on improving the lack of Bangla handwritten digit recognition approach using deep learning models. In the research, we have developed three deep learning-based models, CNN, DenseNet121, and ResNet50 to recognize Bangla digits. To make our work efficient we have used two existing BanglaLekha-Isolated-Numerics and Ekush datasets along with a new dataset that is collected by our team. We tested the three models on each dataset and achieved remarkable performance in all the datasets. Among the models, DenseNet121 outperformed on every dataset. DenseNet121 achieved 99.09% test accuracy on the Ekush dataset and 97.65% test accuracy on our developed dataset.

关键词： Deep learning Handwriting recognition Accuracy Shape Computational modeling Residual neural networks

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Research and Practice of Container System 15

Research and Practice of Container System

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15th International symposium on theoretical Aspects of Software Engineering (TASE)

作者： Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Cluster & Grid Comp LabSch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665441636

Container technology has been widely used in various real-world situations, like cloud platforms, CI/CD, and DevOps. By enabling a layered image system and OS-level virtualization, container technology can provide agile deployment and isolate execution environment for applications. However, existing container systems fail to support containers efficiently and securely. On the one hand, coarse-grained image management makes the deployment and update of applications time-consuming when the corresponding images need to be delivered in network. On the other hand, shared OS kernel may arise resource contention and security issues. this talk shows our research and practice of container systems. Specifically, I will introduce approaches of image management for fast container deployment, OS kernel isolation for secure and high-performance container execution environment, and container live migration for mitigating resource contention.

关键词： Cloud computing Containers Security Kernel Virtualization Software engineering

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