The Experts below are selected from a list of 38208 Experts worldwide ranked by ideXlab platform

Petros Koumoutsakos - One of the best experts on this subject based on the ideXlab platform.

  • korali efficient and scalable Software framework for bayesian uncertainty quantification and stochastic optimization
    Computer Methods in Applied Mechanics and Engineering, 2021
    Co-Authors: Sergio M Martin, Petr Karnakov, Petros Koumoutsakos, Daniel Walchli, Georgios Arampatzis, Athena Economides
    Abstract:

    Abstract We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance Software such as Aphros , LAMMPS (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art Software Frameworks.

  • a hybrid particle volume of fluid method for curvature estimation in multiphase flows
    International Journal of Multiphase Flow, 2020
    Co-Authors: Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos
    Abstract:

    Abstract We present a particle method for estimating the curvature of interfaces in volume-of-fluid simulations of multiphase flows. The method is well suited for under-resolved interfaces, and it is shown to be more accurate than the parabolic fitting that is employed in such cases. The curvature is computed from the equilibrium positions of particles constrained to circular arcs and attracted to the interface. The proposed particle method is combined with the method of height functions at higher resolutions, and it is shown to outperform the current combinations of height functions and parabolic fitting. The algorithm is conceptually simple and straightforward to implement on new and existing Software Frameworks for multiphase flow simulations thus enhancing their capabilities in challenging flow problems. We evaluate the proposed hybrid method on a number of two- and three-dimensional benchmark flow problems and illustrate its capabilities on simulations of flows involving bubble coalescence and turbulent multiphase flows.

Petr Karnakov - One of the best experts on this subject based on the ideXlab platform.

  • korali efficient and scalable Software framework for bayesian uncertainty quantification and stochastic optimization
    Computer Methods in Applied Mechanics and Engineering, 2021
    Co-Authors: Sergio M Martin, Petr Karnakov, Petros Koumoutsakos, Daniel Walchli, Georgios Arampatzis, Athena Economides
    Abstract:

    Abstract We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance Software such as Aphros , LAMMPS (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art Software Frameworks.

  • a hybrid particle volume of fluid method for curvature estimation in multiphase flows
    International Journal of Multiphase Flow, 2020
    Co-Authors: Petr Karnakov, Sergey Litvinov, Petros Koumoutsakos
    Abstract:

    Abstract We present a particle method for estimating the curvature of interfaces in volume-of-fluid simulations of multiphase flows. The method is well suited for under-resolved interfaces, and it is shown to be more accurate than the parabolic fitting that is employed in such cases. The curvature is computed from the equilibrium positions of particles constrained to circular arcs and attracted to the interface. The proposed particle method is combined with the method of height functions at higher resolutions, and it is shown to outperform the current combinations of height functions and parabolic fitting. The algorithm is conceptually simple and straightforward to implement on new and existing Software Frameworks for multiphase flow simulations thus enhancing their capabilities in challenging flow problems. We evaluate the proposed hybrid method on a number of two- and three-dimensional benchmark flow problems and illustrate its capabilities on simulations of flows involving bubble coalescence and turbulent multiphase flows.

Yihua Huang - One of the best experts on this subject based on the ideXlab platform.

  • unified programming model and Software framework for big data machine learning and data analytics
    Computer Software and Applications Conference, 2015
    Co-Authors: Yun Tang, Qianhao Dong, Zhaokang Wang, Zhiqiang Liu, Shuai Wang, Chunfeng Yuan, Yihua Huang
    Abstract:

    In a new era of Big Data, the rapid growth of the applications, such as social media and web-search, requires efficient and scalable machine learning and statistical analytical algorithms. However, there lacks easy-to-use and efficient Software Frameworks or systems that can support fast development of such big data analytical algorithms. To solve these problems, we propose Octopus, an easy-to-use and efficient analytical system for big data. Octopus allows data analysts conduct complex data analytics for big data with traditional programming languages and methods in an easy and efficient way. To achieve the goal of ease-to-use, we propose a matrix-based unified programming model, which is the core of many data-intensive statistical applications such as numerical analysis and data mining. Further, in order to improve the performance, the Octopus Software framework adopts various distributed computing platforms, including Hadoop MapReduce, Spark and MPI. On these computing platforms, we design several parallel matrix computation algorithms, which are suitable for various scenarios. Finally, the features of Octopus are encapsulated into a library with matrix-based APIs and exposed to users as an R package. R is a widely-used statistical programming language and supports diversified data analysis tasks through extension packages. Experimental results show that Octopus achieves efficient performance and near linear scalability.

Davide Brugali - One of the best experts on this subject based on the ideXlab platform.

  • the brics component model a model based development paradigm for complex robotics Software systems
    ACM Symposium on Applied Computing, 2013
    Co-Authors: Herman Bruyninckx, Markus Klotzbucher, Nico Hochgeschwender, Gerhard K Kraetzschmar, Luca Gherardi, Davide Brugali
    Abstract:

    Because robotic systems get more complex all the time, developers around the world have, during the last decade, created component-based Software Frameworks (Orocos, Open-RTM, ROS, OPRoS, SmartSoft) to support the development and reuse of "large grained" pieces of robotics Software. This paper introduces the BRICS Component Model (BCM) to provide robotics developers with a set of guidelines, metamodels and tools for structuring as much as possible the development of, both, individual components and component-based architectures, using one or more of the aforementioned Software Frameworks at the same time, without introducing any framework- or application-specific details. The BCM is built upon two complementary paradigms: the "5Cs" (separation of concerns between the development aspects of Computation, Communication, Coordination, Configuration and Composition) and the meta-modeling approach from Model-Driven Engineering.

Rodríguez-vázquez Ángel - One of the best experts on this subject based on the ideXlab platform.

  • Performance assessment of deep learning Frameworks through metrics of CPU hardware exploitation on an embedded platform
    FERIT, 2020
    Co-Authors: Velasco-montero D., Fernández-berni J., Carmona-galán R., Rodríguez-vázquez Ángel
    Abstract:

    In this paper, we analyze heterogeneous performance exhibited by some popular deep learning Software Frameworks for visual inference on a resource-constrained hardware platform. Benchmarking of Caffe, OpenCV, TensorFlow, and Caffe2 is performed on the same set of convolutional neural networks in terms of instantaneous throughput, power consumption, memory footprint, and CPU utilization. To understand the resulting dissimilar behavior, we thoroughly examine how the resources in the processor are differently exploited by these Frameworks. We demonstrate that a strong correlation exists between hardware events occurring in the processor and inference performance. The proposedhardware-aware analysis aims to findlimitations andbottlenecks emerging from the jointinteraction ofFrameworks andnetworks on a particular CPU-based platform. This provides insight into introducing suitable modifications in bothtypes of components to enhance their global performance. It also facilitates the selection of Frameworks and networks among a large diversity of these components available these days for visual understanding

  • On the Correlation of CNN Performance and Hardware Metrics for Visual Inference on a Low-Cost CPU-based Platform
    'Institute of Electrical and Electronics Engineers (IEEE)', 2019
    Co-Authors: Velasco-montero Delia, Fernández-berni J., Carmona-galán R., Rodríguez-vázquez Ángel
    Abstract:

    26th IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Osijek, Croatia, June 2019.While providing the same functionality, the various Deep Learning Software Frameworks available these days do not provide similar performance when running the same network model on a particular hardware platform. On the contrary, we show that the different coding techniques and underlying acceleration libraries have a great impact on the instantaneous throughput and CPU utilization when carrying out the same inference with Caffe, OpenCV, TensorFlow and Caffe2 on an ARM Cortex-A53 multi-core processor. Direct modelling of this dissimilar performance is not practical, mainly because of the complexity and rapid evolution of the toolchains. Alternatively, we examine how the hardware resources are distinctly exploited by the Frameworks. We demonstrate that there is a strong correlation between inference performance – including power consumption – and critical parameters associated with memory usage and instruction flow control. This identified correlation is a preliminary step for the development of a simple empirical model. The objective is to facilitate selection and further performance tuning among the ever-growing zoo of deep neural networks and Frameworks, as well as the exploration of new network architectures.Peer reviewe