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Research on performance optimization of virtual data space across WAN

Frontiers of computer science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“high performance computing Virtual Data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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Grading retinopathy of prematurity with feedforward network 11th

Grading retinopathy of prematurity with feedforward network

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11th International Conference on Soft computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Giraddi, Shantala Chickerur, Satyadhyan Annigeri, Nirmala School of Computer Science and Engineering KLE Technological University HubliKarnataka580031 India Center for High Performance Computing KLE Technological University HubliKarnataka580031 India

ISBN: (纸本)9783030493448

Retinopathy of Prematurity is a disease that affects premature infants having low birth weight. The disease may lead to blindness unless timely treatment is not provided. Because of the high birth rate premature babies and expanded neonatal care, the incidence of ROP is worrying in India today. There is an urgent need to create awareness about disease. The researchers propose a new approach of grading ROP with feed forward networks using second order texture features. Experiments are conducted with six different architectures of Feed Forward Networks. Second order texture features mean, entropy, contrast, correlation, homogeneity, energy from Gray level co-occurrence matrix (GLCM) are considered. The results obtained indicate Feed forward network offers an easy yet effective paradigm for ROP Grading. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Grading

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Automatic facet extraction based on multidimensional semantic index

Automatic facet extraction based on multidimensional semanti...

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2012 8th International Conference on Semantics, Knowledge and Grids, SKG 2012

作者： Wei, Xiao Luo, Xiangfeng Li, Qing School of Computing Engineering and Science High Performance Computing Center Shanghai University Shanghai 200072 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 200235 China City University of Hong Kong Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780769547947

Faceted search on web pages needs exact facets. However, it is difficult to extract facets exactly from web pages because the web pages are unstructured and lack of facet information. Therefore, facet extraction is a key to faceted search. This paper proposed a method of extracting facets automatically from unstructured web pages to improve the faceted search on web. The Multidimensional Semantic Index (MDSI) of web pages is constructed by mining all kinds of semantic relations among the words from web pages, which creates a semantic-rich index for web pages. In MDSI, the differently dimensional semantic indexes are bridged by mining the semantic mapping between them. Based on the MDSI of web pages, the facets are extracted by analyzing semantic mapping relations in MDSI. To validate the effect of the proposed method, two datasets are constructed and the experimental results show that the proposed method is feasible and comparatively precise. © 2012 IEEE.

关键词： Extraction

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An Investigation into Power Aware Aspects of Rendering 3D Models on Multi-Core Processors

An Investigation into Power Aware Aspects of Rendering 3D Mo...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Konnurmath, Guruprasad Chickerur, Satyadhyan School of Computer Science and Engineering KLE Technological University Hubballi580031 India Centre for High Performance Computing K L e Technological University Hubballi580031 India

Graphics Processing Units (GPUs) are designed to process numerically intensive applications and specially customized with additional computational power to achieve efficient rendering of 3D applications. Shaders are basically the computer programs that run on graphics rendering pipeline and inform about how to process and render each pixel, to perform efficient rendering of 3D applications. Shaders included in our Geometric complexity, texture resolution, per-pixel lighting. These shaders expose GPUs to suffer more power utilization during rendering process. In this paper, initially GPU power usage is investigated at run-time for rendering configuration to define shader parameters on a 3D scene and then power prediction model is proposed at different shader calls on GPU to generate power-aware dynamically reconfigurable rendering model. In the implementation process we integrate the TensorFlow management library routines and dynamic voltage and frequency scheduling (DVFS) mechanism to improve efficiency in rendering process and reach optimal power savings. To analyze the power efficiency of GPUs, shader parameters such as geometric complexity, texture resolution, per-pixel lighting, filtering that expose more towards power consumption are experimented with common GPU workloads during rendering process, then frequency and voltage is altered based on the framerate monitoring. Also in addition to this the frame rate is monitored and maintained in such a way that GPU frequency is adjusted in parallel if the framerate is within the acceptable range so that unnecessary power usage is reduced. During the experimental tests a lower limit of 30 frames per second and upper limit of 60 frames per second is configured. The GPU frequency is reduced if the existing framerate is greater than upper configured limit and frequency range is increased if framerate is below the configured lower limit. Compared with the previous state of art, results show power savings around 40% and imp

关键词： Graphics processing unit

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Grid enabled optimization

Grid enabled optimization

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European Grid Conference on Advances in Grid computing - EGC 2005

作者： Ng, Hee-Khiang Ong, Yew-Soon Hung, Terence Lee, Bu-Sung School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore Institute of High Performance Computing Singapore Science Park II Singapore 117528 Singapore

In this paper, we present a scalable parallel framework, which employs grid computing technologies, for solving computationally expensive and intractable design problems. Using an aerodynamic airfoil design optimization problem as an example the application of the grid computing strategies is discussed. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Distributed computer systems

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Vision Transformers with Hierarchical Attention

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Machine Intelligence Research 2024年第4期21卷 670-683页

作者： Yun Liu Yu-Huan Wu Guolei Sun Le Zhang Ajad Chhatkuli Luc Van Gool Institute for Infocomm Research(I2R) A*STARSingapore 138632Singapore Institute of High Performance Computing(IHPC) A*STARSingapore 138632Singapore Computer Vision Lab ETH ZürichZürich 8092Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China

This paper tackles the high computational/space complexity associated with multi-head self-attention(MHSA)in vanilla vision *** this end,we propose hierarchical MHSA(H-MHSA),a novel approach that computes self-attention in a hierarchical ***,we first divide the input image into patches as commonly done,and each patch is viewed as a ***,the proposed H-MHSA learns token relationships within local patches,serving as local relationship ***,the small patches are merged into larger ones,and H-MHSA models the global dependencies for the small number of the merged *** last,the local and global attentive features are aggregated to obtain features with powerful representation *** we only calculate attention for a limited number of tokens at each step,the computational load is reduced ***,H-MHSA can efficiently model global relationships among tokens without sacrificing fine-grained *** the H-MHSA module incorporated,we build a family of hierarchical-attention-based transformer networks,namely *** demonstrate the superiority of HAT-Net in scene understanding,we conduct extensive experiments on fundamental vision tasks,including image classification,semantic segmentation,object detection and instance ***,HAT-Net provides a new perspective for vision *** and pretrained models are available at https://***/yun-liu/HAT-Net.

关键词： Vision transformer hierarchical attention global attention local attention scene understanding.

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Identity-based authentication in cloud storage sharing

Identity-based authentication in cloud storage sharing

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International Conference on Multimedia Information Networking and Security

作者： Kang, Lishan Zhang, Xuejie School of Computer Science and Engineering Yunnan University Kunming Yunnan Province 650091 China High Performance Computing Center Yunnan Univeristy Kunming Yunnan Province 650091 China

ISBN: (纸本)9780769542584

This paper aims at the heavy load of thin client in cloud storage sharing due to traditional mutual authentication scheme with inherent public-key operation and interaction for many times, and proposes an Identity-Based Authentication (IBA) scheme which is of short key size, identity-based, non-interactive. This scheme divides the sharing users into the very same domain and in this domain relies on the sharing global master key to exercise mutual authentication. By analysis of performance, we can find user-side performance is improved for over two times both in computational cost and in communicating cost. The IBA scheme can be enabled by an emerging cryptographic technique from the bilinear pairing and its security can be assured by the Bilinear Diffie-Hellman Problem (BDHP). © 2010 IEEE.

关键词： Authentication

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Distributed Deep Learning with Data Parallelism for Diabetic Retinopathy Classification 1

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7th International Conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Patil, Sanskruti S. Patil, Mahesh S. Chickerur, Satyadhyan Giraddi, Shantala Shahapur, Seetharam N. Hadalageri, Anup School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India Center for High Performance Computing KLE Technological University Karnataka Hubballi580031 India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

The large-scale model training is typically slower and necessitates high-performance workstations and Distributed Deep Learning (DDL). The DDL models trained on a massive volume of data can outperform single accelerators being used, i.e., the performance of deep learning (DL) models can be enhanced by using distributed and parallel deep learning methods. But the DDL models need to be redesigned and evaluated for particular hardware resources and applications, requiring the literature on various DDL model evaluations for assistance. Authors in the proposed work design and evaluate DDL with data parallelism for classifying Diabetic retinopathy (DR) images. A performance comparative study on the DDL strategy implemented on DenseNet is presented where the model is trained on multi-GPUs with counts from 1 to 8. The results show that the model takes less time when trained on multi-GPUs compared to single GPU. The detailed evaluation analysis of DDL with data parallelism is presented in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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GECKA3D: A 3D game engine for commonsense knowledge acquisition 29

GECKA3D: A 3D game engine for commonsense knowledge acquisit...

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29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016

作者： Cambria, Erik Nguyen, Tam V. Cheng, Brian Kwok, Kenneth Sepulveda, Jose School of Computer Engineering Nanyang Technological University Singapore ARTIC Research Center Singapore Polytechnic Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9781577357568

Commonsense knowledge representation and reasoning is key tor tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Serious games

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Towards a data-driven approach to scenario generation for serious games

Towards a data-driven approach to scenario generation for se...

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作者： Luo, Linbo Yin, Haiyan Cai, Wentong Lees, Michael Othman, Nasri Bin Zhou, Suiping School of Computer Science and Technology Xidian University Xi'an China School of Computer Engineering Nanyang Technological University Singapore Section Computational Science University of Amsterdam Netherlands Institute of High Performance Computing Singapore School of Science and Technology Middlesex University United Kingdom

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario's training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance. Copyright © 2014 John Wiley & Sons, Ltd.

关键词： Serious games

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