this paper proposes solution to optimize the peak side lobes level (PSLL) in a distributed random antenna array (RAA) when locations of the nodes in the array cannot be manipulated. Using the conventional beamforming ...
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ISBN:
(纸本)9781479920815
this paper proposes solution to optimize the peak side lobes level (PSLL) in a distributed random antenna array (RAA) when locations of the nodes in the array cannot be manipulated. Using the conventional beamforming method, RAA produces a poor beam pattern with high sidelobe level, which greatly reduces the performance and the efficiency of the antenna. Existing literature focuses on finding the best position of antenna placement in RAA to lower the sidelobes. this is not feasible when the user has no autonomy over the position of the antenna elements. Our proposed solution achieves beampattern with much lower PSLL regardless of the array size and number of nodes in the array. the proposed method also enables up to 40% of energy savings when the size of array is small and 20% of savings when bigger array size is considered.
this paper describes a distributed coordinated checkpointing protocol that always ensures a consistent set of checkpoints. A checkpoint initiator initiates checkpointing activity and the protocol followed is two phase...
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this paper describes a distributed coordinated checkpointing protocol that always ensures a consistent set of checkpoints. A checkpoint initiator initiates checkpointing activity and the protocol followed is two phase with each process maintaining a tentative checkpoint till it is made permanent or aborted. However, there is no central checkpoint initiator, but each of the processes takes turn to act as the initiator. Processes take local checkpoints only after being notified by the initiator. the guaranty that no message would be lost in the system (where processes communicate via messages only) in case of failure is maintained in this work by forcing processes to refrain from sending computation messages for a certain period of time that generally equals the time a message in a network takes to reach its destination from the sender. Processes carry out local computations only during that period that eventually gets included in the current permanent checkpoint.
Nodes in a distributed System are susceptible to failures for many different reasons. In case of such failures the distributed system as a whole needs to be restored to an error free state, existing prior to failure. ...
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Nodes in a distributed System are susceptible to failures for many different reasons. In case of such failures the distributed system as a whole needs to be restored to an error free state, existing prior to failure. this restoration is done by rolling back the computation at the nodes to an error free state. To minimize the amount of computation which needs to be rolled back checkpoints or snapshots of a globally consistent state are taken from time to time. We present a synchronous checkpointing algorithm which forces a minimum number of nodes to take a checkpoint. Underlying computation need not be blocked completely during the progress of the algorithm. No additional effort needs to be expended to circumvent the problem of concurrent initiations of the algorithm, since the initiator node assumes the responsibility of completing one instance before another one can be initiated. Since the consistency of the snapshots is ensured at the time the global snapshot is taken, no time needs to be spent during recovery.
this paper proposes a parallel peer-to-peer (noted P2P) cooperative algorithm for approximately solving the constrained fixed-orientation two-staged two-dimensional cutting problem. the resolution process is based in ...
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ISBN:
(纸本)9780769543284
this paper proposes a parallel peer-to-peer (noted P2P) cooperative algorithm for approximately solving the constrained fixed-orientation two-staged two-dimensional cutting problem. the resolution process is based in three mechanisms: a beam-search search strategy, a strip generation filling procedure, and a upper bound applied for refining the selected paths. the algorithm explores, in parallel, a subset of elite nodes where each processor develops its own path according to its internal lists. the algorithms adapts to the number of processors available on the peer-to-peer platform by backuping the plateform the partial solutions. the computational investigation on the P2Pdc environment shows the good efficiency of the proposed algorithm.
In this paper, we present the improving capability of accuracy and the parallel efficiency of self-organizing neural groves (SONGs) for classification on a MIMD parallel computer. Self-generating neural networks (SGNN...
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In this paper, we present the improving capability of accuracy and the parallel efficiency of self-organizing neural groves (SONGs) for classification on a MIMD parallel computer. Self-generating neural networks (SGNNs) are originally proposed on adopting to classification or clustering by automatically constructing self-generating neural tree (SGNT) from given training data. the SONG is composed of plural SGNTs each of which is independently generated by shuffling the order of the given training data, and the output of the SONG is voted all outputs of the SGNTs. We allocate each of SGNTs to each of processors in the MIMD parallel computer. Experimental results show that the more the number of processors increases, the more the classification accuracy increases for all problems.
A wide variety of systems from many different fields use geospatial data to represent physical environments. these systems, and the data on which they operate, are not only becoming more complex, but they are increasi...
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A wide variety of systems from many different fields use geospatial data to represent physical environments. these systems, and the data on which they operate, are not only becoming more complex, but they are increasingly being connected together. Although each system may be interested in very different aspects of the environment, the geospatial data they use must be consistent and correlated with each other. As these "systems of systems" grow and as the amount of geospatial data gathered each day increases, the collection and preparation of these data has become the most time consuming and costly processes in the use of many of these systems. the solution to this problem lies in the creation of a geospatial dataset generation system that can efficiently collect, manipulate, and correlate geospatial data to the requirements of the end user. In this paper, we use the unique requirements of building geospatial datasets to derive a distributed software architecture for such a system.
Volunteer computing projects use donated CPU time to solve problems that would otherwise be too computationally intensive to solve. the donated CPU time comes from computers whose owners install a volunteer computing ...
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ISBN:
(纸本)9780889867741
Volunteer computing projects use donated CPU time to solve problems that would otherwise be too computationally intensive to solve. the donated CPU time comes from computers whose owners install a volunteer computing client program on their computer, allowing a project to use the computer's idle time. the low participation rate in volunteer computing and the increasing number of volunteer computing projects make improvements that more effectively use the donated CPU cycles very important. Past work showed that using certain task retrieval policies could increase the number of tasks volunteer computing clients complete. However, the past work assumed that the volunteered computers had a single CPU and the task retrieval methods that resulted in more completed tasks required the client to be connected to the Internet more often than the other policies. We simulated the task retrieval policies for computers with multi-core CPUs and found that in most cases, the multi-core architecture can lead to a slightly greater than linear increase in the number of tasks that the clients complete, relative to the number of cores the computer running the client has. Additionally, the multi-core architecture can reduce the performance gap between the best and worst performing policies significantly, affecting which policies are used.
We analyze the parallel processing in clusters of computers of a prediction method based on the improvement of Radial Basis Function (RBF) neural networks using matrix decomposition techniques such as the Singular Val...
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We analyze the parallel processing in clusters of computers of a prediction method based on the improvement of Radial Basis Function (RBF) neural networks using matrix decomposition techniques such as the Singular Value Decomposition (SVD) and the QR-cp factorization. parallel processing is required because of the extensive computation found in those techniques, but the reward is obtained in form of better prediction performance and less network complexity. this general prediction procedure (in the sequential version) was published in the technical literature previously, with a high degree of experimental success. parallelism is a convenient way to make this prediction module available for inexpensive operation within decision-making contexts. We discuss two alternatives of concurrency: parallel implementation of the prediction procedure over the ScaLAPACK suite, and the formulation of another parallel routine customized to a higher degree for better performance.
Most recent distributed file systems have adopted architecture with an independent metadata server cluster. However, potential multiple hotspots and flash crowds access patterns often cause a metadata service that vio...
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ISBN:
(纸本)9781479920815
Most recent distributed file systems have adopted architecture with an independent metadata server cluster. However, potential multiple hotspots and flash crowds access patterns often cause a metadata service that violates performance Service Level Objectives. To maximize the throughput of the metadata service, an adaptive request load balancing framework is critical. We present a distributed cache framework above the distributed metadata management schemes to manage hotspots rather than managing all metadata to achieve request load balancing. this benefits the metadata hierarchical locality and the system scalability. Compared with data, metadata has its own distinct characteristics, such as small size and large quantity. the cost of useless metadata prefetching is much less than data prefetching. In light of this, we devise a time period-based prefetching strategy and a perfecting-based adaptive replacement cache algorithm to improve the performance of the distributed caching layer to adapt constantly changing workloads. Finally, we evaluate our approach with a hadoop distributed file system cluster.
Writing efficient software for heterogeneous architectures equipped with modern accelerator devices presents a serious challenge to programmer productivity, creating a need for powerful performance-analysis tools to a...
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ISBN:
(纸本)9780769543284
Writing efficient software for heterogeneous architectures equipped with modern accelerator devices presents a serious challenge to programmer productivity, creating a need for powerful performance-analysis tools to adequately support the software development process. To guide the design of such tools, we describe typical patterns of inefficient runtime behavior that may adversely affect the performance of applications that use general-purpose processors along with GPU devices through a CUDA compute engine. To evaluate the general impact of these patterns on application performance, we further present a microbenchmark suite that allows the performance penalty of each pattern to be quantified with results obtained on NVIDIA Fermi and Tesla architectures, indeed demonstrating significant delays. Furthermore this suite can be used as a default test scenario to add CUDA support to performance-analysis tools used in high-performance computing.
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