In this paper a performance analysis in a cluster system of the parallel single front genetic algorithm (PSFGA) is carried out. The PSFGA is a parallel evolutionary optimizer for multiobjective problems that use a str...
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In this paper a performance analysis in a cluster system of the parallel single front genetic algorithm (PSFGA) is carried out. The PSFGA is a parallel evolutionary optimizer for multiobjective problems that use a structured population in the form of a set of islands. The SFGA, an elitist evolutionary algorithm with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes, is performed on each subpopulation (island) associated to a different area in the search space. Experimental results show that PSFGA outperforms SFGA and SPEA (strength Pareto evolutionary algorithm) in the cases studied.
Multiple hypothesis tracking addresses difficult multiple target tracking problems by making association decisions using multiple scans or frames of data. This paper reviews forty years of its development, including t...
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Multiple hypothesis tracking addresses difficult multiple target tracking problems by making association decisions using multiple scans or frames of data. This paper reviews forty years of its development, including the original measurement-oriented approach of Reid, track-oriented approach first formulated by Morefield, distributed processing, and recent graph-based approaches. It also discusses its relationship with random set approaches for tracking.
The InfiniBand architecture (IBA) is becoming an industry standard both for communication between processing nodes and I/O devices and for interprocessor communication. It replaces the traditional I/O bus with a switc...
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The InfiniBand architecture (IBA) is becoming an industry standard both for communication between processing nodes and I/O devices and for interprocessor communication. It replaces the traditional I/O bus with a switch-based interconnect for connecting processing nodes and I/O devices. It is being developed by the InfiniBand/sup SM/ Trade Association (IBTA) to provide the levels of reliability, availability, performance, scalability, and quality of service (QoS) necessary for present and future server systems. For this, IBA provides a series of mechanisms that are able to guarantee QoS to the applications. Alfaro, Sanchez and Das (see Proceedings of International Parallel and distributed processing Symposium, April 2002) proposed a strategy to compute the InfiniBand arbitration tables. We only evaluated our proposal for CBR traffic with fixed mean bandwidth requirements. We evaluate our strategy to compute the InfiniBand arbitration tables with VBR traffic. Performance results show that, this class of traffic also gets their QoS requirements.
GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in ...
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ISBN:
(数字)9781665497473
ISBN:
(纸本)9781665497480
GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.
Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations ...
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Summary form only given. This paper is motivated by the observation that different methods to compute the weights of nodes and edges when scheduling DAGs onto heterogeneous machines may lead to significant variations in the generated schedule. To minimize such variations, we present a novel heuristic for DAG scheduling, which is based upon solving a series of independent task scheduling problems. A novel heuristic for the latter problem is also included. Both heuristics compare favourably with other related heuristics.
SystemC has become a very popular standardized language for the modeling of system-on-chip (SoC) devices. However, due to the ever increasing complexity of SoC designs, the ever longer simulation times affect SoC expl...
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SystemC has become a very popular standardized language for the modeling of system-on-chip (SoC) devices. However, due to the ever increasing complexity of SoC designs, the ever longer simulation times affect SoC exploration potential and time-to-market. In order to reduce these times, we have developed a parallel SystemC kernel. Because the SystemC semantics require a high level of synchronization which can dramatically affect the performance gains, we investigate in this paper some ways to reduce the synchronization overheads. We validate then our approaches against an academic design model and a real, industrial application.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
ISBN:
(纸本)9781467376853
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Over the past several decades, the compiler research community has developed a number of sophisticated and powerful algorithms for a variety of code improvements. While there are still promising directions for particu...
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Over the past several decades, the compiler research community has developed a number of sophisticated and powerful algorithms for a variety of code improvements. While there are still promising directions for particular optimizations, research on new or improved optimizations is reaching the point of diminishing returns and new approaches are needed to achieve significant performance improvements beyond traditional optimizations. In this paper, we describe a new strategy based on a continuous compilation system that constantly improves application code by applying aggressive and adaptive code optimizations at all times, from static optimization to online dynamic optimization. In this paper, we describe our general approach and process for continuous compilation of application code. We also present initial results from our research with continuous compilation. These initial results include a new prediction framework that can estimate the benefit of applying code transformations without actually doing the transformation. We also describe results that demonstrate the benefit of adaptively changing application code for embedded systems to make trade-offs between code size, performance, and power consumption.
In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In th...
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ISBN:
(纸本)9781538637913
In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, “on-demand processing”, which tries to upload data analysis models and execute them “near” where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture; XML/JSON based runtime environment description; and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 ~ 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.
Summary form only given. Materializing the vision and the huge business opportunities offered by the Internet-of-Things requires a paradigm shift in sensor data processing, fusing, understanding. Centralized approache...
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Summary form only given. Materializing the vision and the huge business opportunities offered by the Internet-of-Things requires a paradigm shift in sensor data processing, fusing, understanding. Centralized approaches (sensors at the edges, with centralized intelligence in the cloud) are not scalable, hierarchical, distributed processing is a strong requirement. In this talk I will describe recent trends in the development of new computing platforms geared to distributed sensor data management, discuss design challenges and research opportunities.
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