We consider the problem of distributedthroughput maximization for multi-channel ALOHA networks. We focus on networks containing a large number of users that transmit over a low number of channels. First, we consider ...
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We consider the problem of distributedthroughput maximization for multi-channel ALOHA networks. We focus on networks containing a large number of users that transmit over a low number of channels. First, we consider the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a distributed best-response algorithm to solve the rate maximization problem, where each user updates its strategy using its local channel state information (CSI) and by monitoring the channel utilization. We then consider the case where users are not restricted by transmission probability constraints. distributed optimization of the network throughput under uncertainty is mandatory since the transmission probabilities of other users are unknown. We propose a distributed scheme to solve the throughput optimization problem under uncertainty, where users adjust their transmission probability to maximize their rates, but maintain the desired load on the channels. We propose sequential and parallel algorithms for this purpose.
the two degrees of freedom parallel robot Par2 is designed for high-speed and high-accuracy industrial pick-and-place operation tasks. As a result of high acceleration trajectories, its end-effector undergoes some und...
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the Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of...
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ISBN:
(纸本)9781450323697
the Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. the effectiveness of feature- related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommend- ing software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). then, we mine the hidden affnities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. the results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. the result of feature recommendation is effective and interesting. Categories and Subject Descriptors D.2.9 [Software Engineering]: Mining Software Reposi- tory;H.3.3 [Information Storage and retrieval]: Fea- ture Model, Clustering, Query formulation General Terms Algorithms, Human Factors.
We analyse gather-scatter performance bottlenecks in molecular dynamics codes and the challenges that they pose for obtaining benefits from SIMD execution. this analysis informs a number of novel code-level and algori...
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We analyse gather-scatter performance bottlenecks in molecular dynamics codes and the challenges that they pose for obtaining benefits from SIMD execution. this analysis informs a number of novel code-level and algorithmic improvements to Sandia's miniMD benchmark, which we demonstrate using three SIMD widths (128-, 256and 512bit). the applicability of these optimisations to wider SIMD is discussed, and we show that the conventional approach of exposing more parallelism through redundant computation is not necessarily best. In single precision, our optimised implementation is up to 5x faster than the original scalar code running on Intel®Xeon®processors with 256-bit SIMD, and adding a single Intel®Xeon Phi™coprocessor provides up to an additional 2x performance increase. these results demonstrate: (i) the importance of effective SIMD utilisation for molecular dynamics codes on current and future hardware; and (ii) the considerable performance increase afforded by the use of Intel®Xeon Phi™coprocessors for highly parallel workloads.
the management performance of cloud systems is measured by the capacity of the cloud for controlling virtual infrastructures and their capability to run parallel-computing applications and distributed-processing servi...
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the management performance of cloud systems is measured by the capacity of the cloud for controlling virtual infrastructures and their capability to run parallel-computing applications and distributed-processing services independently. the challenge about how this management performance can be done more dynamically (self-organization) by means of distributed user data and application data demands is yet an area to explore. this paper introduces first a functional architecture design, following the principles for cloud-based service lifecycle control and service composition in cloud, and second an in-house approach enabling self-organization for cloud services controlling the installation of virtual machines by using event-driven management operations acting as a proof of concept implementation. From a management point of view in cloud, enabling control of virtual infrastructures as a response to performance protocols by means of event(s) data processing is fundamental. Likewise managing cloud services lifecycle by enabling scalable applications and using distributed information systems and linked data processing, guarantee the self-organizing feature for cloud systems. Finally multiple advantages arise when infrastructure performance and end user data are used in cloud service management as it is discussed in this paper.
the growth of data used by data-intensive computations has out-paced the growth of the power of a single processor. In this paper, we propose a high performance and scalability workflow engine, a lightweight parallel ...
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ISBN:
(纸本)9781479900961
the growth of data used by data-intensive computations has out-paced the growth of the power of a single processor. In this paper, we propose a high performance and scalability workflow engine, a lightweight parallel and distributed computing platform based on HADOOP and OSGI. Workflow engine provides flexible and scalable OSGI-based interfaces which user can implement to define data processing functions and expand functions in HADOOP ecosystem. Workflow engine consists of invoking interfaces, scheduler engine, optimizer and fault-tolerance manager. Given a workflow process, the engine analyzes data dependencies among nodes, then dispatches them to Map Reduce clusters based on the current status of the system. this paper also describes the design and strategy in detail on implementation processes, key points, relationships of each participants and etc. Our experiment demonstrates that workflow engine can significantly improve the performance of workflow execution.
Sparse Matrix-Vector Multiplication (SMVM) on parallel hardware is a very sophisticated problem because of the irregular data communication requirements. the communication volume in the parallel hardware is determined...
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ISBN:
(纸本)9780769548982;9781467345668
Sparse Matrix-Vector Multiplication (SMVM) on parallel hardware is a very sophisticated problem because of the irregular data communication requirements. the communication volume in the parallel hardware is determined by how data is distributed among the processing elements. In this paper we introduce two methods of data mapping for SMVM based on Network-on-Chip (NoC) in order to spread the load among its components. Later, we introduce the effect of recordering of the sparse matrix on those mapping methods. Simulations are performed using an OMNet++ based NoC simulator.
parallelization of time-dependent partial differential equations (PDEs) can be accomplished by time decomposition using the parareal algorithm. While the parareal algorithm was designed to enable real-time simulations...
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ISBN:
(纸本)9780769546766
parallelization of time-dependent partial differential equations (PDEs) can be accomplished by time decomposition using the parareal algorithm. While the parareal algorithm was designed to enable real-time simulations, it holds particular promise for long time simulations on computational grids and clouds, due its low communication overhead and potential for adaptation to heterogeneous processors. this contribution extends previous work on the scheduling of tasks of the parareal algorithm to resources with heterogeneous CPU performance. Experiments on Amazon's EC2 show the suitability of this algorithm for execution on a heterogeneous cloud platform and its insensitivity to network latency.
Finding a biologically relevant sequence alignment may be difficult since several sequence alignments are possible, taking different parameters in consideration. A perceptron neuron can be used to associate weights to...
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ISBN:
(纸本)9780769546766
Finding a biologically relevant sequence alignment may be difficult since several sequence alignments are possible, taking different parameters in consideration. A perceptron neuron can be used to associate weights to a set of alignment characteristics and then deciding if two residues should be aligned. Finding a good set of weights can be a hard problem and simulated annealing can be used to find a good set but can take a long time. In this paper, we propose a parallelization strategy for simulated annealing optimizing a Fragment Based Alignment in Linear Space (FBALS). the results were superior to the competing algorithm and the obtained speedups were compatible withthe number of processing cores, indicating a good parallel strategy.
this paper proposes a new parallel search procedure for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. the proposed procedure first uses parall...
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