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arXiv 2020年

作者： Qin, Xinming Shang, Honghui Xu, Lei Hu, Wei Yang, Jinlong Li, Shigang Zhang, Yunquan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory for Physical Sciences at Microscale Department of Chemical Physics Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China

Hybrid density-functional calculation is one of the most commonly adopted electronic structure theory used in computational chemistry and materials science because of its balance between accuracy and computational cost. Recently, we have developed a novel scheme called NAO2GTO to achieve linear scaling (Order-N) calculations for hybrid density-functionalsShang et al. (2011). In our scheme, the most time-consuming step is the calculation of the electron repulsion integrals (ERIs) part. So how to create an even distribution of these ERIs in parallel implementation is an issue of particular importance. Here, we present two static scalable distributed algorithms for the ERIs computation. Firstly, the ERIs are distributed over ERIs shell pairs. Secondly, the ERIs is distributed over ERIs shell quartets. In both algorithms, the calculation of ERIs is independent of each other, so the communication time is minimized. We show our speedup results to demonstrate the performance of these static parallel distributed algorithms in the Hefei Order-N packages for ab initio simulations (HONPAS)Qin et al. (2014). Copyright © 2020, The Authors. All rights reserved.

关键词： Calculations

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On mean absolute error for deep neural network based vector-to-vector regression

arXiv

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arXiv 2020年

作者： Qi, Jun Du, Jun Siniscalchi, Sabato Marco Ma, Xiaoli Lee, Chin-Hui School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei230027 China Faculty of Architecture and Engineering University of Enna "Kore" Enna94100 Italy Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector-to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

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The Influence of Using Responsive Façade as a Tool for Improving the Built Environment: Case study: Attaba – Opera square

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IOP Conference Series: Earth and Environmental science 2022年第1期992卷

作者： M F Eltanboly M M Afify Architecture Department Teaching Assistant Cairo Higher Institute for Engineering Computer Science and Management No.2 Banafseg Center2 first Settlement Cairo Postcode 11477 Egypt and PhD. Student Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza Architecture department Professor in Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza

The path we are currently following to improve the physics of construction due to the destruction of our environment through the negative impact of the built environment around us. Current efforts to decrease carbon and energy are noted by modifying and improving modern 'layered' envelopes and improving a misunderstanding of thermal comfort that has become much less effective than expected. Understanding construction physics is an important factor in improving the quality of life indoor and outdoor, relying primarily on theoretical and computational aspects, then applying the practical part to reduce excessive use of carbon and energy and hinder the elaboration of a sustainable and built environment. This paper shows how the problem stems from neglecting to think about the first principles and basic construction physics. Similarly, we show how combining good construction physics with reassessing old methodologies to construction and structure use provides some powerful and effective tools to address the climate emergency. The use of construction physics methods supports the search for new solutions to express the most important factors affecting thermal conduction and minimum surface temperatures. However, buildings have integrated concepts in which advanced systems work together to optimize energy, comfort, and health performance. Convergence between various engineering fields and the overlapping field of construction techniques and services has great potential in the future to achieve the next step in energy saving. As a result, the research highlights new methods that make our buildings interact with the external environment using climate data, responsive envelope design, simulation software, thereby achieving architectural response to surrounding variables, improving building physics to reduce environmental pollution, thermal comfort, and achieving the highest energy efficiency and performance in buildings. This is illustrated by the presentation of a case study

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Analyzing upper bounds on mean absolute errors for deep neural network based vector-to-vector regression

arXiv

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arXiv 2020年

作者： Qi, Jun Du, Jun Marco Siniscalchi, Sabato Ma, Xiaoli Lee, Chin-Hui School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei230027 China Faculty of Architecture and Engineering University of Enna Kore Enna94100 Italy Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image denoising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the "over-parametrization" technique. Copyright © 2020, The Authors. All rights reserved.

关键词： Convex optimization

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The dynamic parallel distribution algorithm for hybrid density-functional calculations in HONPAS package

arXiv

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arXiv 2020年

作者： Shang, Honghui Xu, Lei Wu, Baodong Qin, Xinming Zhang, Yunquan Yang, Jinlong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory for Physical Sciences at Microscale Department of Chemical Physics Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China

This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange (HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electron repulsion integrals (ERIs) calculation is perfectly load-balanced with 2-level master-worker dynamic parallel scheme, the density matrix and the HFX matrix are both stored in the sparse format, the network communication time is minimized via only communicating the index of the batched ERIs and the final sparse matrix form of the HFX matrix. The performance of this dynamic scalable distributed algorithm has been demonstrated by several examples of large scale hybrid density-functional calculations on Tianhe-2 supercomputers, including both molecular and solid states systems with multiple dimensions, and illustrates good scalability. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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Latency Measurements for an Emulation Platform on Autonomous Driving Platform NVIDIA Drive PX2

Latency Measurements for an Emulation Platform on Autonomous...

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ARCS Workshop 2018;31th International Conference on architecture of Computing Systems

作者： Christian Widerspick Wolfgang Bauer Dietmar Fey Department of Computer Science Chair of Computer Architecture Friedrich-Alexander-University Erlangen-Nuremberg

The use of GPUs in the automotive sector is a possibility to provide a lot of computing power for complex algorithms required for autonomous driving. In order to be able to make statements about the real-time capability of algorithms, it is necessary to be able to make precise statements about the runtime behavior. This work deals with the execution time of instructions on the NVIDIA Drive PX2 evaluation platform designed for an automotive environment. It is to be shown what influence block and warp variables have on execution times and whether these values can be used to estimate the total runtime of algorithms. This provides a deeper insight into the functionality of the modern Pascal architecture.

关键词： execution time PANX2 gene real-time capability algorithms driving platform Statement computational power Automobile driving instruction simulating DRIVES Platform Runtime

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H2-MARL: Multi-agent reinforcement learning for Pareto optimality in hospital capacity strain and human mobility during epidemic

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Expert Systems with Applications 2025年 291卷

作者： Xueting Luo Hao Deng Jihong Yang Yao Shen Huanhuan Guo Zhiyuan Sun Mingqing Liu Jiming Wei Shengjie Zhao School of Computer Science and Technology Tongji University Shanghai 201804 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai 201804 China China Land Surveying and Planning Institute Beijing 100035 China College of Architecture and Urban Planning Tongji University Shanghai 200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing 401121 China College of Electronic and Information Engineering Tongji University Shanghai 201804 China Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou 510290 China

Effectively balancing the losses from mobility restrictions and hospital capacity strain has drawn significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics; however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different population scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can simultaneously minimize hospital capacity strain and human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications.

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A Pipelining Loop Optimization Method for Dataflow architecture

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Journal of computer science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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Discrete-Time Modeling of NFV Accelerators that Exploit Batched Processing

Discrete-Time Modeling of NFV Accelerators that Exploit Batc...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Stanislav Lange Leonardo Linguaglossa Stefan Geissler Dario Rossi Thomas Zinner University of Würzburg Institute of Computer Science Chair of Communication Networks Wüurzburg Germany Telecom ParisTech Paris France TU Berlin Berlin Germany

Network Functions Virtualization (NFV) is among the latest network revolutions, bringing flexibility and avoiding network ossification. At the same time, all-software NFV implementations on commodity hardware raise performance issues with respect to ASIC solutions. To address these issues, numerous software acceleration frameworks for packet processing have appeared in the last few years. Common among these frameworks is the use of batching techniques. In this context, packets are processed in groups as opposed to individually, which is required at high-speed to minimize the framework overhead, reduce interrupt pressure, and leverage instruction-level cache hits. Whereas several system implementations have been proposed and experimentally benchmarked, the scientific community has so far only to a limited extent attempted to model the system dynamics of modern NFV routers exploiting batching acceleration. In this paper, we fill this gap by proposing a simple generic model for such batching-based mechanisms, which allows a very detailed prediction of highly relevant performance indicators. These include the distribution of the processed batch size as well as queue size, which can be used to identify loss-less operational regimes or quantify the packet loss probability in high-load scenarios. We contrast the model prediction with experimental results gathered in a high-speed testbed including an NFV router, showing that the model not only correctly captures system performance under simple conditions, but also in more realistic scenarios in which traffic is processed by a mixture of functions.

关键词： Software Computational modeling Predictive models Packet loss System performance Hardware

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Big mobility data reveals hyperlocal air pollution exposure disparities in the Bronx, New York

Nature Cities

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Nature Cities 2024年第8期1卷 512-521页

作者： Iacopo Testi An Wang Sanjana Paul Simone Mora Fábio Duarte Paolo Santi Carlo Ratti Erica Walker Marguerite Nyhan Senseable City Lab Department of Urban Studies and Planning Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Istituto di Informatica e Telematica Consiglio Nazionale delle Ricerche Pisa Italy ABC Department Politecnico di Milano Milan Italy School of Public Health Brown University Providence RI USA School of Engineering and Architecture University College Cork Cork Ireland

Air pollution disproportionately affects socially disadvantaged populations. Our study bridges the existing gap in quantifying mobility-based exposure and its associated disparity issues. We combined the granular mobility of over 500,000 unique anonymized users daily and hyperlocal air pollution data in 100 × 100-m grid cells to quantify disparities in particulate matter exposure in a racially diverse and dense urban area of New York City. Our approach advances the study of exposure and its disparity from individualized exposure tracking to a population-representative scale. We observed apparently different spatial patterns between personal exposure and exposure disparities, noting that people from Hispanic-majority and low-income neighborhoods were those most severely and disproportionately exposed to fine particulate matter (PM2.5) pollution. We reveal that race and ethnicity are much stronger indicators of exposure disparity than income. Our study further demonstrates that within-group variation contributes a major portion to exposure disparities, suggesting more granular mitigation plans are needed to target high-exposure individuals from socially disadvantaged groups in addition to generic air quality improvement.

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