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Determination of checkpointing intervals for malleable applications

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arXiv 2017年

作者： Raghavendra, K. Vadhiyar, Sathish S. Department of Computer Science and Engineering Indian Institute of Technology Madras India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Selecting optimal intervals of checkpointing an application is important for minimizing the run time of the application in the presence of system failures. Most of the existing efforts on checkpointing interval selection were developed for sequential applications while few efforts deal with parallel applications where the applications are executed on the same number of processors for the entire duration of execution. Some checkpointing systems support parallel applications where the number of processors on which the applications execute can be changed during the execution. We refer to these kinds of parallel applications as malleable applications. In this paper, we develop a performance model for malleable parallel applications that estimates the amount of useful work performed in unit time (UWT) by a malleable application in the presence of failures as a function of checkpointing interval. We use this performance model function with different intervals and select the interval that maximizes the UWT value. By conducting a large number of simulations with the traces obtained on real supercomputing systems, we show that the checkpointing intervals determined by our model can lead to high efficiency of applications in the presence of failures. Copyright © 2017, The Authors. All rights reserved.

关键词： Systems engineering

Dynamic load balancing strategies for graph applications on GPUs

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arXiv 2017年

作者： Raval, Ananya Nasre, Rupesh Kumar, Vivek Vasudevan, R. Vadhiyar, Sathish Pingali, Keshav Department of Computational and Data Sciences Indian Institute of Science Bangalore Department of Computer Science and Engineering Indian Institute of Technology Madras Institute for Computational Engineering and Sciences University of Texas at Austin United States Department of Computer Science University of Texas at Austin United States

Acceleration of graph applications on GPUs has found large interest due to the ubiquitous use of graph processing in various domains. The inherent irregularity in graph applications leads to several challenges for parallelization. A key challenge, which we address in this paper, is that of load-imbalance. If the work-assignment to threads uses node-based graph partitioning, it can result in skewed task-distribution, leading to poor load-balance. In contrast, if the work-assignment uses edge-based graph partitioning, the load-balancing is better, but the memory requirement is relatively higher. This makes it unsuitable for large graphs. In this work, we propose three techniques for improved load-balancing of graph applications on GPUs. Each technique brings in unique advantages, and a user may have to employ a specific technique based on the requirement. Using Breadth First Search and Single Source Shortest Paths as our processing kernels, we illustrate the effectiveness of each of the proposed techniques in comparison to the existing node-based and edge-based mechanisms. Copyright © 2017, The Authors. All rights reserved.

关键词： Program processors

Revisiting simple neural networks for learning representations of knowledge graphs

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arXiv 2017年

作者： Ravishankar, Srinivas Chandrahas Talukdar, Partha Pratim Department of Computer Science R.V. College of Engineering Bangalore India Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge graph

OpenAccess Series in Informatics

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Preface

OpenAccess Series in Informatics 2019年 70卷 IX-X页

作者： Eskevich, Maria de Melo, Gerard Fäth, Christian McCrae, John P. Buitelaar, Paul Chiarcos, Christian Klimek, Bettina Dojchinovski, Milan CLARIN ERIC Utrecht Netherlands Department of Computer Science Rutgers University New BrunswickNJ United States Applied Computational Linguistics Goethe University Frankfurt Germany Insight Centre for Data Analytics National University of Ireland Galway Ireland Agile Knowledge Engineering and Semantic Web University of Leipzig Germany

The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

NeoN: Neuromorphic control for autonomous robotic navigation

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NeoN: Neuromorphic control for autonomous robotic navigation

IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

作者： J. Parker Mitchell Grant Bruer Mark E. Dean James S. Plank Garrett S. Rose Catherine D. Schuman Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN Computational Data Analytics Oak Ridge National Laboratory Oak Ridge TN

In this paper we describe the use of a new neuromorphic computing framework to implement the navigation system for a roaming, obstacle avoidance robot. Using a Dynamic Adaptive Neural Network Array (DANNA) structure, our TENNLab (Laboratory of Tennesseans Exploring Neural Networks) hardware/software co-design framework and evolutionary optimization (EO) as the training algorithm, we create, train, implement, and test a spiking neural network autonomous robot control system using an array of neuromorphic computing elements built on an FPGA. The simplicity and flexibility of the DANNA neuromorphic computing elements allow for sufficient scale and connectivity on a Xilinx Kintex-7 FPGA to support sensory input and motor control for a mobile robot to navigate a dynamically changing environment. We further describe how more complex capabilities can be added using the same platform, e.g. object identification and tracking.

关键词： Neurons Robot sensing systems Neuromorphics Biological neural networks computer architecture

Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

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arXiv 2017年

作者： Qian, Chao Bian, Chao Jiang, Wu Tang, Ke Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a type of general-purpose randomized optimization algorithm, have been shown to be able to solve noisy optimization problems well. However, previous theoretical analyses of EAs mainly focused on noise-free optimization, which makes the theoretical understanding largely insufficient for the noisy case. Meanwhile, the few existing theoretical studies under noise often considered the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation;while in many realistic applications, several bits of a solution can be changed simultaneously. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results. Since our analysis discloses that the (1+1)-EA can be efficient only under low noise levels, we also study whether the sampling strategy can bring robustness to noise. We prove that using sampling can significantly increase the largest noise level allowing a polynomial running time, that is, sampling is robust to noise. Copyright © 2017, The Authors. All rights reserved.

关键词： computational complexity

Fundamental Physics Opportunities with the Next-Generation Event Horizon Telescope

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arXiv 2023年

作者： Ayzenberg, Dimitry Blackburn, Lindy Brito, Richard Britzen, Silke Broderick, Avery E. Carballo-Rubio, Raúl Cardoso, Vitor Chael, Andrew Chatterjee, Koushik Chen, Yifan Cunha, Pedro V.P. Davoudiasl, Hooman Denton, Peter B. Doeleman, Sheperd S. Eichhorn, Astrid Eubanks, Marshall Fang, Yun Foschi, Arianna Fromm, Christian M. Galison, Peter Ghosh, Sushant G. Gold, Roman Gurvits, Leonid I. Hadar, Shahar Held, Aaron Houston, Janice Hu, Yichao Johnson, Michael D. Kocherlakota, Prashant Natarajan, Priyamvada Olivares, Héctor Palumbo, Daniel Pesce, Dominic W. Rajendran, Surjeet Roy, Rittick Saurabh Shao, Lijing Tahura, Shammi Tamar, Aditya Tiede, Paul Vincent, Frédéric H. Visinelli, Luca Wang, Zhiren Wielgus, Maciek Xue, Xiao Yakut, Kadri Yang, Huan Younsi, Ziri Theoretical Astrophysics Eberhard-Karls Universität Tübingen TübingenD-72076 Germany Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Harvard University 20 Garden Street CambridgeMA02138 United States CENTRA Departamento de Física Instituto Superior Técnico – IST Universidade de Lisboa – UL Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Waterloo Centre for Astrophysics University of Waterloo WaterlooONN2L 3G1 Canada CP3-Origins University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Florida Space Institute University of Central Florida 12354 Research Parkway Partnership 1 OrlandoFL32826 United States Niels Bohr International Academy Niels Bohr Institute Blegdamsvej 17 Copenhagen2100 Denmark Princeton Gravity Initiative Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Physics University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States Campus de Santiago Aveiro3810-183 Portugal High Energy Theory Group Physics Department Brookhaven National Laboratory UptonNY11973 United States Asteroid Initiatives LLC United States Perimeter Institute for Theoretical Physics WaterlooONN2L2Y5 Canada Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Institut für Theoretische Physik und Astrophysik Universität Würzburg Emil-Fischer-Strasse 31 Würzburg97074 Germany Institut für Theoretische Physik Goethe Universität Frankfurt Max-von

The Event Horizon Telescope (EHT) Collaboration recently published the first images of the supermassive black holes in the cores of the Messier 87 and Milky Way galaxies. These observations have provided a new means to study supermassive black holes and probe physical processes occurring in the strong-field regime. We review the prospects of future observations and theoretical studies of supermassive black hole systems with the next-generation Event Horizon Telescope (ngEHT), which will greatly enhance the capabilities of the existing EHT array. These enhancements will open up several previously inaccessible avenues of investigation, thereby providing important new insights into the properties of supermassive black holes and their environments. This review describes the current state of knowledge for five key science cases, summarising the unique challenges and opportunities for fundamental physics investigations that the ngEHT will enable. Copyright © 2023, The Authors. All rights reserved.

关键词： Telescopes

Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi