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performance Evaluation of Heterogeneous GPU programming Frameworks for Hemodynamic Simulations

Performance Evaluation of Heterogeneous GPU Programming Fram...

引用

2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Martin, Aristotle Liu, Geng Ladd, William Lee, Seyong Gounley, John Vetter, Jeffrey Patel, Saumil Rizzi, Silvio Mateevitsi, Victor Insley, Joseph Randles, Amanda Biomedical Engineering Duke University DurhamNC United States Leadership Computing Facility Argonne National Laboratory LemontIL United States Computer Science and Mathematics Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798400707858

Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time. © 2023 ACM.

关键词： Graphics processing unit

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Water Quality Monitoring System for Nile Tilapia Farming Using LoRa Technology 8

Water Quality Monitoring System for Nile Tilapia Farming Usi...

引用

8th International conference on Information Technology, InCIT 2024

作者： Chansiri, Nawara Sangkaphet, Ponglert Chaleamwong, Buppawan Kaewklom, Chaivichit Makdee, Supawee Ubon Ratchathani Rajabhat University Faculty of Computer Science Department of Software Engineering Ubon Ratchathani Thailand Ubon Ratchathani Rajabhat University Faculty of Computer Science Department of Computer Science Ubon Ratchathani Thailand

ISBN: (纸本)9798350366303

Fish farmers operating at Sirindhorn Dam in Ubon Ratchathani have faced significant challenges, including high mortality rates attributable to unstable weather conditions that compromise water quality. To tackle these issues, the Internet of Things has emerged as a tool to enhance aquaculture management. This research seeks to develop a prototype water quality monitoring system using LoRa technology to facilitate data transmission between sensor nodes in a peer-to-peer configuration. The development and testing of the system demonstrate its ability to accurately monitor key water quality parameters, including dissolved oxygen, pH, and water temperature, while allowing for remote data transmission. This research involved a sample group of five program development experts and 15 general users, with various tools employed for system efficiency assessment. A statistical analysis including the mean and standard deviation was performed to evaluate performance. The findings reveal a high overall efficiency assessment, with an average score of 4.43. © 2024 IEEE.

关键词： Efficiency

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Pattern-Driven Privacy: Columnar Binary Transformation for Robust Iris Recognition

Pattern-Driven Privacy: Columnar Binary Transformation for R...

引用

2024 International Research conference on Smart Computing and Systems Engineering, SCSE 2024

作者： Wickramaarachchi, Wiraj Udara Zhao, Dongdong Zhou, Junwei Xiang, Jianwen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Rajarata University of Sri Lanka Department of Information and Communication Technology Mihintale Sri Lanka

ISBN: (纸本)9798350375688

Iris is a distinct and strong biometric feature that is being employed as a method of authentication all over the world. Irreversibility, Renewability, and Unlinkability are the most critical aspects required to ensure the privacy of biometrics. Keeping recognition performance at a high level while providing privacy is a critical challenge that many researchers try to overcome. Information distortion through transforming original iris data into another form of information can be used to ensure privacy features. Therefore, this paper proposes a novel approach for iris authentication that ensures privacy features and high recognition performance. The binary representation of the existing iris template is transposed to another new binary representation by computing it column-wise. The Genuine Acceptance Rate (GAR) depicted for the proposed method is 97.11%, while the False Acceptance Rate (FAR) is 0.01%. Compared to the existing research works, the proposed method demonstrates an Equal Error Rate (EER) of 0.59%. The theoretical and experimental proof with explanation is stated in the paper to confirm the privacy aspects. © 2024 IEEE.

关键词： Biometrics

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Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

Wavelet Knowledge Distillation: Towards Efficient Image-to-I...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Linfeng Chen, Xin Tu, Xiaobing Wan, Pengfei Xu, Ning Ma, Kaisheng Tsinghua Univ Beijing Peoples R China Intel Corp Santa Clara CA 95051 USA Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-the-art GANs usually suffer from low efficiency and bulky memory usage. To tackle this challenge, firstly, this paper investigates GANs performance from a frequency perspective. The results show that GANs, especially small GANs lack the ability to generate high-quality high frequency information. To address this problem, we propose a novel knowledge distillation method referred to as wavelet knowledge distillation. Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands. As a result, the student GAN can pay more attention to its learning on high frequency bands. Experiments demonstrate that our method leads to 7.08x compression and 6.80x acceleration on CycleGAN with almost no performance drop. Additionally, we have studied the relation between discriminators and generators which shows that the compression of discriminators can promote the performance of compressed generators.

关键词： computer vision Image coding Memory management Generative adversarial networks Wavelet analysis Generators Discrete wavelet transforms

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Asynchronous I/O Persistence for In-Memory Database Servers: Leveraging io_uring to Optimize Redis Persistence 17th

Asynchronous I/O Persistence for In-Memory Database Servers:...

引用

17th International conference on Cloud Computing, CLOUD 2024, Held as Part of the Services conference Federation, SCF 2024

作者： Chen, Le-Gao Li, Yanzhi Laohakangvalvit, Tipporn Sugaya, Midori Innovative Global Program College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan Department of Computer Science and Engineering College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan

ISBN: (纸本)9783031771521

With constant hardware improvements allowing for increasingly large memory sizes, in-memory database servers have become an attractive option for various cloud applications. Even though in-memory database servers store all data in memory, there is still a need to persist data onto an I/O device for maintenance, updates, or protection against server crashes. In high throughput environments, persistence happens often, significantly hindering server throughput performance. Although popular in-memory database servers such as Redis or Dragonfly DB have adopted asynchronous I/O for socket operations, none have extended this approach for persistence. This results in significantly longer time taken for persistence, leading to prolonged reduced throughput. Given the recent introduction of io_uring, which has demonstrated significant performance improvements over past asynchronous I/O APIs, we have decided to implement it into Redis to evaluate its effectiveness in reducing these performance overheads. Our findings indicate that io_uring can significantly enhance performance in use cases with medium to large payload sizes but may hinder performances in use cases involving small payload sizes due to higher complexity and higher operation cost per datapoint. This method can likely be applied to other servers, such as Dragonfly DB and Memcached, to achieve similar benefits in appropriate scenarios. Additionally, in-memory database server persistence can serve as valuable benchmarks for further io_uring development and optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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high-dimensionality-adjusted consistent AIC in normal multivariate linear regression 28th

High-dimensionality-adjusted consistent AIC in normal multiv...

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28th International conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Yanagihara, Hirokazu Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan

For multivariate models, one of the important properties of a variable selection criterion is consistency, whereby the probability of selecting the true subset of explanatory variables approaches 1 as the sample size goes to infinity. However, in cases of high-dimensionality, the BIC, which is expected to be consistent under large-sample asymptotic theory, may not be consistent, and the AIC, which is said to be inconsistent under the same large-sample asymptotic theory, may be consistent. In this paper, we propose a high-dimensionality-adjusted consistent AIC called the HCAIC, which is an adjusted version of the consistent AIC proposed by Bozdogan (1987) regarding the effect of high-dimensionality through an asymptotic theory whereby the dimension of the vector of response variables allows both an increasing and a fixed condition as sample size increases. Through a series of numerical experiments, we verify that the proposed HCAIC is a high-performance information criterion in the sense that it can select the true subset of explanatory variables with high probability for any dimensionality. © 2024 The Authors.

关键词： Multiple linear regression

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CHARM-SYCL: New Unified programming Environment for Multiple Accelerator Types

CHARM-SYCL: New Unified Programming Environment for Multiple...

引用

2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Fujita, Norihisa Johnston, Beau Kobayashi, Ryohei Teranishi, Keita Lee, Seyong Boku, Taisuke Vetter, Jeffrey S. Center for Computational Sciences University of Tsukuba Ibaraki Tsukuba Japan Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798400707858

Addressing performance portability across diverse accelerator architectures has emerged as a major challenge in the development of application and programming systems for high-performance computing environments. Although recent programming systems that focus on performance portability have significantly improved productivity in an effort to meet this challenge, the problem becomes notably more complex when compute nodes are equipped with multiple accelerator types - each with unique performance attributes, optimal data layout, and binary formats. To navigate the intricacies of multi-accelerator programming, we propose CHARM-SYCL as an extension of our CHARM multi-accelerator execution environment [27]. This environment will combine our SYCL-based performance-portability programming front end with a back end for extremely heterogeneous architectures as implemented with the IRIS runtime from Oak Ridge National Laboratory. Our preliminary evaluation indicates potential productivity boost and reasonable performance compared to vendor-specific programming system and runtimes. © 2023 ACM.

关键词： computer software portability

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Comparison Electromagnetic Characteristics of Multi-phase SPMSM for Electric Vessel According to Pole/Slot Combination under Fault Operation 26

Comparison Electromagnetic Characteristics of Multi-phase SP...

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26th International conference on Electrical Machines and Systems, ICEMS 2023

作者： Wang, Chang-Hyeon Kim, Nam-Ho Bae, Su-Bin Kim, Yong-Min Jung, Sang-Yong Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Hyundai Transis Namyoung Korea Republic of

ISBN: (纸本)9798350317589

Electric vessel propulsion motors require high reliability and fault tolerance. Therefore, the initial design step of the motor and its interpretation during fault operation must be considered. So, through FEA, the phase angle difference of circuit current, the electromagnetic field performance during normal operation, and electromagnetic filed performance during fault operation are compared and analyzed for each pole/slot combination, and an excellent model is proposed. Shows control by appropriate control at the time of © 2023 IEEE.

关键词： Fault tolerance

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performance Evaluation of Transport Protocols and Roadmap to a high-performance Transport Design for Immersive Applications 14

Performance Evaluation of Transport Protocols and Roadmap to...

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14th International conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Ali, Inayat Hong, Seungwoo Park, Pyung-Koo Kim, Tae Yeon Intelligent Network Research Section Daejeon Korea Republic of

ISBN: (纸本)9798350335385

Immersive technologies such as virtual reality (VR), augmented reality (AR), and holograms will change users' digital experience. These immersive technologies have a multitude of applications, including telesurgeries, teleconferencing, Internet shopping, computer games, etc. Holographic-type communication (HTC) is a type of augmented reality media that provides an immersive experience to Internet users. However, HTC has different characteristics and network requirements, and the existing network architecture and transport protocols may not be able to cope with the stringent network requirements of HTC. Therefore, in this paper, we provide an in-depth and critical study of the transport protocols for HTC. We also discuss the characteristics and the network requirements for HTC. Based on the performance evaluation of the existing transport protocols, we propose a roadmap to design new high-performance transport protocols for immersive applications. © 2023 IEEE.

关键词： Augmented reality

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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9th IEEE International conference on Data Science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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