A novel MapReduce computation model in hybrid computing environment called HybridMR is proposed in the paper. Using this model, high performance cluster nodes and heterogeneous desktop PCs in Internet or Intranet can ...
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ISBN:
(数字)9783319111940
ISBN:
(纸本)9783319111940;9783319111933
A novel MapReduce computation model in hybrid computing environment called HybridMR is proposed in the paper. Using this model, high performance cluster nodes and heterogeneous desktop PCs in Internet or Intranet can be integrated to form a hybrid computing environment. In this way, the computation and storage capability of large-scale desktop PCs can be fully utilized to process large-scale datasets. HybridMR relies on a hybrid distributed file system called HybridDFS, and a time-out method has been used in HybridDFS to prevent volatility of desktop PCs, and file replication mechanism is used to realize reliable storage. A new node priority-based fair scheduling (NPBFS) algorithm has been developed in HybridMR to achieve both data storage balance and job assignment balance by assigning each node a priority through quantifying CPU speed, memory size and I/O bandwidth. Performance evaluation results show that the proposed hybrid computation model not only achieves reliable MapReduce computation, reduces task response time and improves the performance of MapReduce, but also reduces the computation cost and achieves a greener computing mode.
Matrix eigenvalue theory has become an important analysis tool in scientific computing. Sometimes, people do not need to find all eigenvalues but only the maximum eigenvalue. Existing algorithms of finding the maximum...
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ISBN:
(数字)9783319111940
ISBN:
(纸本)9783319111940;9783319111933
Matrix eigenvalue theory has become an important analysis tool in scientific computing. Sometimes, people do not need to find all eigenvalues but only the maximum eigenvalue. Existing algorithms of finding the maximum eigenvalue of matrices are implemented sequentially. Withthe increasing of the orders of matrices, the workload of calculation is getting heavier. therefore, traditional sequential methods are unable to meet the need of fast calculation for large matrices. this paper proposes a parallel algorithm named PA-ST to find the maximum eigenvalue of positive matrices by using similarity transformation which is implemented by CUDA (Computer Unified Device Architecture) on GPU (Graphic Process Unit). To the best of our knowledge, this is the first CUDA based parallel algorithm of calculating maximum eigenvalue of matrices. In order to improve the performance, optimization techniques are applied in this paper such as using the shared memory rather than the global memory to improve the speed of computation, avoiding bank conflicts by setting the span index, satisfying the principle of coalesced memory access, and by using single-precision floating-point arithmetic and the pinned memory to reduce the copy operation and obtain higher data transfer bandwidth between the host and the GPU device. the experimental results show that the similarity transformation technique can significantly shorten the running time compared to the sequential algorithm and the speedup ratio is nearly stable when the number of iterations increases. As the matrix order increases, the running time of the sequential algorithm and PA-ST increases correspondingly. Experiments also show that the speedup ratio of the PA-ST is between 2.85 and 35.028.
the proceedings contain 79 papers. the topics discussed include: secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks;dynamic data race detection for correl...
ISBN:
(纸本)9783642246494
the proceedings contain 79 papers. the topics discussed include: secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks;dynamic data race detection for correlated variables;distributed mining of constrained frequent sets from uncertain data;set-to-set disjoint-paths routing in recursive dual-net;redflag: a framework for analysis of kernel-level concurrency;redflag: a framework for analysis of kernel-level concurrency;fault-tolerant routing based on approximate directed routable probabilities for hypercubes;adaptive resource remapping through live migration of virtual machines;anonymous communication over invisible mix rings;lightweight transactional arrays for read-dominated workloads;cascading multi-way bounded wait timer management for moody and autonomous systems;and world-wide distributed multiple replications in parallel for quantitative sequential simulation.
the proceedings contain 79 papers. the topics discussed include: secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks;dynamic data race detection for correl...
ISBN:
(纸本)9783642246685
the proceedings contain 79 papers. the topics discussed include: secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks;dynamic data race detection for correlated variables;distributed mining of constrained frequent sets from uncertain data;set-to-set disjoint-paths routing in recursive dual-net;redflag: a framework for analysis of kernel-level concurrency;redflag: a framework for analysis of kernel-level concurrency;fault-tolerant routing based on approximate directed routable probabilities for hypercubes;adaptive resource remapping through live migration of virtual machines;anonymous communication over invisible mix rings;lightweight transactional arrays for read-dominated workloads;cascading multi-way bounded wait timer management for moody and autonomous systems;and world-wide distributed multiple replications in parallel for quantitative sequential simulation.
In this paper, we address the problem of defining a semantic indexing techniques based on RDF triples. In particular, we define algorithms for: i) defining clustering techniques of semantically similar RDF triplets;ii...
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this paper introduces CLEPTA, an extension to the PROFETA robotic programming framework for the integration of cloud services in developing the software for autonomous robots. CLEPTA provides a set of basic classes, t...
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this paper introduces a heuristic-based scheduler to optimise the throughput and latency of stream programs with dynamic network structure. the novelty is the utilisation of positive and negative demands of the stream...
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With a suitable method to rank the user influence in micro-blogging service, we could get influential individuals to make information reach large populations. Here a novel parallel social influence model is proposed t...
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the number of nodes inside supercomputers is continuously increasing. As detailed in the TOP500 list, there are now systems that include more than one million nodes;for instance China's Tianhe-2. To cope withthis...
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