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  • 1.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dato, D.
    Tiscali S.p.A., Cagliari, Italy.
    Lucchese, C.
    ISTI-CNR, Pisa, Italy.
    Mori, M.
    Tiscali S.p.A., Cagliari, Italy.
    Nardini, F. M.
    ISTI-CNR, Pisa, Italy.
    Orlando, S.
    University Ca' Foscari of Venice, Italy.
    Perego, R.
    ISTI-CNR, Pisa, Italy.
    Tonellotto, N.
    ISTI-CNR, Pisa, Italy.
    QuickRank: A C++ suite of learning to rank algorithms2015In: CEUR Workshop Proceedings, 2015, Vol. 1404Conference paper (Refereed)
    Abstract [en]

    Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and effciency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of effcient and effective Learning to Rank (LtR) algorithms that allows high-quality ranking functions to be devised from possibly huge training datasets. QuickRank is a project with a double goal: i) answering industrial need of Tiscali S.p.A. for a exible and scalable LtR solution for learning ranking models from huge training datasets; ii) providing the IR research community with a exible, extensible and effcient LtR framework to design LtR solutions and fairly compare the performance of different algorithms and ranking models. This paper presents our choices in designing QuickRank and report some preliminary use experiences.

  • 2.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Larsson, Thomas B
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Collision Culling for Large-Scale Simulations by a Parallel Sweep and Prune Algorithm2016In: Proceedings of the 16th Eurographics Symposium on Parallel Graphics and Visualization EGPGV 2016, Groningen, Netherlands: Eurographics Association , 2016, p. 1-10Conference paper (Refereed)
  • 3.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Larsson, Thomas B
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Collision Culling for Massive Simulations by a Parallel and Context-Aware Sweep and Prune Algorithm2018In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 4, no 7, p. 2064-2077Article in journal (Refereed)
    Abstract [en]

    We present an improved parallel Sweep and Prune algorithm that solves the dynamic box intersection problem in three dimensions. It scales up to very large datasets, which makes it suitable for broad phase collision detection in complex moving body simulations. Our algorithm gracefully handles high-density scenarios, including challenging clustering behavior, by using a double-axis sweeping approach and a cache-friendly succinct data structure. The algorithm is realized by three parallel stages for sorting, candidate generation, and object pairing. By the use of temporal coherence, our sorting stage runs with close to optimal load balancing. Furthermore, our approach is characterized by a work-division strategy that relies on adaptive partitioning, which leads to almost ideal scalability. In addition, for scenarios that involves intense clustering along several axes simultaneously, we propose an enhancement that increases the context-awareness of the algorithm. By exploiting information gathered along three orthogonal axes, an efficient choice of what range query to perform can be made per object during run-time. Experimental results show high performance for up to millions of objects on modern multi-core CPUs.

  • 4.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Larsson, Thomas B
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Efficient Collision Culling by a Succinct Bi-dimensional Sweep and Prune Algorithm2016In: Proceedings of the 32nd Spring Conference on Computer Graphics, 2016, p. 25-32Conference paper (Refereed)
  • 5.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Larsson, Thomas B
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Output Sensitive Collision Detection for Unisize Boxes2016In: Proceedings of SIGRAD 2016, Visby, Sweden, 2016, p. 22-27Conference paper (Refereed)
  • 6.
    Capannini, Gabriele
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lucchese, C.
    Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of the National Research Council of Italy (CNR), Pisa, Italy.
    Nardini, F. M.
    Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of the National Research Council of Italy (CNR), Pisa, Italy.
    Orlando, S.
    University Ca’ Foscari of Venice, Italy.
    Perego, R.
    Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of the National Research Council of Italy (CNR), Pisa, Italy.
    Tonellotto, N.
    Istituto di Scienza e Tecnologie dell'Informazione (ISTI) of the National Research Council of Italy (CNR), Pisa, Italy.
    Quality versus efficiency in document scoring with learning-to-rank models2016In: Information Processing & Management, ISSN 0306-4573, E-ISSN 1873-5371, Vol. 52, no 6, p. 1161-1177Article in journal (Refereed)
    Abstract [en]

    Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far. The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features. We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (Λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (Ωλ-MART), an algorithm that induces forests of oblivious regression trees. We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget.

  • 7.
    Larsson, Thomas B
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Capannini, Gabriele
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Källberg, Linus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Parallel computation of optimal enclosing balls by iterative orthant scan2016In: Computers & graphics, ISSN 0097-8493, E-ISSN 1873-7684, Vol. 56, p. 1-10Article in journal (Refereed)
    Abstract [en]

    We propose an algorithm for computing the exact minimum enclosing ball of large point sets in general dimensions. It aims to reduce the number of passes by retrieving a well-balanced set of outliers in each linear search through the input by decomposing the space into orthants. The experimental evidence indicates that the convergence rate in terms of the required number of linear passes is superior compared to previous exact methods, and substantially faster execution times are observed in dimensions d≤16. In the important three-dimensional case, the execution times indicate real-time performance. Furthermore, we show how the algorithm can be adapted for parallel execution on both CPU and GPU architectures using OpenMP, AVX, and CUDA. For large datasets, our CUDA solution is superior. For example, the benchmark results show that optimal bounding spheres for inputs with tens of millions of points can be computed in just a few milliseconds.

1 - 7 of 7
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