检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,547 篇 会议
77 篇 期刊文献
15 册 图书

馆藏范围

4,639 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,552 篇 工学
- 2,359 篇 计算机科学与技术...
- 1,162 篇 软件工程
- 491 篇 电气工程
- 405 篇 信息与通信工程
- 213 篇 控制科学与工程
- 210 篇 电子科学与技术（可...
- 98 篇 网络空间安全
- 80 篇 动力工程及工程热...
- 80 篇 生物工程
- 68 篇 光学工程
- 65 篇 机械工程
- 56 篇 生物医学工程（可授...
- 36 篇 建筑学
- 29 篇 化学工程与技术
- 29 篇 环境科学与工程（可...
611 篇 理学
- 374 篇 数学
- 143 篇 物理学
- 93 篇 生物学
- 74 篇 系统科学
- 69 篇 统计学（可授理学、...
464 篇 管理学
- 345 篇 管理科学与工程(可...
- 134 篇 图书情报与档案管...
- 129 篇 工商管理
- 65 篇 公共管理
111 篇 法学
- 103 篇 社会学
104 篇 医学
- 73 篇 公共卫生与预防医...
- 31 篇 临床医学
- 29 篇 基础医学(可授医学...
65 篇 文学
- 65 篇 新闻传播学
39 篇 经济学
- 38 篇 应用经济学
17 篇 教育学
10 篇 农学
1 篇 艺术学

主题

1,194 篇 computer archite...
427 篇 hardware
344 篇 high performance...
295 篇 computational mo...
255 篇 concurrent compu...
236 篇 application soft...
212 篇 computer science
205 篇 parallel process...
180 篇 distributed comp...
178 篇 costs
171 篇 delay
170 篇 grid computing
168 篇 bandwidth
164 篇 field programmab...
154 篇 resource managem...
153 篇 cloud computing
144 篇 computer network...
142 篇 throughput
129 篇 processor schedu...
126 篇 scalability

机构

22 篇 institute of com...
16 篇 graphic era deem...
14 篇 chitkara univers...
13 篇 university of ch...
11 篇 college of compu...
10 篇 school of comput...
10 篇 computer science...
9 篇 georgia inst tec...
9 篇 computer systems...
9 篇 intel corp santa...
8 篇 department of co...
8 篇 department of el...
8 篇 ibm thomas j. wa...
8 篇 barcelona superc...
8 篇 mathematics and ...
8 篇 quantum universi...
8 篇 中国电子科技集团...
8 篇 mathematics and ...
8 篇 key laboratory o...
8 篇 univ fed rio gra...

作者

14 篇 navaux philippe ...
10 篇 satvik vats
9 篇 vats satvik
9 篇 i. foster
9 篇 d.k. panda
8 篇 dhabaleswar k. p...
8 篇 dongarra jack
8 篇 loh gabriel h.
8 篇 guedes dorgival
8 篇 viktor k. prasan...
8 篇 prasanna viktor ...
8 篇 mutlu onur
7 篇 shiva mehta
7 篇 borin edson
7 篇 chong frederic t...
7 篇 torrellas josep
7 篇 kim nam sung
7 篇 li chao
7 篇 zhou huiyang
7 篇 ferreira renato

语言

4,482 篇 英文
136 篇 其他
20 篇 中文
2 篇 葡萄牙文
1 篇 法文

检索条件"任意字段=15th Symposium on Computer Architecture and High Performance Computing"

共 4639 条记录，以下是1021-1030 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Autonomic Secure HPC Fabric architecture 15

Autonomic Secure HPC Fabric Architecture

引用

15th IEEE/ACS International Conference on computer Systems and Applications (AICCSA)

作者： Fargo, Farah Sury, Samantika Intel Corp Hudson MA 01749 USA

ISBN: (纸本)9781538691205

Cloud computing is the major paradigm in today's IT world with the capabilities of security management, high performance, flexibility, scalability. Customers valuing these features can better benefit if they use a cloud environment built using HPC fabric architecture. However, security is still a major concern, not only on the software side but also on the hardware side. there are multiple studies showing that the malicious users can affect the regular customers through the hardware if they are co-located on the same physical system. therefore, solving possible security concerns on the HPC fabric architecture will clearly make the fabric industries leader in this area. In this paper, we propose an autonomic HPC fabric architecture that leverages both resilient computing capabilities and adaptive anomaly analysis for further security.

关键词： Omni-Path Fabric high performance computing Cloud computing Resiliency Cloud Security

来源：评论

学校读者我要写书评

暂无评论

Efficient differentiated storage architecture for large-scale flow tables in openflow networks 21

Efficient differentiated storage architecture for large-scal...

引用

21st IEEE International Conference on high performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xiong, Bing Wu, Rengeng Liao, Zhuofan Wang, Jin School of Computer AND Communication Engineering Changsha University of Science AND Technology Changsha410114 China Department of Computer and Information Sciences Temple University Philadelphia19122 United States Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science AND Technology Changsha China

ISBN: (纸本)9781728120584

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. However, it leads to large-scale flow tables and poses serious challenges on their storage resources and lookup performance in OpenFlow switches. this paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow networks. Firstly, we investigate into the impact of wildcards in match fields on packet-in-batch property within a flow based on network traffic locality. then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. the experimental results indicate that our proposed storage architecture obviously outperforms the traditional one applying the elephant/mice flow differentiation method in terms of TCAM hit rates and average flow table access time. © 2019 IEEE.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

Cross Architectural Power Modelling

Cross Architectural Power Modelling

引用

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

作者： Kai Chen Peter Kilpatrick Dimitrios S. Nikolopoulos Blesson Varghese Queen’s University Belfast UK Virginia Tech USA

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Existing power modelling research focuses on the model rather than the process for developing models. An automated power modelling process that can be deployed on different processors for developing power models with high accuracy is developed. For this, (i) an automated hardware performance counter selection method that selects counters best correlated to power on both ARM and Intel processors, (ii) a noise filter based on clustering that can reduce the mean error in power models, and (iii) a two stage power model that surmounts challenges in using existing power models across multiple architectures are proposed and developed. the key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual method reported in the literature, (ii) the noise filter reduces the mean error in power models by up to 55%, and (iii) the two stage power model can predict dynamic power with less than 8% error on both ARM and Intel processors, which is an improvement over classic models.

关键词： Hardware Program processors Computational modeling Power measurement computer architecture Mathematical model Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Energy efficiency and performance modeling of stencil applications on manycore and GPU computing resources

Energy efficiency and performance modeling of stencil applic...

引用

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

作者： Krzysztof Kurowski Miłosz Ciżnicki Jan Węglarz Poznań Supercomputing and Networking Center affiliated to IBCH PAS Poznań Poland Poznań University of Technology Poznań Poland

ISBN: (数字)9781728160955

One of the most critical application areas for many operational high-performance computing systems worldwide is a numerical weather prediction. Such complex predictions and corresponding computing models have to solve a large number of Partial Differential Equations using stencil computations on structured grids within tight production schedules. A stencil kernel within simulations is often the most demanding computing part and naturally may impact the energy consumption of the whole HPC system. In this paper, we introduce new functional extensions developed for DCworms and GSSIM simulators to predict the energy efficiency of extreme-scale stencil applications on heterogeneous computing resources, in particular consisting of a large number of many-core and GPUs. New energy-efficiency metrics for modeling of heterogeneous computing resources have been successfully added to the simulators thanks to their pluggable and extensible architectures. Our recent improvements related to application-specific performance models in simulators help users to take into account many relevant application parameters, in particular, those related to detailed energy consumption on heterogeneous HPC resources. We show in this paper how to extract stencil application-specific parameters based on real experiments. Moreover, we demonstrate how those parameters can be applied for modeling and simulation experiments to evaluate the overall performance and energy consumption of stencil computations on manycore CPUs and GPUs. Finally, we discuss new DCworms simulator capabilities and demonstrate added-values of performance analysis tools.

关键词： Computational modeling computer architecture Hardware Graphics processing units Energy consumption Predictive models

来源：评论

学校读者我要写书评

暂无评论

Calibration of the integrating sphere for O4 in KAGRA gravitational wave telescope. 38

Calibration of the integrating sphere for O4 in KAGRA gravit...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Fujii, Shingo Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-8585 Japan Institute of Physics Academia Sinica 128 Sec. 2 Academia Rd. Na

KAGRA, the gravitational wave telescope located in Japan, has started the 4th international observing run called O4 with LIGO and Virgo. the accuracy of parameter estimation for gravitational wave source objects depends on the accuracy of calibration. LIGO, Virgo, and KAGRA use the same calibration method called the photon calibrator (Pcal) method. In the Pcal system, we will obtain the precise displacement of a mirror induced by a well-calibrated radiation pressure of a laser beam which is injected into the mirror. As the uncertainty of the Pcal system is primarily limited by the uncertainty of the laser power measurement, we use integrating sphere (IS) power meters, which are considered to have good accuracy and precision, for the laser power measurement. However, during our calibration measurements in O3GK, we found that there were unidentified systematic errors in the laser power measurement. therefore, we investigated the incident direction dependence and polarization dependence of the IS power meter. According to our study, the angular dependence differs between two types of ISs, namely GPS (General Purpose Spheres) and LPM (Laser Power Measurement Spheres), which are utilized in our calibration system. the angular dependence of the GPS type was greater than that of the LPM type. the GPS measurement showed an approximate 8.4% increase from the value obtained with perpendicular incidence at maximum. In contrast, the LPM measurement showed a 0.2% increase from the value obtained with perpendicular incidence at maximum. As to polarization dependence, the effect of that on the LPM type was within the range of -0.06% to 0.06%. From this study, we found a possibility that the angle dependence was able to explain the uncertainty observed in O3GK. In this proceeding, we report the angular and polarization dependence of ISs and consider the effect on the uncertainty of the KAGRA Pcal system. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Spheres

来源：评论

学校读者我要写书评

暂无评论

Co-occurrence Morphological Edge Detection 12

Co-occurrence Morphological Edge Detection

引用

IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of things (ithings) / 5th IEEE Int Conf on Smart Data

作者： Yu, Heng Lu, Ying Yu, Cong Zhao, Hongya Wang, Lei 3 High Sch Bengbu Anhui China Bengbu Peoples R China China Acad Launch Vehicle Technol CALT Beijing Peoples R China Shenzhen Vocat & Tech Coll Ind Ctr Shenzhen Peoples R China

ISBN: (纸本)9781728129808

Morphological edge detection plays a significant role in image processing and computer vision. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects also. However, edge detection is challenged and difficult to find the discontinuities in surface orientation, changes in material properties and variations in scene illuminations. In this paper, an improved co-occurrence morphology edge detection algorithm is proposed. We exploit the co-occurrence filter (a combination of bilateral filter and gray-level co-occurrence matrix) to handle with noisy images, and two novel structural elements are used for edge detection. the proposed edge detection algorithm can reasonably consider noise reduction and preserve the detailed edge information. the experimental results demonstrate the superior performance with some existing methods and exhibit better anti-noise ability.

关键词： Co-occurrence Filter Edge Detection Image Processing

来源：评论

学校读者我要写书评

暂无评论

Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

Event reconstruction using pattern spectra and convolutional...

引用

7th Heidelberg International symposium on high-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Breen, S. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

the Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. the simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. the application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

来源：评论

学校读者我要写书评

暂无评论

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

SOL: Effortless Device Support for AI Frameworks without Sou...

引用

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

作者： Nicolas Weber Felipe Huici NEC Laboratories Europe

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Modern high performance computing clusters heavily rely on accelerators to overcome the limited compute power of CPUs. these supercomputers run various applications from different domains such as simulations, numerical applications or artificial intelligence (AI). As a result, vendors need to be able to efficiently run a wide variety of workloads on their *** the AI domain this is in particular exacerbated by the existance of a number of popular frameworks (e.g, PyTorch, TensorFlow, etc.) that have no common code base, and can vary in functionality. the code of these frameworks evolves quickly, making it expensive to keep up with all changes and potentially forcing developers to go through constant rounds of *** this paper we explore how to provide hardware support in AI frameworks without changing the framework's source code in order to minimize maintenance overhead. We introduce SOL, an AI acceleration middleware that provides a hardware abstraction layer that allows us to transparently support heterogenous hardware. As a proof of concept, we implemented SOL for PyTorch with three backends: CPUs, GPUs and vector processors.

关键词： Artificial intelligence Hardware Libraries computer architecture Optimization Layout Middleware

来源：评论

学校读者我要写书评

暂无评论

FP-AMG: FPGA-Based Acceleration Framework for Algebraic Multigrid Solvers

FP-AMG: FPGA-Based Acceleration Framework for Algebraic Mult...

引用

Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Pouya Haghi Tong Geng Anqi Guo Tianqi Wang Martin Herbordt Department of Electrical and Computer Engineering Boston University Boston MA USA Department of Physics University of Science and Technology of China Hefei China

ISBN: (数字)9781728158037

ISBN: (纸本)9781728158044

Partial Differential Equations (PDEs) are fundamental to many real-world scientific computing applications and so their optimization has undergone decades of study. Algebraic multigrid (AMG) is one of the most well-known solvers, being widely adopted in high performance computing (HPC) due to its good scalability. Acceleration of AMG is known to be very challenging, due to the following reasons: (1) irregular computation patterns, (2) random memory access, and (3) a large number of kernels with various computation types. To the best of our knowledge, there is no prior work on FPGA-based acceleration of AMG. To tackle these challenges, we propose an efficient FPGA-based reconfigurable framework, called FP-AMG, for high-performance AMG calculation. In order to obtain full pipeline utilization, we propose a novel and scalable architecture that can be reused for all kernels in AMG. Given that AMG is strictly memory-bound, we propose algorithmic and architectural optimizations to ensure nearly ideal use of memory bandwidth. the efficiency of FP-AMG is evaluated with six well-known benchmarks on two FPGA devices: one with and one without high bandwidth memory (HBM). the experimental results are compared with a highly optimized Intel Xeon E5-2680-V4 implementation of the state-of-the-art HYPRE library. Our experiments show that FP-AMG can achieve average speedups of 2.5× and 6.6×, for FPGAs without and with HBM, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scalable performance awareness for in situ scientific applications 15

Scalable performance awareness for in situ scientific applic...

引用

15th IEEE International Conference on eScience, eScience 2019

作者： Wolf, Matthew Choi, Jong Eisenhauer, Greg Ethier, Stephane Huck, Kevin Klasky, Scott Logan, Jeremy Malony, Allen Wood, Chad Dominski, Julien Merlo, Gabriele Oak Ridge National Laboratory Oak RidgeTN United States School of Computer Science Georgia Institute of Technology AtlantaGA United States Princeton Plasma Physics Laboratory PrincetonNJ United States University of Tennessee KnoxvilleTN United States University of Oregon EugeneOR United States University of Texas AustinTX United States

ISBN: (纸本)9781728124513

Part of the promise of exascale computing and the next generation of scientific simulation codes is the ability to bring together time and spatial scales that have traditionally been treated separately. this enables creating complex coupled simulations and in situ analysis pipelines, encompassing such things as 'whole device' fusion models or the simulation of cities from sewers to rooftops. Unfortunately, the HPC analysis tools that have been built up over the preceding decades are ill suited to the debugging and performance analysis of such computational ensembles. In this paper, we present a new vision for performance measurement and understanding of HPC codes, MonitoringAnalytics (MONA). MONA is designed to be a flexible, high performance monitoring infrastructure that can perform monitoring analysis in place or in transit by embedding analytics and characterization directly into the data stream, without relying upon delivering all monitoring information to a central database for post-processing. It addresses the trade-offs between the prohibitively expensive capture of all performance characteristics and not capturing enough to detect the features of interest. We demonstrate several uses of MONA;capturing and indexing multi-executable performance profiles to enable later processing, extraction of performance primitives to enable the generation of customizable benchmarks and performance skeletons, and extracting communication and application behaviors to enable better control and placement for the current and future runs of the science ensemble. Relevant performance information based on a system for MONA built from ADIOS and SOSflow technologies is provided for DOE science applications and leadership machines. © 2019 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共464页 << < 99 100 101 102 103 104 105 106 107 108 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：