the proceedings contain 52 papers. the special focus in this conference is on Big Data and Its Applications. the topics include: Preference-aware HDFS for hybrid storage;urban traffic congestion prediction using float...
ISBN:
(纸本)9783319271217
the proceedings contain 52 papers. the special focus in this conference is on Big Data and Its Applications. the topics include: Preference-aware HDFS for hybrid storage;urban traffic congestion prediction using floating car trajectory data;a metadata cooperative caching architecture based on SSD and DRAM for file systems;parallel training GBRT based on kmeans histogram approximation for big data;an intelligent clustering algorithm based on mutual reinforcement;an effective method for gender classification with convolutional neural networks;a highly efficient indexing and retrieving method for astronomical big data of time series images;specialized FPGA-based accelerator architecture for data-intensive k-means algorithms;effectively identifying hot data in large-scale I/O streams with enhanced temporal locality;a search-efficient hybrid storage system for massive text data;enhancing parallel data loading for large scale scientific database;tradeoff between the price of distributing a database and its collusion resistance based on concatenated codes;a mapreduce reinforced distributed sequential pattern mining algorithm;a fast documents classification method based on simhash;identification of natural images and computer generated graphics using multi-fractal differences of PRNU;enriching document representation withthe deviations of word co-occurrence frequencies;big data analytics and visualization with spatio-temporal correlations for traffic accidents;a novel app recommendation method based on SVD and social influence;a segmentation hybrid index structure for temporal data and a refactored content-aware host-side SSD cache.
the proceedings contain 59 papers. the special focus in this conference is on Applications of parallel and Distributed Computing. the topics include: On exploring a virtual agent negotiation inspired approach for rout...
ISBN:
(纸本)9783319271361
the proceedings contain 59 papers. the special focus in this conference is on Applications of parallel and Distributed Computing. the topics include: On exploring a virtual agent negotiation inspired approach for route guidance in urban traffic networks;optimization of binomial option pricing on intel MIC heterogeneous system;stencil computations on HPC-oriented ARMv8 64-bit multi-core processor;a particle swarm optimization algorithm for controller placement problem in software defined network;a streaming execution method for multi-services in mobile cloud computing;economy-oriented deadline scheduling policy for render system using IaaS cloud;towards detailed tissue-scale 3D simulations of electrical activity and calcium handling in the human cardiac ventricle;task parallel implementation of matrix multiplication on multi-socket multi-core architectures;refactoring for separation of concurrent concerns;exploiting scalable parallelism for remote sensing analysis models by data transformation graph;resource-efficient vibration data collection in cyber-physical systems;a new approach for vehicle recognition and tracking in multi-camera traffic system;a scalable distributed fingerprint identification system;energy saving and load balancing for SDN based on multi-objective particle swarm optimization;pre-stack kirchhoff time migration on hadoop and spark;a cyber physical system with GPU for CNC applications;a solution of the controller placement problem in software defined networks;parallel column subset selection of kernel matrix for scaling up support vector machines;real-time deconvolution with GPU and spark for big imaging data analysis and parallel kirchhoff pre-stack depth migration on large high performance clusters.
the proceedings contain 59 papers. the special focus in this conference is on Software Systems, Programming Models, Performance Modeling and Evaluation. the topics include: A scalable fault-tolerance programing model ...
ISBN:
(纸本)9783319271392
the proceedings contain 59 papers. the special focus in this conference is on Software Systems, Programming Models, Performance Modeling and Evaluation. the topics include: A scalable fault-tolerance programing model on MIC cluster;multi-chunk redundant array of independent SSDS with improved performance;an energy efficient storage system for astronomical observation data on dome a;parallel aware hybrid solid-state storage;automatic optimization of software transactional memory through linear regression and decision tree;a data-centric tool to improve the performance of multithreaded program on NUMA;a light-weight hot data identification scheme via grouping-based LRU lists;towards interactive programming withparallel linear algebra in r;enhancing i/o scheduler performance by exploiting internal parallelism of SSDS;a performance and scalability analysis of the MPI based tools utilized in a large ice sheet model executing in a multicore environment;an efficient algorithm for a generalized LCS problem;on exploring a quantum particle swarm optimization method for urban traffic light scheduling;identifying repeated interleavings to improve the efficiency of concurrency bug detection;global reliability evaluation for cloud storage systems with proactive fault tolerance;performance evaluation and optimization of Wi-Fi display on android;a novel scheduling algorithm for file fetch in transparent computing;joint power and reduced spectral leakage-based resource allocation for d2d communications in 5G and an optimization strategy of energy consumption for data transmission based on optimal stopping theory in mobile networks.
the proceedings contain 58 papers. the special focus in this conference is on parallel and Distributed architectures. the topics include: parallelizing block cryptography algorithms on speculative multicores;performan...
ISBN:
(纸本)9783319271187
the proceedings contain 58 papers. the special focus in this conference is on parallel and Distributed architectures. the topics include: parallelizing block cryptography algorithms on speculative multicores;performance characterization and optimization for intel xeon phi coprocessor;an extended MDS code to improve single write performance of disk arrays for correcting triple disk failures;a distributed location-based service discovery protocol for vehicular ad-hoc networks;unified virtual memory support for deep CNN accelerator on SoC FPGA;dynamic time slice scheduler for virtual machine monitor;memory-aware NoC application mapping based on adaptive genetic algorithm;a study on non-volatile 3D stacked memory for big data applications;parallel implementation of dense optical flow computation on many-core processor;a power-conserving online scheduling scheme for video streaming services;prevent deadlock and remove blocking for self-timed systems;improving the memory efficiency of in-memory mapreduce based HPC systems;dual best-first search mapping algorithm for shared-cache multicore processors;energy efficient network-on-chip router with heterogeneous virtual channels;availability and network-aware mapreduce task scheduling over the internet;an optimized algorithm based on CRS codes in big data storage systems;quantum computer simulation on multi-GPU incorporating data locality;query execution optimization based on incremental update in database distributed middleware;coding-based cooperative caching in data broadcast environments;usage history-directed power management for smartphones;a mobile application distribution method and a clustering algorithm based on rough sets for the recommendation domain in trust-based access control.
Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the ...
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ISBN:
(纸本)9783030389918;9783030389901
Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. the implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domainspecific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domainspecific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.
Vehicle Routing Problems (VRPs) are well-know combinatorial optimization problems used to design an optimal route for a fleet of vehicles to service a set of customers under a number of constraints. Due to their NP-ha...
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ISBN:
(纸本)9783030602451;9783030602444
Vehicle Routing Problems (VRPs) are well-know combinatorial optimization problems used to design an optimal route for a fleet of vehicles to service a set of customers under a number of constraints. Due to their NP-hard complexity, a number of purely computational techniques have been proposed in recent years in order to solve them. Among these techniques, nature-inspired algorithms have proven their effectiveness in terms of accuracy and convergence speed. Some of these methods are also designed in such a way to decompose the basic problem into a number of sub-problems which are subsequently solved in parallel computing environments. It is therefore the purpose of this paper to review the fresh corpus of the literature dealing withthe main approaches proposed over the past few years to solve combinatorial optimization problems in general and, in particular, the VRP and its different variants. Bibliometric and review studies are conducted with a special attention paid to metaheuristic strategies involving procedures withparallelarchitectures. the obtained results show an expansion of the use of parallelalgorithms for solving various VRPs. Nevertheless, the regression in the number of citations in this framework proves that the interest of the research community has declined somewhat in recent years. this decline may be explained by the lack of rigorous mathematical results and practical interfaces under famous calculation softwares.
In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. the extraordinary development of the denoising area is no doubt due to the ever expa...
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ISBN:
(纸本)9783030602451;9783030602444
In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. the extraordinary development of the denoising area is no doubt due to the ever expanding and successful computing technology, but also to the emergence of the multi-resolution analysis (MRA) on both mathematical and algorithmic levels. However, many denoising techniques still remain ineffective in dealing with certain types of noise. Other methods can be too expensive, given their nested and complicated structure. therefore, in this paper, A new multi-scale parallel denoising paradigm is defined and tested. A comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). the comparison is carried out in this framework of multi-scaled parallel denoising, where a Non-Local Means (NLM) filter is implemented and adjusted scale-by-scale to a sample of X-ray benchmark images. Some state-of-the-art denoising methods were also used in the evaluation. the numerical results proved the effectiveness of the multi-scaled parallel denoising in terms of accuracy and speed of convergence, especially when the NLM filtering is coupled withthe EMD. this shows a bright future for their medical use in the next few years.
In this article, we use a Kullback-Leibler random sample partition data model to generate a set of disjoint data blocks, where each block is a good representation of the entire data set. Every random sample partition ...
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ISBN:
(纸本)9783030602451;9783030602444
In this article, we use a Kullback-Leibler random sample partition data model to generate a set of disjoint data blocks, where each block is a good representation of the entire data set. Every random sample partition (RSP) block has a sample distribution function similar to the entire data set. To obtain the statistical measure between them, Kernel Density Estimation (KDE) with a dual-tree recursion data structure is firstly applied to fast estimate the probability density of each block. then, based on the Kullback-Leibler (KL) divergence measure, we can obtain the statistical similarity between a randomly selected RSP data block and other RSP data blocks. We rank the RSP data blocks according to their divergence values in descending order and choose the first ten for an ensemble classification learning. the classification models are established in parallel for the selected RSP data blocks and the final ensemble classification model is obtained by the weighted voting ensemble strategy. the experiments were conducted by building XGboost model based on those ten blocks in parallel, and we incrementally ensemble them according to their KL values. the testing classification results show that our method can increase the generalization capability of the ensemble classification model. It could reduce the model building time in parallel computation environment by using less than 15% of the entire data, which could also solve the memory constraints of big data analysis.
Withthe increasing size of high performance computing systems, the expensive communication overhead between processors has become a key factor leading to the performance bottleneck. However, default process-to-proces...
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ISBN:
(纸本)9783030050511;9783030050504
Withthe increasing size of high performance computing systems, the expensive communication overhead between processors has become a key factor leading to the performance bottleneck. However, default process-to-processor mapping strategies do not take into account the topology of the interconnection network, and thus the distance spanned by communication messages may be particularly far. In order to enhance the communication locality, we propose a new topology-aware mapping method called TAMM. By generating an accurate description of the communication pattern and network topology, TAMM employs a two-step optimization strategy to obtain an efficient mapping solution for various parallel applications. this strategy first extracts an appropriate subset of all idle computing resources on the underlying system and then constructs an optimized one-to-one mapping with a refined iterative algorithm. Experimental results demonstrate that TAMM can effectively improve the communication performance on the Tianhe-2A supercomputer.
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