the proceedings contain 71 papers. the special focus in this conference is on Simulated Evolution and Learning. the topics include: Solving dynamic optimisation problem with variable dimensions;a probabilistic evoluti...
ISBN:
(纸本)9783319135625
the proceedings contain 71 papers. the special focus in this conference is on Simulated Evolution and Learning. the topics include: Solving dynamic optimisation problem with variable dimensions;a probabilistic evolutionary optimization approach to compute quasiparticle braids;adaptive system design by a simultaneous evolution of morphology and information processing;generating software test data by particle swarm optimization;a steady-state genetic algorithm for the dominating tree problem;evolution of developmental timing for solving hierarchically dependent deceptive problems;the introduction of asymmetry on traditional 2-parent crossover operators for crowding and its effects;the performance effects of interaction frequency in parallel cooperative coevolution;customized selection in estimation of distribution algorithms;a hybrid GP-tabu approach to QoS-aware data intensive web service composition;a modified screening estimation of distribution algorithm for large-scale continuous optimization;clustering problems for more useful benchmarking of optimization algorithms;fuzzy clustering with fitness predator optimizer for multivariate data problems;effects of mutation and crossover operators in the optimization of traffic signal parameters;a GP approach to QoS -aware web service composition and selection;user preferences for approximation-guided multi-objective evolution;multi-objective optimisation, software effort estimation and linear models;adaptive update range of solutions in MOEA/D for multi and many-objective optimization;classification of lumbar ultrasound images with machine learning;schemata bandits for binary encoded combinatorial optimisation problems;anomaly detection using replicator neural networks trained on examples of one class and genetic programming for multiclass texture classification using a small number of instances.
the effective parallelization of processing exploiting the MPI library for the numerically exact quantum transfer matrix (QTM) and exact diagonalization (ED) deterministic simulations of chromium-based rings is propos...
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ISBN:
(数字)9783642551956
ISBN:
(纸本)9783642551956
the effective parallelization of processing exploiting the MPI library for the numerically exact quantum transfer matrix (QTM) and exact diagonalization (ED) deterministic simulations of chromium-based rings is proposed. In the QTM technique we have exploited parallelization of summation in the partition function. the efflciency of the QTM calculations is above 80% up to about 1000 processes. With our test programs we calculated low temperature torque, specific heat and entropy for the chromium ring Cr-8 exploiting realistic Hamiltonian with singleion anisotropy and the alternation of the nearest neighbor exchange couplings. Our parallelized ED technique makes use of the self-scheduling scheme and the longest processing time algorithm to distribute and diagonalize separate blocks of a Hamiltonian matrix by slave processes. Its parallelprocessing scales very well, with efflciency above 90% up to about 10 processes only. this scheme is improved by processing more input data sets in one job which leads to very good scalability up to arbitrary number of processes. the scaling is improved for both techniques when larger systems are considered.
Relevance feedback algorithms improve content-based image retrieval (CBIR) systems by effectively using relevant/non-relevant images labeled by users. the main constraint of these algorithms is the update time for lar...
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One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping withthe rate at which unprocessed data is being accumulated. In this regard, associative memory concepts ope...
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ISBN:
(纸本)9783319115689
One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping withthe rate at which unprocessed data is being accumulated. In this regard, associative memory concepts open a new pathway for accessing data in a highly distributed environment that will facilitate a parallel-distributed computational model to automatically adapt to the dynamic data environment for optimized performance. Withthis in mind, this paper targets a new type of data processing approach that will efficiently partition and distribute data for clouds, providing a parallel data access scheme that enables data storage and retrieval by association where data records are treated as patterns;hence, finding overarching relationships among distributed data sets becomes easier for a variety of pattern recognition and data-mining applications. the ability to partition data optimally and automatically will allow elastic scaling of system resources and remove one of the main obstacles in provisioning data centric software-as-a-service in clouds.
parallel computing has been the enabling technology of high-end machines for many years. Now, it has finally become the ubiquitous key to the efficient use of any kind of multi-processor computer architecture, from sm...
ISBN:
(数字)9781614993810
ISBN:
(纸本)9781614993803
parallel computing has been the enabling technology of high-end machines for many years. Now, it has finally become the ubiquitous key to the efficient use of any kind of multi-processor computer architecture, from smart phones, tablets, embedded systems and cloud computing up to exascale computers. _x000D_ this book presents the proceedings of ParCo2013 – the latest edition of the biennial internationalconference on parallel Computing – held from 10 to 13 September 2013, in Garching, Germany. the conference focused on several key parallel computing areas. themes included parallel programming models for multi- and manycore CPUs, GPUs, FPGAs and heterogeneous platforms, the performance engineering processes that must be adapted to efficiently use these new and innovative platforms, novel numerical algorithms and approaches to large-scale simulations of problems in science and engineering._x000D_ the conference programme also included twelve mini-symposia (including an industry session and a special PhD Symposium), which comprehensively represented and intensified the discussion of current hot topics in high performance and parallel computing. these special sessions covered large-scale supercomputing, novel challenges arising from parallelarchitectures (multi-/manycore, heterogeneous platforms, FPGAs), multi-level algorithms as well as multi-scale, multi-physics and multi-dimensional problems._x000D_ It is clear that parallel computing – including the processing of large data sets (“Big Data”) – will remain a persistent driver of research in all fields of innovative computing, which makes this book relevant to all those with an interest in this field.
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for the multi-frontal direct solver algorithm executed over two dimensional meshes with point singularities. the eliminati...
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ISBN:
(数字)9783642551956
ISBN:
(纸本)9783642551956
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for the multi-frontal direct solver algorithm executed over two dimensional meshes with point singularities. the elimination tree found by the optimization algorithm results in a linear computational cost of sequential direct solver. Based on the optimal elimination tree found by the optimization algorithm we construct heuristic sequential multi-frontal direct solver algorithm resulting in a linear computational cost as well as heuristic parallel multi-frontal direct solver algorithm resulting in a logarithmic computational cost. the resulting parallel algorithm is implemented on NVIDIA CUDA GPU architecture based on our graph-grammar approach.
Distributed vertex-centric graph processing systems have been recently proposed to perform different types of analytics on large graphs. these systems utilize the parallelism of shared nothing clusters. In this work w...
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Distributed vertex-centric graph processing systems have been recently proposed to perform different types of analytics on large graphs. these systems utilize the parallelism of shared nothing clusters. In this work we propose a novel model for the performance cost of such *** also define novel metrics related to the workload balance and network communication cost of clusters processing massive real graph datasets. We empirically investigate the effects of different graph partitioning mechanisms and their tradeoff for two different categories of graph processingalgorithms.
Range searching is a primal problem in computational geometry with applications to database systems, mobile computing, geographical information systems, and the like. Defined simply, the problem is to preprocess a giv...
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ISBN:
(数字)9783319098739
ISBN:
(纸本)9783319098739;9783319098722
Range searching is a primal problem in computational geometry with applications to database systems, mobile computing, geographical information systems, and the like. Defined simply, the problem is to preprocess a given a set of points in a d-dimensional space so that the points that lie inside an orthogonal query rectangle can be efficiently reported. Many practical applications of range trees require one to process a massive amount of points and a massive number of queries. In this context, we propose an efficient parallel implementation of range trees on manycore architectures such as GPUs. We extend our implementation to query processing. While queries can be batched together to exploit inter-query parallelism, we also utilize intra-query parallelism. this inter-and intra-query parallelism greatly reduces the per query latency thereby increasing the throughput. On an input of 1 M points in a 2-dimensional space, our implementation on a single Nvidia GTX 580 GPU for constructing a range tree shows an improvement of 12X over a 12-threaded CPU implementation. We also achieve an average throughput of 10 M queries per second for answering 4 M queries on a range tree containing 1 M points on a Nvidia GTX 580 GPU. We extend our implementation to an application where we seek to report the set of maximal points in a given orthogonal query rectangle.
Actor-based programming is widely used in concurrent systems to express dependencies between tasks and exploit task-level and pipeline parallelism. Current and emerging multicore architectures induce big challenges in...
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the goal of this paper is to describe problems associated with storage and processing of confidential data in public clouds, and to propose relevant mitigation strategies. In our opinion many types of data in the comm...
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ISBN:
(纸本)9783642552243
the goal of this paper is to describe problems associated with storage and processing of confidential data in public clouds, and to propose relevant mitigation strategies. In our opinion many types of data in the commercial and scientific worlds require special attention to protect them against possible mishandling. this issue affects highly valuable data, such as trade secrets and financial information, as well as personal data including medical records used in scientific research. We analyse situations which require special care and next we propose a set of solutions for ensuring data security and describe feasibility studies based on tests performed using popular cryptographic software (OpenSSL). those solutions would allow to fullfil objectives of the paper which are to analyze the requirements of scientific software regarding protection of confidential data, the nature of the data itself and threats against those assets.
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