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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Addressing Waste Management Efficiency in Qatar: A Meta-Heuristic and Approximation Approach to Traveling Salesman Problem

Addressing Waste Management Efficiency in Qatar: A Meta-Heur...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Nikita Nilesh Mistry Noora Shifa Kawthar Khaled Roshni Nawaz Department of Computer Science & Engineering Qatar University Doha Qatar

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Efficient waste collection and management are imperative for addressing the environmental implications arising from the escalating volume of daily waste generation. To mitigate these challenges, strategic interventions are essential to optimize waste collection systems, particularly in regions like Qatar where financial constraints underscore the need for cost-effective solutions meeting various performance criteria. This study endeavors to optimize waste collection and management in Qatar by utilizing the Traveling Salesman Problem (TSP) framework. Through a meticulous exploration of four distinct TSP implementations - namely, the Nearest Neighbor (NN), Christofides', Ant Colony Optimization (ACO), and Simulated Annealing (SA) - tailored to the distinctive demands of waste collection logistics, our focus has been on delineating the most efficient routes for waste collection vehicles. The selection of these algorithms is based on their demonstrated effectiveness in similar optimization problems and their suitability for waste collection operations. Embracing approximation approaches such as NN and Christofides' algorithm has provided practical solutions, complemented by the strategic insights offered by ACO and SA heuristics. By conducting a rigorous analysis and comparison of these algorithms, we endeavor to offer insights into their efficacy in addressing Qatar's waste management imperatives, thereby informing decision-making processes and enhancing operational efficiency within the waste management domain.

关键词： Waste management Runtime Smart cities Scalability Memory management Simulated annealing Traveling salesman problems Approximation algorithms Computational efficiency Robots

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A Layer Decomposition Approach to Inference Time Prediction of Deep Learning architectures

A Layer Decomposition Approach to Inference Time Prediction ...

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international Conference on Machine Learning and Applications (ICMLA)

作者： Ola Mustafa Alqahtani Lakshmish Macheeri Ramaswamy School of Computing The University of Georgia Athens Georgia USA

In recent years, deep learning models have been widely adopted in lots of fields. such as computer vision, pattern recognition, and classification problems like plant disease classification. Due to the large diversity among the computing devices that these models may run on, we need to choose between the appropriate device based on cost and performance. Furthermore, finding the suitable optimal device for a given project is a complex process that needs significant time and resources. Prediction of inference latency DNN models is necessary for many tasks where measuring the latency on real devices is either infeasible or too costly. This is a very challenging problem, and most existing approaches fail to achieve high accuracy of prediction. While some research has been carried out to predict the inference time of DNN models – most existing techniques assume that training time is linearly related to the number of floating-point operations. This paper designs and develops a framework to predict the inference time for deep learning models and is generic to be easily extended for a large set of devices. Our key idea is decomposing a given model inference into layers and conducting layer-level prediction. Our experiments demonstrate that this strategy provides significant benefits in terms of prediction accuracy.

关键词： Deep learning Training performance evaluation Plant diseases Computational modeling Time to market Predictive models

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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symposium of Image, Signal Processing, and Artificial Vision (stSIVA)

作者： Sanchita Das Gouranga Maity Diptarka Mandal Dmitrii Kaplun Alexander Voznesensky Ram Sarkar Dept. of CSE Jadavpur University Kolkata India Department of Applied Mathematics Defence Instituite of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Dept. of Automation and Control Process St. Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net.

关键词： Image segmentation Accuracy Microscopy Computational modeling Biological system modeling Source coding computer architecture Entropy Robustness Medical diagnostic imaging

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Overview of high performance computing Power Building for the Big Data of Marine Forecasting

Overview of High Performance Computing Power Building for th...

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2020 international Conference on Big Data and Informatization Education, ICBDIE 2020

作者： Lin, Bo National Marine Environment Forecast Center Ministry of Natural Resources Beijing China

ISBN: (纸本)9781728159003

This thesis introduced the construction and development process of high-performance computing of marine forecasting from scratch, from small to large, from weak to strong by National Marine Environmental Forecasting Center of the Ministry of Natural Resources of the People's Republic of China in the past half century;expounded the 'two milestones': zero breakthrough of high-performance computing of marine forecasting and computing power exceeding 1 billion times per second for the first time from the early 1970s to the late 1990s in high-performance computing of ocean forecasting, and the achievements of ocean forecasting on the journey of globalization, refinement and high resolution with the help of high-performance computing after entering the 21st century;studied, designed and showed a better architecture blueprint for the next generation of the high-performance computer system for marine forecasting;pointed out that there were gaps between the high-performance computing of marine forecasting and the international advanced one, domestic advanced one and professional requirements, and put forward two suggestions to enhance the computing capability of existing equipment through technological innovation and scientific research innovation from the perspective of professional technology;and finally held that there was still a long way to go to build China's highperformance computing power for marine forecasting in comparison with the medium and long-term objectives and tasks of the modernization of marine forecasting. © 2020 IEEE.

关键词： Big data

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Pruned Simulation-Based Optimal Sailboat Path Search Using Micro HPC Systems 21st

Pruned Simulation-Based Optimal Sailboat Path Search Using M...

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21st international Conference on Computational Science, ICCS 2021

作者： Dębski, Roman Sniezynski, Bartlomiej Institute of Computer Science AGH University of Science and Technology Al. Mickiewicza 30 Kraków30-059 Poland

ISBN: (纸本)9783030779696

Simulation-based optimal path search algorithms are often solved using dynamic programming, which is typically computationally expensive. This can be an issue in a number of cases including near-real-time autonomous robot or sailboat path planners. We show the solution to this problem which is both effective and (energy) efficient. Its three key elements – an accurate and efficient estimator of the performance measure, two-level pruning (which augments the estimator-based search space reduction with smart simulation and estimation techniques), and an OpenCL-based spmd-parallelisation of the algorithm – are presented in detail. The included numerical results show the high accuracy of the estimator (the medians of relative estimation errors smaller than 0.003), the high efficacy of the two-level pruning (search space and computing time reduction from seventeen to twenty times), and the high parallel speedup (its maximum observed value was almost 40). Combining these effects gives (up to) 782 times faster execution. The proposed approach can be applied to various domains. It can be considered as an optimal path planing framework parametrised by a problem specific performance measure heuristic/estimator. © 2021, Springer Nature Switzerland AG.

关键词： Dynamic programming

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Topology Awareness for Smart 5G eMBB Network Slicing VNF Placement

Topology Awareness for Smart 5G eMBB Network Slicing VNF Pla...

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21st IEEE international symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM)

作者： Sanchez-Navarro, Ignacio Calero, Jose M. Alcaraz Wang, Qi Univ West Scotland Sch Comp Engn & Phys Sci Paisley Renfrew Scotland

ISBN: (纸本)9781728173740

This paper presents an architecture to gather non-traditional metrics from 5G multi-tenant infrastructure using information about the network topology to take smart decisions on where to optimal placement VNFs that are used to provide services to network slices. The metrics considered are spatial metrics, where information about the shape and size of the network topology is taken into consideration. The architecture has been prototypical validated showing how optimal decisions are taken in an eMBB high-dense scenario, with topologies up to 65538 mobile users geographically concentrated on the same location. Our prototype is able to deal with the calculation of such spatial metrics over the 5G multi-tenant network with 65538 mobile users within 20 seconds, which make it viable at operational phase.

关键词： VNF Placement Smart computing 5G Network Topology Network Slicing Multy-Teancy

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The OpenPME Problem Solving Environment for Numerical Simulations 21st

The OpenPME Problem Solving Environment for Numerical Simula...

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21st international Conference on Computational Science, ICCS 2021

作者： Khouzami, Nesrine Schütze, Lars Incardona, Pietro Kraatz, Landfried Subic, Tina Castrillon, Jeronimo Sbalzarini, Ivo F. Faculty of Computer Science Technische Universität Dresden Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden Germany Cluster of Excellence Physics of Life TU Dresden Dresden Germany

ISBN: (纸本)9783030779603

We introduce OpenPME, the Open Particle-Mesh Environment, a problem solving environment that provides a Domain Specific Language (DSL) for numerical simulations in scientific computing. It is built atop a domain metamodel that is general enough to cover the main types of numerical simulations: simulations using particles, meshes, and hybrid combinations of particles and meshes. Using model-to-model transformations, OpenPME generates code against the state-of-the-art C++ parallel computing library OpenFPM. This effectively lowers the programming barrier and enables users to implement scalable simulation codes for high-performance computing (HPC) systems using high-level abstractions. Plenty of recent research has shown that higher-level abstractions and problem solving environments are well suited to alleviate low-level implementation overhead. We demonstrate this for OpenPME and its compiler on three different test cases—particle-based, mesh-based, and hybrid particle-mesh—showing up to 7-fold reduction in the number of lines of code compared to a direct OpenFPM implementation in C++. © 2021, Springer Nature Switzerland AG.

关键词： Numerical models

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Baldur: A Power-Efficient and Scalable Network Using All-Optical Switches 26

Baldur: A Power-Efficient and Scalable Network Using All-Opt...

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26th IEEE international symposium on high performance computer architecture (HPCA)

作者： Jokar, Mohammad Reza Qiu, Junyi Chong, Frederic T. Goddard, Lynford L. Dallesasse, John M. Feng, Milton Li, Yanjing Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Illinois Dept Elect & Comp Engn 1406 W Green St Urbana IL 61801 USA

ISBN: (数字)9781728161495

ISBN: (纸本)9781728161495

We present the first all-optical network, Baldur, to enable power-efficient and high-speed communications in future exascale computing systems. The essence of Baldur is its ability to perform packet routing on-the-fly in the optical domain using an emerging technology called the transistor laser (TL), which presents interesting opportunities and challenges at the system level. Optical packet switching readily eliminates many inefficiencies associated with the crossings between optical and electrical domains. However, TL gates consume high power at the current technology node, which makes TL-based buffering and optical clock recovery impractical. Consequently, we must adopt novel (bufferless and clock-less) architecture and design approaches that are substantially different from those used in current networks. At the architecture level, we support a bufferless design by turning to techniques that have fallen out of favor for current networks. Baldur uses a low-radix, multi-stage network with a simple routing algorithm that drops packets to handle congestion, and we further incorporate path multiplicity and randomness to minimize packet drops. This design also minimizes the number of TL gates needed in each switch. At the logic design level, a non-conventional, length-based data encoding scheme is used to eliminate the need for clock recovery. We thoroughly validate and evaluate Baldur using a circuit simulator and a network simulator. Our results show that Baldur achieves up to 3,000X lower average latency while consuming 3.2X-26.4X less power than various state-of-the-art networks under a wide variety of traffic patterns and real workloads, for the scale of 1,024 server nodes. Baldur is also highly scalable, since its power per node stays relatively constant as we increase the network size to over 1 million server nodes, which corresponds to 14.6X-31.0X power improvements compared to state-of-the-art networks at this scale.

关键词： optical computing all-optical network exascale computing datacenter network

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Fruit Fly Detection and Classification in IoT Setup 23rd

Fruit Fly Detection and Classification in IoT Setup

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23rd international Conference on Computational Science and Its Applications, ICCSA 2023

作者： Fasih, Syed M. Ali, Asad Mabood, Talha Ullah, Atif Hanif, Muhammad Ahmad, Waqar Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Swabi Pakistan

ISBN: (纸本)9783031371165

In the 21st century, advancements in hardware and processing speeds have enabled the deployment of machine learning algorithms such as YOLOv5 on Internet of Things (IoT) devices. These AI-based IoT devices are powerful computers suitable for embedded applications, AI IoT, and edge computing, delivering the performance of a modern AI module at a reduced cost. This conference paper presents a modified version of YOLOv5 called YOLOv5-FlyEye, optimized for embedded systems such as the Nvidia Jetson Nano. The proposed modification achieves high performance in detecting and classifying fruit flies by reducing the computational complexity of the YOLOv5 algorithm. The study aims to demonstrate the potential of AI-based IoT devices in the field of agriculture, particularly in pest detection and monitoring, and contributes to the development of optimized algorithms for efficient and cost-effective embedded systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Hashing-based Undersampling for Large Scale Histopathology Image Classification

Hashing-based Undersampling for Large Scale Histopathology I...

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IEEE international Conference on Cognitive Informatics

作者： Xing Tian Lin Qiu Qihua Li Wing W. Y. Ng Jianjun Zhang Sam Kwong Hui Wang Xinran Dong Baoyi Liu Yijun Hu Honghua Yu School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computer Science Hong Kong City University Hong Kong China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Department of Ophthalmology Guangdong Eye Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Aier School of Ophthalmology Central South University Changsha China

The early diagnosis of cancer based on histopathology images plays an important role in medical science. Existing techniques generally partition the original histopathology image into small pieces for further classification. However, due to the fact that the number of benign (majority) samples is much larger than that of malignant (minority) samples, the classification is significantly imbalanced which adversely affects classification performance. Undersampling is commonly used to address the class-imbalance problem. However, existing methods are typically time consuming so they are not suitable to handle large-scale and high-dimensional data. In this paper we propose a fast and scalable undersampling method, hashing-based undersampling (HBU), to address class imbalance in large-scale medical image classification. Benign images are hashed and then placed into different buckets according to their locations in the input space. Undersampling is achieved by proportionally selecting benign images from the hash buckets. The HBU method is experimentally evaluated on two real histopathology image datasets, CAMELYON16 and ACDC@LUNGHP, by comparison with existing methods. Experimental results show that the HBU method outperforms six state-of-the-art methods and is scalable and fast.

关键词： Histopathology Cognitive systems Medical diagnostic imaging Cancer Image classification

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