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Michal Sumbera - One of the best experts on this subject based on the ideXlab platform.

  • using constraint programing to resolve the multi source multi site data movement paradigm on the grid
    Proceedings of XII Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT08), 2009
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer t o collaborative sites as well as to create a distribution of data over multiple sites, efficient data mo vement is one of the most essential aspects in distributed environment. With such capabilities a t hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. Constraint programming (CP) is a technique from artificial intelligence and operations research allowing to find solutions in a multi-dimensi onal space of variables. We present a method of creating a CP model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks a s part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution’s finding time by using search tree time limit, approximations , restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for e ach case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.

  • using constraint programming to resolve the multi source multi site data movement paradigm on the grid
    arXiv: Performance, 2008
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer to collaborative sites as well as to create a distribution of data over multiple sites, efficient data movement is one of the most essential aspects in distributed environment. With such capabilities at hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. We present a method of creating a Constraint Programming (CP) model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks as part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution's finding time by using search tree time limit, approximations, restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for each case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.

Brett Hyde - One of the best experts on this subject based on the ideXlab platform.

  • the odd parity Input Problem in metrical stress theory
    Phonology, 2012
    Co-Authors: Brett Hyde
    Abstract:

    Brett Hyde Phonology / Volume 29 / Issue 03 / December 2012, pp 383 ­ 431 DOI: 10.1017/S0952675712000218, Published online: 13 December 2012 Link to this article: http://journals.cambridge.org/abstract_S0952675712000218 How to cite this article: Brett Hyde (2012). The odd­parity Input Problem in metrical stress theory. Phonology, 29, pp 383­431 doi:10.1017/S0952675712000218 Request Permissions : Click here

  • the odd parity Input Problem in metrical stress theory
    Phonology, 2012
    Co-Authors: Brett Hyde
    Abstract:

    Under the weak layering approach to prosodic structure (Ito & Mester 1992), the requirement that output forms be exhaustively parsed into binary feet, even when the Input contains an odd-number of syllables, results in the odd-parity Input Problem, which consists of two sub-Problems. The odd heavy Problem is a pathological type of quantity-sensitivity where a single odd-numbered heavy syllable in an odd-parity output is parsed as a monosyllabic foot. The even output Problem is the systematic conversion of odd-parity Inputs to even-parity outputs. The article examines the typology of binary stress patterns predicted by two approaches, symmetrical alignment (McCarthy & Prince 1993) and iterative foot optimisation (Pruitt 2008, 2010), to demonstrate that the odd-parity Input Problem is pervasive in weak layering accounts. It then demonstrates that the odd-parity Input Problem can be avoided altogether under the alternative structural assumptions of weak bracketing (Hyde 2002).

Michal Zerola - One of the best experts on this subject based on the ideXlab platform.

  • using constraint programing to resolve the multi source multi site data movement paradigm on the grid
    Proceedings of XII Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT08), 2009
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer t o collaborative sites as well as to create a distribution of data over multiple sites, efficient data mo vement is one of the most essential aspects in distributed environment. With such capabilities a t hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. Constraint programming (CP) is a technique from artificial intelligence and operations research allowing to find solutions in a multi-dimensi onal space of variables. We present a method of creating a CP model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks a s part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution’s finding time by using search tree time limit, approximations , restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for e ach case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.

  • using constraint programming to resolve the multi source multi site data movement paradigm on the grid
    arXiv: Performance, 2008
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer to collaborative sites as well as to create a distribution of data over multiple sites, efficient data movement is one of the most essential aspects in distributed environment. With such capabilities at hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. We present a method of creating a Constraint Programming (CP) model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks as part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution's finding time by using search tree time limit, approximations, restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for each case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.

Benjamin Raichel - One of the best experts on this subject based on the ideXlab platform.

  • net and prune a linear time algorithm for euclidean distance Problems
    arXiv: Computational Geometry, 2014
    Co-Authors: Sariel Harpeled, Benjamin Raichel
    Abstract:

    We provide a general framework for getting expected linear time constant factor approximations (and in many cases FPTAS's) to several well known Problems in Computational Geometry, such as $k$-center clustering and farthest nearest neighbor. The new approach is robust to variations in the Input Problem, and yet it is simple, elegant and practical. In particular, many of these well studied Problems which fit easily into our framework, either previously had no linear time approximation algorithm, or required rather involved algorithms and analysis. A short list of the Problems we consider include farthest nearest neighbor, $k$-center clustering, smallest disk enclosing $k$ points, $k$th largest distance, $k$th smallest $m$-nearest neighbor distance, $k$th heaviest edge in the MST and other spanning forest type Problems, Problems involving upward closed set systems, and more. Finally, we show how to extend our framework such that the linear running time bound holds with high probability.

  • net and prune a linear time algorithm for euclidean distance Problems
    Symposium on the Theory of Computing, 2013
    Co-Authors: Sariel Harpeled, Benjamin Raichel
    Abstract:

    We provide a general framework for getting linear time constant factor approximations (and in many cases FPTAS's) to a copious amount of well known and well studied Problems in Computational Geometry, such as k-center clustering and furthest nearest neighbor. The new approach is robust to variations in the Input Problem, and yet it is simple, elegant and practical. In particular, many of these well studied Problems which fit easily into our framework, either previously had no linear time approximation algorithm, or required rather involved algorithms and analysis. A short list of the Problems we consider include furthest nearest neighbor, k-center clustering, smallest disk enclosing k points, k-th largest distance, k-th smallest m-nearest neighbor distance, k-th heaviest edge in the MST and other spanning forest type Problems, Problems involving upward closed set systems, and more. Finally, we show how to extend our framework such that the linear running time bound holds with high probability.

Roman Bartak - One of the best experts on this subject based on the ideXlab platform.

  • using constraint programing to resolve the multi source multi site data movement paradigm on the grid
    Proceedings of XII Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT08), 2009
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer t o collaborative sites as well as to create a distribution of data over multiple sites, efficient data mo vement is one of the most essential aspects in distributed environment. With such capabilities a t hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. Constraint programming (CP) is a technique from artificial intelligence and operations research allowing to find solutions in a multi-dimensi onal space of variables. We present a method of creating a CP model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks a s part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution’s finding time by using search tree time limit, approximations , restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for e ach case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.

  • using constraint programming to resolve the multi source multi site data movement paradigm on the grid
    arXiv: Performance, 2008
    Co-Authors: Michal Zerola, J Lauret, Roman Bartak, Michal Sumbera
    Abstract:

    In order to achieve both fast and coordinated data transfer to collaborative sites as well as to create a distribution of data over multiple sites, efficient data movement is one of the most essential aspects in distributed environment. With such capabilities at hand, truly distributed task scheduling with minimal latencies would be reachable by internationally distributed collaborations (such as ones in HENP) seeking for scavenging or maximizing on geographically spread computational resources. But it is often not all clear (a) how to move data when available from multiple sources or (b) how to move data to multiple compute resources to achieve an optimal usage of available resources. We present a method of creating a Constraint Programming (CP) model consisting of sites, links and their attributes such as bandwidth for grid network data transfer also considering user tasks as part of the objective function for an optimal solution. We will explore and explain trade-off between schedule generation time and divergence from the optimal solution and show how to improve and render viable the solution's finding time by using search tree time limit, approximations, restrictions such as symmetry breaking or grouping similar tasks together, or generating sequence of optimal schedules by splitting the Input Problem. Results of data transfer simulation for each case will also include a well known Peer-2-Peer model, and time taken to generate a schedule as well as time needed for a schedule execution will be compared to a CP optimal solution. We will additionally present a possible implementation aimed to bring a distributed datasets (multiple sources) to a given site in a minimal time.