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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10394 条记录，以下是4281-4290 订阅

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Towards a general and efficient linked-list hash table on GPUs 21

Towards a general and efficient linked-list hash table on GP...

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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： Gao, Lan Xu, Yunlong Xu, Chongyang Wang, Rui Yang, Hailong Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China Beijing China

ISBN: (纸本)9781728120584

Hash table is a fundamental indexing data structure, and extensively used in many applications. Recently, several hash table implementations have been proposed on GPUs. However, they have two pitfalls. Firstly, their synchronization approaches can only ensure the atomicity among individual operations. This makes them can hardly be used in some real-world applications (e.g., online transaction processing, OLTP), which require groups of operations to be executed atomically. Secondly, they use either a GPU thread or a warp of GPU threads to process an operation at a time (named thread-and warp-level execution respectively). The former one incurs more branch divergences, while the latter one requires all threads within a warp to be active. To this end, we (1) propose a readers-writer lock based, bucket-level synchronization to realize the atomicity among both individual and groups of operations, and (2) combine thread-and warp-level executions to optimize system performance. Moreover, we optimize the data structure of linked-list with respect to GPU architectures. Putting them together, we realize a general and efficient linked-list hash table on GPUs, named GEL-HASH. Compared with existing GPU hash table implementations, GEL-HASH can easily support more real-world applications. Moreover, we compare GEL-HASH with the state-of-the-art hash table implementation on GPUs (slab hash) using applications that are well supported by the existing implementations. The results show that, for small data scales (numbers of key-value pairs stored in a hash table) GEL-HASH outperforms slab hash by up to 3.05X, while for large data scales GEL-HASH exhibits less than 20% performance slowdown. © 2019 IEEE.

关键词： Data structures

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Multiplex Symbolic Execution: Exploring Multiple Paths by Solving Once

Multiplex Symbolic Execution: Exploring Multiple Paths by So...

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IEEE International conference on Automated Software Engineering (ASE)

作者： Yufeng Zhang Zhenbang Chen Ziqi Shuai Tianqi Zhang Kenli Li Ji Wang College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering National Supercomputing Center in Changsha Hunan University Changsha China College of Computer State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Path explosion and constraint solving are two challenges to symbolic execution's scalability. Symbolic execution explores the program's path space with a searching strategy and invokes the underlying constraint solver in a black-box manner to check the feasibility of a path. Inside the constraint solver, another searching procedure is employed to prove or disprove the feasibility. Hence, there exists the problem of double searchings in symbolic execution. In this paper, we propose to unify the double searching procedures to improve the scalability of symbolic execution. We propose Multiplex Symbolic Execution (MuSE) that utilizes the intermediate assignments during the constraint solving procedure to generate new program inputs. MuSE maps the constraint solving procedure to the path exploration in symbolic execution and explores multiple paths in one time of solving. We have implemented MuSE on two symbolic execution tools (based on KLEE and JPF) and three commonly used constraint solving algorithms. The results of the extensive experiments on real-world benchmarks indicate that MuSE has orders of magnitude speedup to achieve the same coverage.

关键词： Multiplexing Scalability Tools Search problems Explosions Space exploration Software engineering

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A programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors

A Programmatic and Semantic Approach to Explaining and Debug...

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conference on computer Vision and Pattern Recognition (CVPR)

作者： Edward Kim Divya Gopinath Corina Păsăreanu Sanjit A. Seshia University of California Berkeley NASA Ames Research Center NASA Moffett Field CA USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a programmatic and semantic approach to explaining, understanding, and debugging the correct and incorrect behaviors of a neural network based perception system. Our approach is semantic in that it employs a high-level representation of the distribution of environment scenarios that the detector is intended to work on. It is programmatic in that the representation is a program in a domain-specific probabilistic programming language using which synthetic data can be generated to train and test the neural network. We present a framework that assesses the performance of the neural network to identify correct and incorrect detections, extracts rules from those results that semantically characterizes the correct and incorrect scenarios, and then specializes the probabilistic program with those rules in order to more precisely characterize the scenarios in which the neural network operates correctly or not, without human intervention. We demonstrate our results using the Scenic probabilistic programming language and a neural network-based object detector. Our experiments show that it is possible to automatically generate compact rules that significantly increase the correct detection rate (or conversely the incorrect detection rate) of the network and can thus help with debugging and understanding its behavior.

关键词： Semantics Automobiles Feature extraction Detectors Probabilistic logic Debugging computer vision

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DETAILED EXPERIMENTAL MEASUREMENT AND RANS SIMULATION OF A LOW PRESSURE TURBINE WITH high LIFT BLADING

DETAILED EXPERIMENTAL MEASUREMENT AND RANS SIMULATION OF A L...

引用

ASME Turbo Expo: Turbomachinery Technical conference and Exposition

作者： Perez, Ethan Schmitz, John T. Jaffa, Nicholas A. Jemcov, Aleksandar Cameron, Joshua D. Morris, Scott C. Univ Notre Dame Notre Dame Turbomachinery Lab South Bend IN 46601 USA Williams Int Pontiac MI 48341 USA Penn State Univ Appl Res Lab University Pk PA 16802 USA

ISBN: (纸本)9780791858561

The aerodynamic characteristics of high lift airfoil designs is of interest for improved performance and reduced blade count in Low Pressure Turbine (LPT) design. The present paper presents both experimental measurements as well as numerical simulation results from a single-stage LPT. The airfoils were designed for an embedded stage with a total pressure expansion ratio of 1.75 and a rotor Zweifel coefficient of 1.35. The measurement program was highly unique in that detailed measurements were obtained using a variety of different probe types, including time resolved total pressure and hot wires. Agreement between various measurement types was generally good, but differences beyond typically stated uncertainty bounds were noted. The computations were done using RANS and a mixing model via commercially available software. The numerical results were evaluated to determine the efficacy of this type of model for prediction and design of high lift airfoils. The computations agreed very well with the experimental results in the midspan region, but losses were over predicted in the lower 40% span near the hub. A basic description and understanding of the flow physics in the LPT stage are presented based on the relative agreement between the experiments and computations.

关键词： Pressure Simulation Turbines Reynolds-averaged Navier–Stokes equations Computation Airfoils Design Rotors Blades Flow (Dynamics) computer simulation Wire Physics computer software Probes Uncertainty

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An empirical study of chunk size variation in DASH system 21

An empirical study of chunk size variation in DASH system

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作者： Wang, Bo Ren, Fengyuan Beijing National Research Center Information Science and Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781728120584

Most video players use Dynamic Adaptive Streaming over HTTP (DASH) to ensure high Quality of Experience (QoE) in varying network conditions. To improve the encoding efficiency, streaming providers use Variable BitRate (VBR) encoding for video contents, where the video chunks with the same nominal bitrate (video quality) have different instantaneous bitrates and chunk sizes. However, the chunk size variation is assumed to be trivial compared with the network bandwidth variation, and existing bitrate adaptation algorithms simply use the nominal information to make bitrate decisions. In this paper, we present an empirical study of chunk size variation in DASH to explore whether the algorithm can ignore this stochastic factor. We measure the characteristics of chunk size variation based on a rich set of videos and analyze its impact on QoE by trace-driven experiments. Analysis results show that all the buffer-aware algorithms suffer from degraded QoE if they do not consider the chunk size variation. To eliminate the negative impact, we suggest that the bitrate adaptation algorithm should rely on exact chunk size to adjust bitrate instead of the nominal bitrate. To confirm our insights and suggestion, a preliminary solution is proposed and validated based on a real video player and realistic bandwidth traces. © 2019 IEEE.

关键词： Bandwidth

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DeapSECURE: Empowering Students for Data- and Compute- Intensive Research in Cybersecurity through Training 19

DeapSECURE: Empowering Students for Data- and Compute- Inten...

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conference on Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC)

作者： Purwanto, Wirawan Wu, Hongyi Sosonkina, Masha Arcaute, Karina Old Dominion Univ Informat Technol Serv Norfolk VA 23529 USA Old Dominion Univ Elect & Comp Engn Norfolk VA USA Old Dominion Univ Modeling Simulat & Visualizat Engn Norfolk VA USA Old Dominion Univ STEM Educ & Profess Studies Norfolk VA USA

ISBN: (纸本)9781450372275

As the volume and sophistication of cyber-attacks grow, cybersecurity researchers, engineers and practitioners rely on advanced cyberinfrastructure (CI) techniques like big data and machine learning, as well as advanced CI platforms, e.g., cloud and high-performance computing (HPC) to assess cyber risks, identify and mitigate threats, and achieve defense in depth. There is a training gap where current cybersecurity curricula at many universities do not introduce advanced CI techniques to future cybersecurity workforce. At Old Dominion University (ODU), we are bridging this gap through an innovative training program named DeapSECURE ( ata-Enabled Advanced Training program for Cyber Security Research and Education). We developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Each workshop includes a lecture providing the motivation and context for a CI technique, which is then examined during a hands-on session. The modules are delivered through (1) monthly workshops for ODU students, and (2) summer institutes for students from other universities and Research Experiences for Undergraduates participants. Future plan for the training program includes an online continuous learning community as an extension to the workshops, and all learning materials available as open educational resources, which will facilitate widespread adoption, adaptations, and contributions. The project leverages existing partnerships to ensure broad participation and adoption of advanced CI techniques in the cybersecurity community. We employ a rigorous evaluation plan rooted in diverse metrics of success to improve the curriculum and demonstrate its effectiveness.

关键词： Training Cyberinfrastructure high-performance computing Big data Machine learning Parallel computing Cybersecurity Workforce development

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Behavioral Biometrics for Human Identity Corroboration based on Gesture-Signature with Deep Learning

Behavioral Biometrics for Human Identity Corroboration based...

引用

International conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Edward Opoku-Mensah Yaa Serwaa Bandoh Jianping Li Judith Browne Ayekai Bernard Cobbinah Mawuli Christian Service University College Ashanti Region Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781665405058

ISBN: (纸本)9781665405065

Identity corroboration has gained a substantial deal of attention with the high usage of smart devices and dedicated systems accommodating sensitive data and applications magnitudes. In this paper, we develop a behavioral biometric authentication system based on deep learning. Specifically, an android application is developed as a dedicated tool for capturing the touch behavioral biometrics information, the electronic signature information alongside their corresponding accelerometer and gyroscope sensor readings. Finally, the generated sensor readings are fed into a multi-input convolutional neural network architecture for classification. Extensive experimental results show that our proposed approach uniquely identifies users with a classification performance of 93.46% as compared to other baseline approaches, where only a single sensor reading is considered.

关键词： Biometrics (access control) Accelerometers Authentication Gyroscopes Convolutional neural networks Deep learning Neural networks

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Optimization of a Cardiac Electromechanics Simulation program for Parallel Implementation

Optimization of a Cardiac Electromechanics Simulation Progra...

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2019 International Multi-conference on Engineering, computer and Information Sciences, SIBIRCON 2019

作者： Pravdin, Sergei F. Panfilov, Alexander V. Mathematical Modelling in Cardiology Sector Krasovskii Institute of Mathematics and Mechanics Ekaterinburg Russia Ural Federal University Department of High-Performance Computer Technologies Ekaterinburg Russia Faculty of Science Ghent University Ghent Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia

ISBN: (纸本)9781728144016

The modified mass-spring system for the soft tissue mechanics proposed by O. Jarrousse has a computationally intensive loop that updates the states of all mesh elements such as triangles in 2D or tetrahedra in 3D. Each iteration of the loop computes forces acting at the vertices of an element, leading to a deformation of the mesh. Although the result does not depend on the order of the update, its direct parallelization causes a data race. In this paper, we propose a parallelization approach that divides the set of the mesh elements into subsets so that the elements of each subset can be processed in a parallel way, while the subsets themselves must be processed in a serial way. We show that this problem can be reduced to dividing the set of graph vertices into independent sets, or the well-known graph colouring problem. We use the program to simulate the low-voltage cardioversion in a square model of the myocardium and show how the results depend on the stimulation period and mechano-electrical feedback intensity. © 2019 IEEE.

关键词： Heart

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Realising Edge Analytics for Early Prediction of Readmission: A Case Study

Realising Edge Analytics for Early Prediction of Readmission...

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IEEE International conference on Cloud Engineering (IC2E)

作者： Yucen Nan Wei Li Feng Lu Flavia C. Delicato Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia School of Computer Science Huazhong University of Science and Technology China Department of Computer Science Fluminense Federal University Brazil

ISBN: (数字)9781728110998

ISBN: (纸本)9781728111001

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

关键词： Medical diagnostic imaging Interpolation Hospitals Sensors Surveillance

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Cross-Domain Data Access System for Distributed Sites in HEP 1

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1st International conference on Big Scientific Data Management (BigSDM)

作者： Xu, Qi Cheng, Zhenjing Cheng, Yaodong Chen, Gang Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst High Energy Phys Beijing Peoples R China

ISBN: (数字)9783030280611

ISBN: (纸本)9783030280611;9783030280604

A large amount of data is produced by large scale scientific facilities in high energy physics (HEP) field. And distributed computing technologies has been widely used to process these data. In traditional computing model such as grid computing, computing job is usually scheduled to the sites where the input data was pre-staged in. This model will lead to some problems including low CPU utilization, inflexibility, and difficulty in highly dynamic cloud environment. The paper proposed a cross-domain data access system (CDAS), which presents one same file system view at local and the remote sites, supporting directly data access on demand. Then the computing job can run everywhere no need to know where data is located. For the moment the system has been implemented including these functionalities such as native access for remote data, quick response, data transmission and management on demand based on HTTP, data block hash and store, uniform file view and so on. The test results showed the performance was much better than traditional file system on high-latency WAN.

关键词： Experimental data in HEP Cross-domain access Remote file system Cache high performance

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