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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

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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

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

Event reconstruction using pattern spectra and convolutional...

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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

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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

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

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

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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

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Status of KAGRA 38

Status of KAGRA

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38th International Cosmic Ray Conference, ICRC 2023

作者： Itoh, Yousuke 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. Osaka Metropolitan University Sumiyoshi Osaka3-3-138 Japan 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 Inst

the gravitational wave detectors LIGO and Virgo have so far detected gravitational wave signals from 90 compact binary coalescences including two from neutron star binary mergers. those detections are revolutionizing our understanding of the universe. this ongoing revolution will be accelerated by more accurate localizations of the gravitational wave sources and by longer coincident observation by multi-detectors, which will be made possible using as many detectors as possible. KAGRA is a gravitational wave detector built at Kamioka in Gifu prefecture, Japan, well away from the LIGO and Virgo detectors, with unique features of having cryogenic mirrors and being built underground. KAGRA can be a prototype for next-generation gravitational wave detectors and our experiences benefit those detectors that would have those features to achieve further sensitivity to gravitational waves. In this presentation, the status of KAGRA is given. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gravitational effects

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

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

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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.

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Scalable performance awareness for in situ scientific applications 15

Scalable performance awareness for in situ scientific applic...

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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

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Energy Efficient Algorithms based on VM Consolidation for Cloud computing: Comparisons and Evaluations

Energy Efficient Algorithms based on VM Consolidation for Cl...

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

作者： Qiheng Zhou Minxian Xu Sukhpal Singh Gill Chengxi Gao Wenhong Tian Chengzhong Xu Rajkumar Buyya Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Electronic Engineering and Computer Science Queen Mary University of London UK School of Software and Information Engineering University of Electronic Science and Technology of China China School of Computing and Information Systems University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

Cloud computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. this makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. the experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

关键词： Cloud computing Data centers Energy consumption Heuristic algorithms Computational modeling Scheduling Monitoring

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Automatic Generation of Warp-Level Primitives and Atomic Instructions for Fast and Portable Parallel Reduction on GPUs 2019

Automatic Generation of Warp-Level Primitives and Atomic Ins...

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17th IEEE/ACM International symposium on Code Generation and Optimization (CGO)

作者： De Gonzalo, Simon Garcia Huang, Sitao Gomez-Luna, Juan Hammond, Simon Mutlu, Onur Hwu, Wen-mei UIUC CS & Coordinated Sci Lab Champaign IL 61820 USA UIUC ECE & Coordinated Sci Lab Champaign IL USA Swiss Fed Inst Technol Comp Sci Zurich Switzerland Sandia Natl Labs Scalable Comp Architecture Livermore CA 94550 USA

ISBN: (纸本)9781728114361

Since the advent of GPU computing, GPU hardware has evolved at a fast pace. Since application performance heavily depends on the latest hardware improvements, performance portability is extremely challenging for GPU application library developers. Portability becomes even more difficult when new low-level instructions are added to the ISA (e.g., warp shuffle instructions) or the microarchitectural support for existing instructions is improved (e.g., atomic instructions). Library developers, besides re-tuning the code for new hardware features, deal with the performance portability issue by hand-writing multiple algorithm versions that leverage different instruction sets and microarchitectures. high-level programming frameworks and Domain Specific Languages (DSLs) do not typically support low-level instructions (e.g., warp shuffle and atomic instructions), so it is painful or even impossible for these programming systems to take advantage of the latest architectural improvements. In this work, we design a new set of high-level APIs and qualifiers, as well as specialized Abstract Syntax Tree (AST) transformations for high-level programming languages and DSLs. Our transformations enable warp shuffle instructions and atomic instructions (on global and shared memories) to be easily generated. We show a practical implementation of these transformations by building on Tangram, a high-level kernel synthesis framework. Using our new language and compiler extensions, we implement parallel reduction, a fundamental building block used in a wide range of algorithms. Parallel reduction is representative of the performance portability challenge, as its performance heavily depends on the latest hardware improvements. We compare our synthesized parallel reduction to another high-level programming framework and a hand-written high-performance library across three generations of GPU architectures, and show up to 7.8x speedup (2x on average) over hand-written code.

关键词： Graphics processing units Programming computer architecture Libraries Hardware DSL Microarchitecture

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