1,959 research outputs found

    Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

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    Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads

    Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

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    Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted

    HPC-enabling technologies for high-fidelity combustion simulations

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    With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. }The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CoEC project, grant agreement No. 952181 and the CoE RAISE project grant agreement no. 951733.Peer ReviewedPostprint (published version

    High variability of perezone content in rhizomes of Acourtia cordata wild plants, environmental factors related, and proteomic analysis

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    With the aim of exploring the source of the high variability observed in the production of perezone, in Acourtia cordata wild plants, we analyze the influence of soil parameters and phenotypic characteristics on its perezone content. Perezone is a sesquiterpene quinone responsible for several pharmacological effects and the A. cordata plants are the natural source of this metabolite. The chemistry of perezone has been widely studied, however, no studies exist related to its production under natural conditions, nor to its biosynthesis and the environmental factors that affect the yield of this compound in wild plants. We also used a proteomic approach to detect differentially expressed proteins in wild plant rhizomes and compare the profiles of high vs. low perezone-producing plants. Our results show that in perezone-producing rhizomes, the presence of high concentrations of this compound could result from a positive response to the effects of some edaphic factors, such as total phosphorus (Pt), total nitrogen (Nt), ammonium (NH4), and organic matter (O. M.), but could also be due to a negative response to the soil pH value. Additionally, we identified 616 differentially expressed proteins between high and low perezone producers. According to the functional annotation of this comparison, the upregulated proteins were grouped in valine biosynthesis, breakdown of leucine and isoleucine, and secondary metabolism such as terpenoid biosynthesis. Downregulated proteins were grouped in basal metabolism processes, such as pyruvate and purine metabolism and glycolysis/gluconeogenesis. Our results suggest that soil parameters can impact the content of perezone in wild plants. Furthermore, we used proteomic resources to obtain data on the pathways expressed when A. cordata plants produce high and low concentrations of perezone. These data may be useful to further explore the possible relationship between perezone production and abiotic or biotic factors and the molecular mechanisms related to high and low perezone production.This work was supported by the Programa de Mejoramiento del Profesorado PROMEP/103.5/13/6626 and Consejo Nacional de Ciencia y Tecnología CONACyT-Mexico for Ph.D. scholarship 392123/254165. The University of Alicante lab is a member of Proteored, PRB3 and is supported by grant PT17/0019, of the PE I+D+I 2013-2016, funded by ISCIII and ERDF. Roque Bru-Martínez received financial support from the University of Alicante (VIGROB-105)

    On the design of architecture-aware algorithms for emerging applications

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    This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.Ph.D.Committee Chair: Bader, David; Committee Member: Hong, Bo; Committee Member: Riley, George; Committee Member: Vuduc, Richard; Committee Member: Wills, Scot

    Hardware, Software, Humans: Truth, Fiction and Abstraction

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