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Evaluation of parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology 26

Evaluation of Parallel Tempering to Accelerate Bayesian Para...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Gupta, Sanjana Hainsworth, Liam Hogg, Justin S. Lee, Robin E. C. Faeder, James R. Univ Pittsburgh Sch Med Dept Computat & Syst Biol Pittsburgh PA 15260 USA

ISBN: (纸本)9781538649756

Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. the temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://***/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.

关键词： Bayesian parameter estimation Systems biology parallel tempering Rule-based modeling

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Performance Evaluation of the Metadata-driven MASi Research Data Management Repository Service 26

Performance Evaluation of the Metadata-driven MASi Research ...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Grunzke, Richard Mueller-Pfefferkorn, Ralph Nagel, Wolfgang E. Hartmann, Volker Jejkal, thomas Kollai, Helen Herold, Hendrik Meinel, Gotthard Dressler, Christiane Dolhoff, Julia Schrade, Torsten Stanek, Julia Hoffmann, Alexander Herres-Pawlis, Sonja Tech Univ Dresden Ctr Informat Serv & High Performance Comp Dresden Germany Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Inst Ecol & Reg Dev Monitoring Settlement & Open Space Dev Dresden Germany Akad Wissensch & Literatur Mainz Digitale Akad Mainz Germany Rheinisch Westfal Tech Hsch Aachen Inst Anorgan Chem Aachen Germany

ISBN: (纸本)9781538649756

Research data is increasingly important in order to gain insights from scientific data. To optimally foster this, the management of research data is required to be usable, customizable and fast. We enable this by building up the MASi research data management repository service, based on the KIT DM framework. the aim is on utilizing a single repository instance to serve multiple arbitrary community use cases. Due to their diverse data characteristics the performance of the MASi service has to be fitting across the different cases. We evaluate the performance along three initial heterogeneous use cases. Various aspects are investigated;First, the object insertion and query performance of the database along the object fill level. Second and third, the ingest and download performance of digital objects using real-life data sets. Highly favorable performance characteristics are shown.

关键词： Research Data Management Repository Performance

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Evaluating and Optimizing OpenCL Base64 Data Unpacking Kernel with FPGA 26

Evaluating and Optimizing OpenCL Base64 Data Unpacking Kerne...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Jin, Zheming Johnson, Iris Finkel, Hal Argonne Natl Lab Leadership Comp Facil Argonne IL 60439 USA Northern Illinois Univ Dept Compute Sci De Kalb IL 60115 USA

ISBN: (纸本)9781538649756

Development of applications using OpenCL targeting FPGAs is an emerging approach on heterogeneous computing systems. this paper uses the data unpacking algorithm in Base64 encoding as a case study to present programming and optimization techniques, and experimental results of the OpenCL-based implementations on an FPGA. We explain the algorithm and evaluate the performance of the kernel implementations with Intel's FPGA OpenCL SDK. the experimental results show kernel vectorization and duplication are two optimization techniques that can improve the kernel performance. the performance of kernel duplication is also closely related to the local work size. Our experiment shows 16-lane vectorization increases the bandwidth by a factor of 2 to 10 for large input data sizes. Moreover, the performance of kernel duplication using 16 compute units is 40% to 1.5% less than that of kernel vectorization depending on the input size. Tuning the local work size can improve the kernel performance by a factor of 3 to 23. For this kernel, using local memory is not an effective technique to improve the kernel performance because input data is not reused. A combination of vectorization and duplication achieves the highest performance of 12.3 GiB/s. Compared to an Intel Xeon E5 CPU and an Nvidia Tesla K80 GPU, the performance of the kernel on the Arria 10 FPGA is 6.7X faster than the CPU and 3X slower than the GPU. the performance per watt on the FPGA is 20.5X higher than the CPU and 1.19X lower than the GPU.

关键词： FPGA OpenCL Base64 Encoding

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Low Precision Deep Learning Training on Mobile Heterogeneous Platform

Low Precision Deep Learning Training on Mobile Heterogeneous...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Valery, Olivier Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781538649756

Recent advances in System-on-Chip architectures have made the use of deep learning suitable for a number of applications on mobile devices. Unfortunately, due to the computational cost of neural network training, it is often limited to inference task, e.g., prediction, on mobile devices. In this paper, we propose a deep learning framework that enables both deep learning training and inference tasks on mobile devices. While being able to accommodate with the heterogeneity of computing devices technology on mobile devices, it also uses OpenCL to efficiently leverage modern SoC capabilities, e.g., multi-core CPU, integrated GPU and shared memory architecture, and accelerate deep learning computation. In addition, our system encodes the arithmetic operations of deep networks down to 8-bit fixed-point on mobile devices. As a proof of concept, we trained three well-known neural networks on mobile devices and exhibited a significant performance gain, energy consumption reduction, and memory saving.

关键词： OpenCL GPGPU mobile computing heterogeneous system System-on-Chip deep learning transfer learning

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SharP Data Constructs: Data Constructs to Enable Data-centric Computing 26

SharP Data Constructs: Data Constructs to Enable Data-centri...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Aderholdt, Ferrol Venkata, Manjunath Gorentla Parchman, Zachary W. Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Tennessee Technol Univ Comp Sci Dept Cookeville TN 38501 USA

ISBN: (纸本)9781538649756

Extreme-scale applications (i.e., Big-Compute) are becoming increasingly data-intensive, i.e., producing and consuming increasingly large amounts of data. the HPC systems traditionally used for these applications are now used for Big-Data applications such as data analytics, social network analysis, machine learning, and genomics. As a consequence of these trends, the system architecture should be flexible and data-centric. this can already be witnessed in the pre-exascale systems with TBs of on-node hierarchical and heterogeneous memories, PBs of system memory, low-latency, high-throughput networks, and many threaded cores. As such, the pre-exascale systems suit the needs of both Big-Compute and Big-Data applications. though the system architecture is flexible enough to support both Big-Compute and Big-Data, we argue there is a software gap. Particularly, we need data-centric abstractions to leverage the full potential of the system, i.e., there is a need for native support for data resilience, the ability to express data locality and affinity, mechanisms to reduce data movement, the ability to share data, and abstractions to express User's data usage and data access patterns. In this paper, we (i) show the need for taking a holistic approach towards data-centric abstractions, (ii) show how these approaches were realized in the SHARed data-structure centric Programming abstraction (SharP) library, a data-structure centric programming abstraction, and (iii) apply these approaches to a variety of applications that demonstrate its usefulness. Particularly, we apply these approaches to QMCPack and the Graph500 benchmark and demonstrate the advantages of this approach on extreme-scale systems.

关键词： Libraries Data structures Resilience Memory management Benchmark testing distributed databases Random access memory

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High-resolution numerical relativity simulations of spinning binary neutron star mergers 26

High-resolution numerical relativity simulations of spinning...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Dietrich, Tim Bernuzzi, Sebastiano Bruegmann, Bernd Tichy, Wolfgang Albert Einstein Inst Max Planck Inst Gravitat Phys Muhlenberg 1 D-14476 Potsdam Germany Nikhef Sci Pk 105 NL-1098 XG Amsterdam Netherlands Univ Parma Dipartimento Sci Matemat Fis & Informat I-43124 Parma Italy Ist Nazl Fis Nucl Sez Milano Bicocca Grp Collegato Parma I-43124 Parma Italy Univ Jena Theoret Phys Inst D-07743 Jena Germany Florida Atlantic Univ Dept Phys Boca Raton FL 33431 USA

ISBN: (纸本)9781538649756

the recent detection of gravitational waves and electromagnetic counterparts emitted during and after the collision of two neutron stars marks a breakthrough in the field of multi-messenger astronomy. Numerical relativity simulations are the only tool to describe the binary's merger dynamics in the regime when speeds are largest and gravity is strongest. In this work we report state-of-the-art binary neutron star simulations for irrotational (non-spinning) and spinning configurations. the main use of these simulations is to model the gravitational-wave signal. Key numerical requirements are the understanding of the convergence properties of the numerical data and a detailed error budget. the simulations have been performed on different HPC clusters, they use multiple grid resolutions, and are based on eccentricity reduced quasi-circular initial data. We obtain convergent waveforms with phase errors of 0.5 - 1.5rad accumulated over similar to 12 orbits to merger. the waveforms have been used for the construction of a phenomenological waveform model which has been applied for the analysis of the recent binary neutron star detection. Additionally, we show that the data can also be used to test other state-of-the-art semi-analytical waveform models.

关键词： Numerical models Spinning Mathematical model Neutrons Computational modeling Convergence Earth Observing System

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NIOECM: A network I/O event control mechanism to provide fairness of network performance among vms with same resource configuration in virtualized environment 12th

NIOECM: A network I/O event control mechanism to provide fai...

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12th International conference on Internet and distributed Computing Systems, IDCS 2019

作者： Lee, Jaehak Kang, Jihun Yu, Heonchang Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783030349134

In the virtualization environment, a hypervisor scheduler determines the degree of shared resource occupancy of the virtual machine (VM) according to the degree of CPU processing and it provides a fair CPU processing on virtual machines (VMs). But VMs are experiencing unfair network performance due to the hypervisor scheduler’s policy occupying resources based on CPU processing time. In this paper, we present NIOECM which is a network IO event control technique that controls the network-intensive VM’s network IO event to guarantee a fairness network performance of VMs which have the same resource configuration. the NIOECM performs a network delay processing on the network-intensive VMs which have a high network I/O event set. As a result, the network-intensive VMs which have a low network I/O event set will have more chance to occupy the network resource. In the result of experiments, our approach provides more fairness of network performance and does not give a performance interference on VM which is performing another task. © Springer Nature Switzerland AG 2019.

关键词： Virtual machine

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19th International conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and Systems, NEW2AN 2019, and 12th conference on Internet of things and Smart Spaces, ruSMART 2019

19th International Conference on Next Generation Teletraffic...

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19th International conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and Systems, NEW2AN 2019, and 12th conference on Internet of things and Smart Spaces, ruSMART 2019

ISBN: (纸本)9783030308582

the proceedings contain 66 papers. the special focus in this conference is on Next Generation Teletraffic and Wired/Wireless Advanced networks and Systems. the topics include: FaceWallGraph: Using Machine Learning for Profiling User Behaviour from Facebook Wall;multi-agent Approach to Computational Resource Allocation in Edge Computing;the Use of Context-Dependent Modelling for the Construction of an Anti-fraud System in Transport;an Approach to the Analysis of the Vehicle Movement on the Organization Territory;building Blocks of an Innovative Approach to Education in the Field of Cyber Operations in Smart Environment;Channel Switching Protocols Hinder the Transition to IP World: the Pentagon Story;network Anomaly Detection in Wireless Sensor networks: A Review;polarization Direction Finding Method of Interfering Radio Emission Sources;Coexistence Management Approach for Densification of Randomly Deployed Low Power Nodes in TVWS Spectrum;investigation of the IoT Device Lifetime with Secure Data Transmission;Toward an Ultra-low Latency and Energy Efficient LoRaWAN;Novel AI-based Scheme for Traffic Detection and Recognition in 5G based networks;a Connectivity Game with Incomplete Information on Jammer’s Location;ray-based Modeling of Unlicensed-Band mmWave Propagation Inside a City Bus;maximizing Achievable Data Rate in Unlicensed mmWave networks with Mobile Clients;runtime Minimization of the threshold distributed Computation Protocol in the Case of Participants Failures;preemptive Priority Queuing System with Randomized Push-Out Mechanism and Negative Customers;Development of Analytical Framework for Evaluation of LTE-LAA Probabilistic Metrics;Beamforming Signal processing Performance Analysis for Massive MIMO Systems;Autonomous UAV Landing on a Moving Vessel: Localization Challenges and Implementation Framework.

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12th International conference on Intelligent Robotics and Applications, ICIRA 2019

12th International Conference on Intelligent Robotics and Ap...

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12th International conference on Intelligent Robotics and Applications, ICIRA 2019

ISBN: (纸本)9783030275402

the proceedings contain 378 papers. the special focus in this conference is on Intelligent Robotics and Applications. the topics include: Intelligent Robot Arm: Vision-based Dynamic Measurement System for Industrial Applications;research on Autonomous Face Recognition System for Spatial Human-Robotic Interaction based on Deep Learning;KPCA-based Visual Fault Diagnosis for Nonlinear Industrial Process;data Denosing processing of the Operating State of the Robotic Arm of Coal Sampling Robot;a Study on Step-by-Step Calibration of Robot based on Multi-vision Measurement;characteristic Frequency Input Neural network for Inertia Identification of Tumbling Space Target;An FFT-based Method for Analysis, Modeling and Identification of Kinematic Error in Harmonic Drives;real-Time Human-Posture Recognition for Human-Drone Interaction Using Monocular Vision;HSVM-based Human Activity Recognition Using Smartphones;stability Analysis and Fixed Radius Turning Planning of Hexapod Robot;Human-AGV Interaction: Real-Time Gesture Detection Using Deep Learning;Coverage Path Planning for Complex Structures Inspection Using Unmanned Aerial Vehicle (UAV);development of Four Rotor Fire Extinguishing System for Synchronized Monitoring of Air and Ground for Fire Fighting;A Small Envelope Gait Control Algorithm based on FTL Method for Snake-Like Pipe Robot;trajectory Tracking Control of Wheeled Mobile Robots Using Backstepping;the Design of Inspection Robot Navigation Systems based on distributed Vision;mobile Robot Autonomous Navigation and Dynamic Environmental Adaptation in Large-Scale Outdoor Scenes;movement Analysis of Rotating-Finger Cable Inspection Robot;autonomous Indoor Mobile Robot Exploration based on Wavefront Algorithm;multi-robot Path Planning for Complete Coverage with Genetic Algorithms;the Mechanical Design and Torque Control for the Ankle Exoskeleton During Human Walking.

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A High-Level C++ Approach to Manage Local Errors, Asynchrony and Faults in an MPI Application 26

A High-Level C++ Approach to Manage Local Errors, Asynchrony...

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26th euromicro International conference on parallel, distributed, and network-based processing, PDP 2018

作者： Engwer, Christian Altenbernd, Mirco Dreier, Nils-Arne Goddeke, Dominik Applied Mathematics University of Munster Orleansring 10 Munster48149 Germany IANS University of Stuttgart Allmandring 5b Stuttgart70569 Germany

ISBN: (纸本)9781538649756

C++ advocates exceptions as the preferred way to handle unexpected behaviour of an implementation in the code. this does not integrate well with the error handling of MPI, which more or less always results in program termination in case of MPI failures. In particular, a local C++ exception can currently lead to a deadlock due to unfinished communication requests on remote hosts. At the same time, future MPI implementations are expected to include an API to continue computations even after a hard fault (node loss), i.e. the worst possible unexpected behaviour. In this paper we present an approach that adds extended exception propagation support to C++ MPI programs. Our technique allows to propagate local exceptions to remote hosts to avoid deadlocks, and to map MPI failures on remote hosts to local exceptions. A use case of particular interest are asynchronous 'local failure local recovery' resilience approaches. Our prototype implementation uses MPI-3.0 features only. In addition we present a dedicated implementation, which integrates seamlessly with MPI-ULFM, i.e. the most prominent proposal for extending MPI towards fault tolerance. Our implementation is available at https://***/christi/test-mpi-exceptions. © 2018 IEEE.

关键词： Fault tolerance

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