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Performance evaluation of non-water absorption coefficient partitioning algorithms in optically complex coastal waters of Kochi and Goa, India

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OCEANOLOGIA 2021年第4期63卷 420-437页

作者： Kolluru, Srinivas Gedam, Shirishkumar S. Inamdar, Arun B. Indian Inst Technol Ctr Studies Resources Engn Bombay 400076 Maharashtra India

Absorption coefficient partitioning algorithms (APAs) were developed to partition Absorption;the total absorption coefficient (a(lambda)) or total non-water absorption coefficient (a(nw)(lambda)) into the absorption subcomponents, i.e., absorption due to phytoplankton a(ph)(lambda), colored dissolved organic matter (CDOM) a(g)(lambda) and non-algal particulate matter a(d)(lambda), lambda. is the wavelength. Absorption coefficients of CDOM and non-algal particulate matter are generally combined due to a similarity in exhibited spectral shape and represented as colored detrital matter (CDM) absorption coefficient, a(dg)(lambda). This study focuses on the applicability of five APAs Schofield's, Lin's, Zhang's, Stacked Constraints Model (SCM) and Generalized Stacked Constraints Model (GSCM), in deriving the absorption subcomponents from a(nw)(lambda) in optically complex coastal waters of Kochi and Goa, India. The average spectral Mean Absolute Percentage Errors (MAPE) obtained for all models in the retrieval of a(ph)(lambda), a(d)(lambda), a(g)(lambda) and a(dg)(lambda) lie in the ranges of 26-44%, 37-45%, 34-65% and 42-56%. Slopes of a(dg)(lambda), a(g)(lambda) and a(d)(lambda) as indicated by S-dg, S-g and S-d are derivable from GSCM, Schofield and Lin's models only. GSCM model exhibited good retrieval capability of S-d with MAPE values of 22% and a correlation coefficient of 0.74. In retrieval of S-g parameter, none of the models demonstrated satisfactory performance. Overall, the GSCM and Schofield's models demonstrated good performance in the retrieval of absorption subcomponents, a(ph)(lambda), a(dg)(lambda), a(d)(lambda) and S-d. Effect of applying baseline correction to a(d)(lambda) on model performance is studied. Tuning with in situ data can further improve the absorption subcomponent and slope parameter retrieval capability of the models. (C) 2021 Institute of Oceanology of the Polish Academy of Sciences. Production and hosting by Elsevier B.V.

关键词： Absorption Phytoptankton CDOM Non-algal particulate matter partitioning algorithms

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Histogram partitioning algorithms for adaptive and autonomous threshold estimation in cognitive radio-based industrial wireless sensor networks

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2019年第10期30卷 n/a-n/a页

作者： Onumanyi, A. J. Abu-Mahfouz, A. M. Hancke, G. P. Univ Pretoria Dept Elect Elect & Comp Engn ZA-0028 Pretoria South Africa CSIR Pretoria South Africa City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

Modern energy detectors typically use adaptive threshold estimation algorithms to improve signal detection in cognitive radio-based industrial wireless sensor networks (CR-IWSNs). However, a number of adaptive threshold estimation algorithms often perform poorly under noise uncertainty conditions since they are typically unable to auto-adapt their parameter values per changing spectra conditions. Consequently, in this paper, we have developed two new algorithms to accurately and autonomously estimate threshold values in CR-IWSNs under dynamic spectra conditions. The first algorithm is a parametric-based technique termed the histogram partitioning algorithm, whereas the second algorithm is a fully autonomous variant termed the mean-based histogram partitioning algorithm. We have evaluated and compared both algorithms with some well-known methods under different CR sensing conditions. Our findings indicate that both algorithms maintained over 90% probability of detection in both narrow and wideband sensing conditions and less than 10% probability of false alarm under noise-only conditions. Both algorithms are quick and highly scalable with a time complexity of O(V), where V is the total number of input samples. The simplicity, effectiveness, and viability of both algorithms make them typically suited for use in CR-IWSN applications.

关键词： adaptive thresholding histograms energy detector signal acquisition algorithms probability of false alarm partitioning algorithms complexity classes Wireless sensor networks Self tuning

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Further Development and Performance Assessment of Automatic partitioning algorithms for Structured Overset Grids

Further Development and Performance Assessment of Automatic ...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Sherer, Scott E. Garmann, Daniel J. Air Force Res Lab Aerodynam Technol Branch AFRL RQVA Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

Additional upgrades to a software tool developed in the Aerospace Systems Directorate to perform domain decomposition of structured overset-grid systems for use in a Message-Passing Interface (MPI) environment on massively parallel high-performance computing (HPC) platforms are presented. This code, referred to as BUNGE, recursively sweeps through all potential partitions of a given topology, but does so in such a way as to quickly rule out invalid partitions, thus limiting the overall time spent performing the search. In this work, modifications to BUNGE, including the implementation of a partitioning approach based on a user-specified target block size and the development of a new approach to quickly find high-quality partitions with a specified number of processors are presented and examined for robustness and quality of the partition. The results from the BUNGE partitions are compared to those produced by the NASA structured-overset CFD solver OVERFLOW using its default partitioning algorithm over a wide range of test grid systems and processor counts. OVERFLOW was also modified to accept user-specified partitions, and the timing obtained from multiple runs of OVERFLOW using partitions obtained from BUNGE are compared to its default algorithm. Relative speedups of between 7% and 37% for both steady-state and unsteady computations are demonstrated when using the BUNGE partition with OVERFLOW compared to its default splitting algorithm over a numerous sample grid systems and processor counts.

关键词： partitions PROCESSOR aerospace systems Overflow partitioning algorithms Grid systems Software Tools Capsizing message passing High Performance Computing Performance assessment

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Distributed partitioning algorithms for Locational Optimization of Multiagent Networks in SE(2)

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2018年第1期63卷 101-116页

作者： Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

This paper is concerned with the development of distributed spatial partitioning algorithms for locational optimization problems involving networks of agents with planar rigid body dynamics subject to communication constraints. The domain of the problems we consider is a three-dimensional (3-D) nonflat manifold embedded in the state space of the agents, which we refer to as the terminal manifold. The approach we propose allows us to associate the partition of the 3-D terminal manifold, which is induced by a nonquadratic proximity metric and comprised of nonconvex cells, with a one-parameter family of partitions of 2-D flat manifolds, which are induced by (parametric) quadratic proximity metrics and comprised of convex polygonal cells. By exploiting the special structure of the parametric partitions, we develop distributed partitioning algorithms that converge in a finite number of steps. Subsequently, we utilize the solutions to the latter problems to solve a class of locational optimization problems over the terminal manifold. Numerical simulations that illustrate the capabilities of the proposed algorithms are also presented.

关键词： Distributed algorithms locational optimization multi-agent networks partitioning algorithms

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Performance evaluation of model-driven partitioning algorithms for data-parallel kernels on heterogeneous platforms

COMPUTATIONAL AND MATHEMATICAL METHODS

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COMPUTATIONAL AND MATHEMATICAL METHODS 2020年第1期2卷

作者： Rico-Gallego, Juan A. Diaz-Martin, Juan C. Moreno-Alvarez, Sergio Calvo-Jurado, Carmen Garcia-Zapata, Juan L. Univ Extremadura Dept Comp Syst Engn & Telemat Caceres Spain Univ Extremadura Dept Comp Technol & Commun Caceres Spain Univ Extremadura Dept Math Caceres Spain

Data- parallel applications running on heterogeneous high-performance computing platforms require a nonuniform distribution of the workload between available processes. Data partitioning algorithms are formulated as an optimization problem. Departing from the computational performance models of the processes, the goal is to find the partition that minimizes the communication cost. Traditionally, communication volume is the metric used to guide the partitioning. This metric, however, is unable to capture the complexity of current heterogeneous systems, which show uneven communication channels and execute applications with different communication patterns. In this paper, we discuss the role of analytical communication performance models as a metric in partitioning algorithms. First, we describe a method to programmatically predict the communication cost of a data-parallel kernel based on the tau -Lop analytical model. We show that this figure better captures the communication features of applications and platforms. We present results showing that this approach builds partitions that equal or improve the performance of data parallel applications on heterogeneous platforms with respect to previous volume-based strategies.

关键词： communication optimization communication performance models data-parallel kernels heterogeneous platforms partitioning algorithms

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DFG partitioning algorithms for Coarse Grained Reconfigurable Array Assisted RTL Simulation Accelerators

DFG Partitioning Algorithms for Coarse Grained Reconfigurabl...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Ipsita Biswas Mahapatra Utkarsh Agarwal S. K. Nandy CAD Lab Indian Institute of Science Bangalore India Adobe Systems Bangalore India

As the complexity of circuit design increases, verification of these circuits through simulation also becomes extremely challenging. This creates a bottleneck in the IC design process. Distributed simulation is one way of solving this problem where the simulation workload is distributed among the parallel processors involved in the simulation. However the design has to be carefully partitioned for this purpose. In order to perform distributed simulation, many efficient partitioning algorithms have been proposed till date. These algorithms mostly partition gate level netlist or logic circuits and reduces inter-processor communication by minimizing cutsize for a given constraint of load balance. In this paper, we present two different partitioning schemes for performing distributed simulation. They are: a Discrete Particle Swarm optimization (DPSO) based partitioning algorithm (DPSO-PA) and an effective partitioning heuristic. These algorithms partitions Data Flow Graph (DFG) for a recently proposed Coarse Grained Reconfigurable Array assisted Hardware Accelerator (CGRA-HA). We also propose an improved version of the original DPSO methodology through careful selection of initial partition sets. It is found that the proposed heuristic based partitioning algorithm outperforms the modified DPSO-PA in terms of lesser cut-edges.

关键词： partitioning algorithms Integrated circuit modeling Load modeling Logic gates Sociology Statistics Hardware design languages

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Nearly Balanced Work partitioning for Heterogeneous algorithms 46

Nearly Balanced Work Partitioning for Heterogeneous Algorith...

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46th International Conference on Parallel Processing Workshops (ICPPW)

作者： Mallipeddi, Hardhik Banerjee, Dip Sankar Ramamoorthy, Kiran Raj Srinathan, Kannan Kothapalli, Kishore Int Inst Informat Technol Hyderabad 500032 Andhra Pradesh India Indian Inst Informat Technol Gopinath Bordoloi GNB Rd Gauhati 781001 India

ISBN: (纸本)9781538610428

The architectural trend towards heterogeneity has pushed heterogeneous computing to the fore of parallel computing research. Heterogeneous algorithms, often carefully hand-crafted, have been designed for several important problems from parallel computing such as sorting, graph algorithms, matrix computations, and the like. A majority of these algorithms follow a work partitioning approach where the input is divided into appropriate sized parts so that individual devices can process the "right" parts of the input. However, arriving at a good work partitioning is usually non-trivial and may require extensive empirical search. Such an extensive empirical search can potentially offset any gains accrued out of heterogeneous algorithms. Other recently proposed approaches too are in general inadequate. In this paper, we propose a simple and effective technique for work partitioning in the context of heterogeneous algorithms. Our technique is based on sampling and therefore can adapt to both the algorithm used and the input instance. Our technique is generic in its applicability as we will demonstrate in this paper. We validate our technique on three problems: finding the connected components of a graph (CC), multiplying two unstructured sparse matrices (spmm), and multiplying two scale-free sparse matrices. For these problems, we show that using our method, we can find the required threshold that is under 10% away from the best possible thresholds.

关键词： partitioning algorithms Algorithm design and analysis Sparse matrices Runtime Graphics processing units Sampling methods Parallel processing

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partitioning algorithms for multi-agent systems based on finite-time proximity metrics

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AUTOMATICA 2015年 55卷 176-182页

作者： Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

We address a generalized Voronoi partitioning problem for a team of mobile agents with nonlinear dynamics with respect to a state-dependent proximity metric. In particular, the proximity (pseudo-) metric corresponds to the reduction of a generalized energy metric that occurs during the transfer of an agent to an arbitrary destination with zero terminal velocity, in finite time. The realization of every finite-time state transition takes place by means of a class of continuous feedback control laws that render the closed loop dynamics of each mobile agent non-Lipschitzian. The arrival time also turns out to be a state-dependent quantity, whose functional description is not prescribed a priori. We show that the partitioning problem studied in this work can admit a decentralized solution, that is, each agent can compute its own cell independently from its teammates provided that is aware of the positions and velocities of its neighboring agents. Numerical simulations that illustrate the theoretical developments are also presented. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Autonomous agents Voronoi diagrams partitioning algorithms Finite-time control Computational methods

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Survey of data partitioning algorithms for big data stores

Survey of data partitioning algorithms for big data stores

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Shraddha Phansalkar Swati Ahirrao Symbiosis Institute of Technology Symbiosis International University Pune India

ISBN: (纸本)9781509036707

Data partitioning plays an important role in distributed systems to elevate performance of the applications. Big data applications require the augmented metrics of performance like responsiveness, availability, scalability and throughput. Data partitioning across multiple nodes improves the performance of the application with respect to its scalability and throughput. Although there is a rich literature on the data partitioning algorithms in distributed systems for big data applications, there is a need to classify as well as regroup the algorithms based on their strategies. The paper presents an exhaustive classification of data partitioning algorithms based on their strategies as well as operational units. A survey of this kind gives an insight to the user about not only a comparative analysis of performance of approaches but also suggests suitability of these approaches for candidate big data applications.

关键词： partitioning algorithms Clustering algorithms Big Data applications Distributed databases Algorithm design and analysis Cost function

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Predictive Evaluation of partitioning algorithms through Runtime Modelling

Predictive Evaluation of Partitioning Algorithms through Run...

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International Conference on High Performance Computing

作者： R. A. Bunt S. A. Wright S. A. Jarvis Y. K. Ho M. J. Street Department of Computer Science University of Warwick Coventry United Kingdom Design Systems Engineering Rolls-Royce plc Derby United Kingdom

ISBN: (纸本)9781509054121

Performance modelling unstructured mesh codes is a challenging process, due to the difficulty of capturing their memory access patterns, and their communication patterns at varying scale. In this paper we first develop extensions to an existing runtime performance model, aimed at overcoming the former, which we validate on up to 1,024 cores of a Haswellbased cluster, using both a geometric partitioning algorithm and ParMETIS to partition the input deck, with a maximum absolute runtime error of 12.63% and 11.55% respectively. To overcome the latter, we develop an application representative of the mesh partitioning process internal to an unstructured mesh code. This application is able to generate partitioning data that is usable with the performance model to produce predicted application runtimes within 7.31% of those produced using empirically collected data. We then demonstrate the use of the performance model by undertaking a predictive comparison among several partitioning algorithms on up to 30,000 cores. Additionally, we correctly predict the ineffectiveness of the geometric partitioning algorithm at 512 and 1024 cores.

关键词： Runtime Computational modeling Data models Predictive models partitioning algorithms Prediction algorithms Analytical models

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