The Experts below are selected from a list of 37521 Experts worldwide ranked by ideXlab platform

Kenny Adamson - One of the best experts on this subject based on the ideXlab platform.

  • SAC - Hierarchical model-based clustering of relational Data with aggregates
    Proceedings of the 2004 ACM symposium on Applied computing - SAC '04, 2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    This paper proposes a propositional method for hierarchical model-based clustering of relational Data. We define a new Type of aggregate -- frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log-likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further relocation clustering, in which Bayes Information Crieterion is used to determine the optimal number of clusters.

  • POSTER ABSTRACT Hierarchical Model-Based Clustering of Relational Data with Aggregates
    2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    paper proposes a propositional method for hierarchical model-based clustering of relational Data. We deflne a new Type of aggregate { frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log- likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further reloca- tion clustering, in which Bayes Information Criterion is used to determine the optimal number of clusters.

Sergei Gorlatch - One of the best experts on this subject based on the ideXlab platform.

  • SoMeT - A High-Level Programming Approach for Distributed Systems with Accelerators
    2012
    Co-Authors: Michel Steuwer, Philipp Kegel, Sergei Gorlatch
    Abstract:

    Application programming for modern heterogeneous systems which comprise multiple accelerators (multi-core CPUs and GPUs) is complex and error-prone. Popular approaches, like OpenCL and CUDA, are low-level and offer no support for the two most complicated issues: 1) programming multiple GPUs within a stand-alone computer, and 2) managing distributed systems that integrate several such computers. In particular, distributed systems require application developers to use a mix of different programming models, e.g., MPI together with OpenCL or CUDA. We propose a uniform approach based on OpenCL for programming both stand-alone and distributed systems with GPUs. The approach implementation is based on two parts: 1) the SkelCL library for high-level application programming on heterogeneous stand-alone computers with multi-core CPUs and multiple GPUs, and 2) the dOpenCL middleware for transparent execution of OpenCL programs on several stand-alone computers connected over a network. Both SkelCL and dOpenCL are built on top of the OpenCL standard which ensures their high portability across different kinds of processors and GPUs. The dOpenCL middleware extends OpenCL, such that arbitrary computing devices (multi-core CPUs and GPUs) in a distributed system can be used within a single application, with Data and program code moved to these devices transparently. The SkelCL library offers a set of pre-implemented patterns (skeletons) of parallel computation and communication which greatly simplify programming for multi-GPU systems. The library also provides an abstract Vector Data Type and a high-level Data (re)distribution mechanism to shield the programmer from the low-level Data transfers between a system's main memory and multiple GPUs. This paper describes dOpenCL and SkelCL and illustrates how they are used to simplify programming of heterogeneous distributed systems with accelerators.

  • IPDPS Workshops - Towards High-Level Programming of Multi-GPU Systems Using the SkelCL Library
    2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2012
    Co-Authors: Michel Steuwer, Philipp Kegel, Sergei Gorlatch
    Abstract:

    Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches - CUDA and OpenCL - are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library presented in this paper is built on top of the OpenCL standard and offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for multi-GPU systems. The library also provides an abstract Vector Data Type and a high-level Data (re)distribution mechanism to shield the programmer from the low-level Data transfers between the system's main memory and multiple GPUs. In this paper, we focus on the specific support in SkelCL for systems with multiple GPUs and use a real-world application study from the area of medical imaging to demonstrate the reduced programming effort and competitive performance of SkelCL as compared to OpenCL and CUDA. Besides, we illustrate how SkelCL adapts to large-scale, distributed heterogeneous systems in order to simplify their programming.

  • IPDPS Workshops - SkelCL - A Portable Skeleton Library for High-Level GPU Programming
    2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, 2011
    Co-Authors: Michel Steuwer, Philipp Kegel, Sergei Gorlatch
    Abstract:

    While CUDA and OpenCL made general-purpose programming for Graphics Processing Units (GPU) popular, using these programming approaches remains complex and error-prone because they lack high-level abstractions. The especially challenging systems with multiple GPU are not addressed at all by these low-level programming models. We propose SkelCL -- a library providing so-called algorithmic skeletons that capture recurring patterns of parallel computation and communication, together with an abstract Vector Data Type and constructs for specifying Data distribution. We demonstrate that SkelCL greatly simplifies programming GPU systems. We report the competitive performance results of SkelCL using both a simple Mandelbrot set computation and an industrial-strength medical imaging application. Because the library is implemented using OpenCL, it is portable across GPU hardware of different vendors.

Jianzhong Chen - One of the best experts on this subject based on the ideXlab platform.

  • SAC - Hierarchical model-based clustering of relational Data with aggregates
    Proceedings of the 2004 ACM symposium on Applied computing - SAC '04, 2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    This paper proposes a propositional method for hierarchical model-based clustering of relational Data. We define a new Type of aggregate -- frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log-likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further relocation clustering, in which Bayes Information Crieterion is used to determine the optimal number of clusters.

  • POSTER ABSTRACT Hierarchical Model-Based Clustering of Relational Data with Aggregates
    2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    paper proposes a propositional method for hierarchical model-based clustering of relational Data. We deflne a new Type of aggregate { frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log- likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further reloca- tion clustering, in which Bayes Information Criterion is used to determine the optimal number of clusters.

Mary Shapcott - One of the best experts on this subject based on the ideXlab platform.

  • SAC - Hierarchical model-based clustering of relational Data with aggregates
    Proceedings of the 2004 ACM symposium on Applied computing - SAC '04, 2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    This paper proposes a propositional method for hierarchical model-based clustering of relational Data. We define a new Type of aggregate -- frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log-likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further relocation clustering, in which Bayes Information Crieterion is used to determine the optimal number of clusters.

  • POSTER ABSTRACT Hierarchical Model-Based Clustering of Relational Data with Aggregates
    2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    paper proposes a propositional method for hierarchical model-based clustering of relational Data. We deflne a new Type of aggregate { frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log- likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further reloca- tion clustering, in which Bayes Information Criterion is used to determine the optimal number of clusters.

Sally Mcclean - One of the best experts on this subject based on the ideXlab platform.

  • SAC - Hierarchical model-based clustering of relational Data with aggregates
    Proceedings of the 2004 ACM symposium on Applied computing - SAC '04, 2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    This paper proposes a propositional method for hierarchical model-based clustering of relational Data. We define a new Type of aggregate -- frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log-likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further relocation clustering, in which Bayes Information Crieterion is used to determine the optimal number of clusters.

  • POSTER ABSTRACT Hierarchical Model-Based Clustering of Relational Data with Aggregates
    2004
    Co-Authors: Jianzhong Chen, Mary Shapcott, Sally Mcclean, Kenny Adamson
    Abstract:

    paper proposes a propositional method for hierarchical model-based clustering of relational Data. We deflne a new Type of aggregate { frequency aggregate, which has a Vector Data Type and can be used to record not only the observed values but also the distribution of the values of an attribute. A hierarchical agglomerative clustering algorithm with log- likelihood distance is then applied to cluster the aggregated Data tentatively, and a mixture model-based method with the EM algorithm is developed to perform a further reloca- tion clustering, in which Bayes Information Criterion is used to determine the optimal number of clusters.