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  • 1.
    Viebke, Andre
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Memeti, Suejb
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Pllana, Sabri
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Abraham, Ajith
    Machine Intelligence Research Labs (MIR Labs).
    CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi2017Ingår i: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning is an important component of big-data analytic tools and intelligent applications, such as, self-driving cars, computer vision, speech recognition, or precision medicine. However, the training process is computationally intensive, and often requires a large amount of time if performed sequentially. Modern parallel computing systems provide the capability to reduce the required training time of deep neural networks.In this paper, we present our parallelization scheme for training convolutional neural networks (CNN) named Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS). Major features of CHAOS include the support for thread and vector parallelism, non-instant updates of weight parameters during back-propagation without a significant delay, and implicit synchronization in arbitrary order. CHAOS is tailored for parallel computing systems that are accelerated with the Intel Xeon Phi. We evaluate our parallelization approach empirically using measurement techniques and performance modeling for various numbers of threads and CNN architectures. Experimental results for the MNIST dataset of handwritten digits using the total number of threads on the Xeon Phi show speedups of up to 103x compared to the execution on one thread of the Xeon Phi, 14x compared to the sequential execution on Intel Xeon E5, and 58x compared to the sequential execution on Intel Core i5.

  • 2.
    Memeti, Suejb
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Pllana, Sabri
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Combinatorial optimization of DNA sequence analysis on heterogeneous systems2017Ingår i: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 29, nr 7, e4037Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analysis of DNA sequences is a data and computational intensive problem, and therefore, it requires suitable parallel computing resources and algorithms. In this paper, we describe our parallel algorithm for DNA sequence analysis that determines how many times a pattern appears in the DNA sequence. The algorithm is engineered for heterogeneous platforms that comprise a host with multi-core processors and one or more many-core devices. For combinatorial optimization, we use the simulated annealing algorithm. The optimization goal is to determine the number of threads, thread affinities, and DNA sequence fractions for host and device, such that the overall execution time of DNA sequence analysis is minimized. We evaluate our approach experimentally using real-world DNA sequences of various organisms on a heterogeneous platform that comprises two Intel Xeon E5 processors and an Intel Xeon Phi 7120P co-processing device. By running only about 5% of possible experiments, our optimization method finds a near-optimal system configuration for DNA sequence analysis that yields with average speedup of 1.6 ×  and 2 ×  compared with the host-only and device-only execution.

  • 3.
    Gargano, Francesco
    et al.
    University of Palermo, Italy.
    Tamburino, Lucia
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för matematik (MA).
    Bagarello, Fabio
    University of Palermo, Italy ; National Institute for Nuclear Physics (INFN), Napoli, Italy.
    Bravo, Giangiacomo
    Linnéuniversitetet, Fakulteten för samhällsvetenskap (FSV), Institutionen för samhällsstudier (SS).
    Large-scale effects of migration and conflict in pre-agricultural groups: Insights from a dynamic model2017Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, nr 3, e0172262Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The debate on the causes of conflict in human societies has deep roots. In particular, the extent of conflict in hunter-gatherer groups remains unclear. Some authors suggest that large-scale violence only arose with the spreading of agriculture and the building of complex societies. To shed light on this issue, we developed a model based on operatorial techniques simulating population-resource dynamics within a two-dimensional lattice, with humans and natural resources interacting in each cell of the lattice. The model outcomes under different conditions were compared with recently available demographic data for prehistoric South America. Only under conditions that include migration among cells and conflict was the model able to consistently reproduce the empirical data at a continental scale. We argue that the interplay between resource competition, migration, and conflict drove the population dynamics of South America after the colonization phase and before the introduction of agriculture. The relation between population and resources indeed emerged as a key factor leading to migration and conflict once the carrying capacity of the environment has been reached.

  • 4.
    Ghorbani, Amineh
    et al.
    Delft University of Technology, The Netherlands.
    Bravo, Giangiacomo
    Linnéuniversitetet, Fakulteten för samhällsvetenskap (FSV), Institutionen för samhällsstudier (SS).
    Frey, Ulrich
    German Aerospace Center (DLR), Germany.
    Theesfeld, Insa
    Martin Luther University Halle-Wittenberg, Germany.
    Self-organization in the commons: An empirically-tested model2017Ingår i: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 96, 30-45 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A appropriate bottom-up rule system can support the sustainability of common-pool resources such as forests and fisheries. The process that leads to the developments of such institutional settings requires the considerations of multiple social, physical, and institutional factors over long time horizons. In this paper, we present the SONICOM model as a general exploratory model of CPR systems. The model can be configured to represent different CPR systems in order to explore what kind of institutional settings result in stable systems, i.e. situations where the resource and the appropriators are in a state of well-being. We use a large-N-dataset of CPR management institutions to validate the model. The results show numerous correlations between various parameters of the system such as rule compliance, social influence and resource growth rate which help explaining the process of institutional emergence as well as unveiling the conditions under which systems are stable.

  • 5.
    Memeti, Suejb
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    Pllana, Sabri
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV).
    A machine learning approach for accelerating DNA sequence analysis2016Ingår i: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The DNA sequence analysis is a data and computationally intensive problem and therefore demands suitable parallel computing resources and algorithms. In this paper, we describe an optimized approach for DNA sequence analysis on a heterogeneous platform that is accelerated with the Intel Xeon Phi. Such platforms commonly comprise one or two general purpose host central processing units (CPUs) and one or more Xeon Phi devices. We present a parallel algorithm that shares the work of DNA sequence analysis between the host CPUs and the Xeon Phi device to reduce the overall analysis time. For automatic worksharing we use a supervised machine learning approach, which predicts the performance of DNA sequence analysis on the host and device and accordingly maps fractions of the DNA sequence to the host and device. We evaluate our approach empirically using real-world DNA segments for human and various animals on a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P device with 61 cores.

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