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  • 1. Chen, Yuxi
    et al.
    Toth, Gabor
    Cassak, Paul
    Jia, Xianzhe
    Gombosi, Tamas I.
    Slavin, James A.
    Markidis, Stefano
    KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Peng, Ivy Bo
    KTH.
    Jordanova, Vania K.
    Henderson, Michael G.
    Global Three-Dimensional Simulation of Earth's Dayside Reconnection Using a Two-Way Coupled Magnetohydrodynamics With Embedded Particle-in-Cell Model: Initial Results2017In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 122, no 10, p. 10318-10335Article in journal (Refereed)
    Abstract [en]

    We perform a three-dimensional (3-D) global simulation of Earth's magnetosphere with kinetic reconnection physics to study the flux transfer events (FTEs) and dayside magnetic reconnection with the recently developed magnetohydrodynamics with embedded particle-in-cell model. During the 1 h long simulation, the FTEs are generated quasi-periodically near the subsolar point and move toward the poles. We find that the magnetic field signature of FTEs at their early formation stage is similar to a "crater FTE," which is characterized by a magnetic field strength dip at the FTE center. After the FTE core field grows to a significant value, it becomes an FTE with typical flux rope structure. When an FTE moves across the cusp, reconnection between the FTE field lines and the cusp field lines can dissipate the FTE. The kinetic features are also captured by our model. A crescent electron phase space distribution is found near the reconnection site. A similar distribution is found for ions at the location where the Larmor electric field appears. The lower hybrid drift instability (LHDI) along the current sheet direction also arises at the interface of magnetosheath and magnetosphere plasma. The LHDI electric field is about 8 mV/m, and its dominant wavelength relative to the electron gyroradius agrees reasonably with Magnetospheric Multiscale (MMS) observations.

  • 2.
    Chien, Steven Wei Der
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Sishtla, Chaitanya Prasad
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Jun, Zhang
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems2018In: Proceedings of the 5th International Conference on Exascale Applications and Software, The University of Edinburgh , 2018, p. 34-Conference paper (Refereed)
    Abstract [en]

    Computational intensive applications such as pattern recognition, and natural language processing, are increasingly popular on HPC systems. Many of these applications use deep-learning, a branch of machine learning, to determine the weights of artificial neural network nodes by minimizing a loss function. Such applications depend heavily on dense matrix multiplications, also called tensorial operations. The use of Graphics Processing Unit (GPU) has considerably speeded up deep-learning computations, leading to a Renaissance of the artificial neural network. Recently, the NVIDIA Volta GPU and the Google Tensor Processing Unit (TPU) have been specially designed to support deep-learning workloads. New programming models have also emerged for convenient expression of tensorial operations and deep-learning computational paradigms. An example of such new programming frameworks is TensorFlow, an open-source deep-learning library released by Google in 2015. TensorFlow expresses algorithms as a computational graph where nodes represent operations and edges between nodes represent data flow. Multi-dimensional data such as vectors and matrices which flows between operations are called Tensors. For this reason, computation problems need to be expressed as a computational graph. In particular, TensorFlow supports distributed computation with flexible assignment of operation and data to devices such as GPU and CPU on different computing nodes. Computation on devices are based on optimized kernels such as MKL, Eigen and cuBLAS. Inter-node communication can be through TCP and RDMA. This work attempts to evaluate the usability and expressiveness of the TensorFlow programming model for traditional HPC problems. As an illustration, we prototyped a distributed block matrix multiplication for large dense matrices which cannot be co-located on a single device and a Conjugate Gradient (CG) solver. We evaluate the difficulty of expressing traditional HPC algorithms using computational graphs and study the scalability of distributed TensorFlow on accelerated systems. Our preliminary result with distributed matrix multiplication shows that distributed computation on TensorFlow is extremely scalable. This study provides an initial investigation of new emerging programming models for HPC.

  • 3.
    Ma, Yingjuan
    et al.
    Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA..
    Russell, Christopher T.
    Univ Calif Los Angeles, Dept Earth Planetary & Space Sci, Los Angeles, CA 90095 USA..
    Toth, Gabor
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Chen, Yuxi
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Nagy, Andrew F.
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Harada, Yuki
    Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA..
    McFadden, James
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Halekas, Jasper S.
    Univ Iowa, Dept Phys & Astron, Iowa City, IA 52242 USA..
    Lillis, Rob
    Univ Calif Berkeley, Space Sci Lab, Berkeley, CA 94720 USA..
    Connerney, John E. P.
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    Espley, Jared
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    DiBraccio, Gina A.
    NASA, Goddard Space Flight Ctr, Greenbelt, MD USA..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Fang, Xiaohua
    Univ Colorado, Lab Atmospher & Space Phys, Boulder, CO 80309 USA..
    Jakosky, Bruce M.
    Univ Colorado, Lab Atmospher & Space Phys, Boulder, CO 80309 USA..
    Reconnection in the Martian Magnetotail: Hall-MHD With Embedded Particle-in-Cell Simulations2018In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 123, no 5, p. 3742-3763Article in journal (Refereed)
    Abstract [en]

    Mars Atmosphere and Volatile EvolutioN (MAVEN) mission observations show clear evidence of the occurrence of the magnetic reconnection process in the Martian plasma tail. In this study, we use sophisticated numerical models to help us understand the effects of magnetic reconnection in the plasma tail. The numerical models used in this study are (a) a multispecies global Hall-magnetohydrodynamic (HMHD) model and (b) a global HMHD model two-way coupled to an embedded fully kinetic particle-in-cell code. Comparison with MAVEN observations clearly shows that the general interaction pattern is well reproduced by the global HMHD model. The coupled model takes advantage of both the efficiency of the MHD model and the ability to incorporate kinetic processes of the particle-in-cell model, making it feasible to conduct kinetic simulations for Mars under realistic solar wind conditions for the first time. Results from the coupled model show that the Martian magnetotail is highly dynamic due to magnetic reconnection, and the resulting Mars-ward plasma flow velocities are significantly higher for the lighter ion fluid, which are quantitatively consistent with MAVEN observations. The HMHD with Embedded Particle-in-Cell model predicts that the ion loss rates are more variable but with similar mean values as compared with HMHD model results.

  • 4. Narasimhamurthy, S.
    et al.
    Danilov, N.
    Wu, S.
    Umanesan, G.
    Chien, Steven Wei Der
    KTH.
    Rivas-Gomez, Sergio
    KTH.
    Peng, Ivy Bo
    KTH.
    Laure, Erwin
    KTH.
    De Witt, S.
    Pleiter, D.
    Markidis, Stefano
    KTH.
    The SAGE project: A storage centric approach for exascale computing2018In: 2018 ACM International Conference on Computing Frontiers, CF 2018 - Proceedings, Association for Computing Machinery (ACM), 2018, p. 287-292Conference paper (Refereed)
    Abstract [en]

    SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a storage centric approach as it is capable of storing and processing large data volumes at the Exascale regime. SAGE addresses the convergence of Big Data Analysis and HPC in an era of next-generation data centric computing. This convergence is driven by the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors where data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. A first prototype of the SAGE system has been been implemented and installed at the Jülich Supercomputing Center. The SAGE storage system consists of multiple types of storage device technologies in a multi-tier I/O hierarchy, including flash, disk, and non-volatile memory technologies. The main SAGE software component is the Seagate Mero Object Storage that is accessible via the Clovis API and higher level interfaces. The SAGE project also includes scientific applications for the validation of the SAGE concepts. The objective of this paper is to present the SAGE project concepts, the prototype of the SAGE platform and discuss the software architecture of the SAGE system.

  • 5.
    Narasimhamurthy, Sai
    et al.
    Seagate Syst UK, London, England..
    Danilov, Nikita
    Seagate Syst UK, London, England..
    Wu, Sining
    Seagate Syst UK, London, England..
    Umanesan, Ganesan
    Seagate Syst UK, London, England..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Rivas-Gomez, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Pleiter, Dirk
    Julich Supercomp Ctr, Julich, Germany..
    de Witt, Shaun
    Culham Ctr Fus Energy, Abingdon, Oxon, England..
    SAGE: Percipient Storage for Exascale Data Centric Computing2019In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 83, p. 22-33Article in journal (Refereed)
    Abstract [en]

    We aim to implement a Big Data/Extreme Computing (BDEC) capable system infrastructure as we head towards the era of Exascale computing - termed SAGE (Percipient StorAGe for Exascale Data Centric Computing). The SAGE system will be capable of storing and processing immense volumes of data at the Exascale regime, and provide the capability for Exascale class applications to use such a storage infrastructure. SAGE addresses the increasing overlaps between Big Data Analysis and HPC in an era of next-generation data centric computing that has developed due to the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors, whose data needs to be processed, analysed and integrated into simulations to derive scientific and innovative insights. Indeed, Exascale I/O, as a problem that has not been sufficiently dealt with for simulation codes, is appropriately addressed by the SAGE platform. The objective of this paper is to discuss the software architecture of the SAGE system and look at early results we have obtained employing some of its key methodologies, as the system continues to evolve.

  • 6.
    Peng, Ivy Bo
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Data Movement on Emerging Large-Scale Parallel Systems2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Large-scale HPC systems are an important driver for solving computational problems in scientific communities. Next-generation HPC systems will not only grow in scale but also in heterogeneity. This increased system complexity entails more challenges to data movement in HPC applications. Data movement on emerging HPC systems requires asynchronous fine-grained communication and efficient data placement in the main memory. This thesis proposes an innovative programming model and algorithm to prepare HPC applications for the next computing era: (1) a data streaming model that supports emerging data-intensive applications on supercomputers, (2) a decoupling model that improves parallelism and mitigates the impact of imbalance in applications, (3) a new framework and methodology for predicting the impact of largescale heterogeneous memory systems on HPC applications, and (4) a data placement algorithm that uses a set of rules and a decision tree to determine the data-to-memory mapping in heterogeneous main memory.

    The proposed approaches in this thesis are evaluated on multiple supercomputers with different processors and interconnect networks. The evaluation uses a diverse set of applications that represent conventional scientific applications and emerging data-analytic workloads on HPC systems. The experimental results on the petascale testbed show that the approaches obtain increasing performance improvements as system scale increases and this trend supports the approaches as a valuable contribution towards future HPC systems.

  • 7.
    Peng, Ivy Bo
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Gioiosa, Roberto
    Kestor, Gokcen
    Laure, Erwin
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Preparing HPC Applications for the Exascale Era: A Decoupling Strategy2017In: 2017 46th International Conference on Parallel Processing (ICPP), IEEE Computer Society, 2017, p. 1-10, article id 8025274Conference paper (Refereed)
    Abstract [en]

    Production-quality parallel applications are often a mixture of diverse operations, such as computation- and communication-intensive, regular and irregular, tightly coupled and loosely linked operations. In conventional construction of parallel applications, each process performs all the operations, which might result inefficient and seriously limit scalability, especially at large scale. We propose a decoupling strategy to improve the scalability of applications running on large-scale systems. Our strategy separates application operations onto groups of processes and enables a dataflow processing paradigm among the groups. This mechanism is effective in reducing the impact of load imbalance and increases the parallel efficiency by pipelining multiple operations. We provide a proof-of-concept implementation using MPI, the de-facto programming system on current supercomputers. We demonstrate the effectiveness of this strategy by decoupling the reduce, particle communication, halo exchange and I/O operations in a set of scientific and data-analytics applications. A performance evaluation on 8,192 processes of a Cray XC40 supercomputer shows that the proposed approach can achieve up to 4x performance improvement.

  • 8. Peng, Ivy Bo
    et al.
    Markidis, Stefano
    Gioiosa, Roberto
    Kestor, Gokcen
    Laure, Erwin
    MPI Streams for HPC Applications2017In: New Frontiers in High Performance Computing and Big Data, IEEE, 2017Chapter in book (Refereed)
  • 9.
    Peng, Ivy Bo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Gioiosa, Roberto
    Pacific Northwest Natl Lab, Computat Sci & Math Div, Richland, WA 99352 USA..
    Kestor, Gokcen
    Pacific Northwest Natl Lab, Computat Sci & Math Div, Richland, WA 99352 USA..
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    MPI Streams for HPC Applications2017In: NEW FRONTIERS IN HIGH PERFORMANCE COMPUTING AND BIG DATA / [ed] Fox, G Getov, V Grandinetti, L Joubert, G Sterling, T, IOS PRESS , 2017, p. 75-92Conference paper (Refereed)
    Abstract [en]

    Data streams are a sequence of data flowing between source and destination processes. Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal of this work is to extend the use of data streams to support both conventional scientific applications and emerging data analytics applications running on HPC platforms. We introduce an extension called MPIStream to the de-facto programming standard on HPC, MPI. MPIStream supports data streams either within a single application or among multiple applications. We present three use cases using MPI streams in HPC applications together with their parallel performance. We show the convenience of using MPI streams to support the needs from both traditional HPC and emerging data analytics applications running on supercomputers.

  • 10.
    Rivas-Gomez, Sergio
    et al.
    KTH, School of Computer Science and Communication (CSC).
    Markidis, Stefano
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Peng, Ivy Bo
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Laure, E.
    Kestor, G.
    Gioiosa, R.
    Extending message passing interface windows to storage2017In: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 728-730Conference paper (Refereed)
    Abstract [en]

    This paper presents an extension to MPI supporting the one-sided communication model and window allocations in storage. Our design transparently integrates with the current MPI implementations, enabling applications to target MPI windows in storage, memory or both simultaneously, without major modifications. Initial performance results demonstrate that the presented MPI window extension could potentially be helpful for a wide-range of use-cases and with low-overhead.

  • 11. Toth, Gabor
    et al.
    Chen, Yuxi
    Gombosi, Tamas I.
    Cassak, Paul
    Markidis, Stefano
    KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Peng, Ivy Bo
    KTH.
    Scaling the Ion Inertial Length and Its Implications for Modeling Reconnection in Global Simulations2017In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 122, no 10, p. 10336-10355Article in journal (Refereed)
    Abstract [en]

    We investigate the use of artificially increased ion and electron kinetic scales in global plasma simulations. We argue that as long as the global and ion inertial scales remain well separated, (1) the overall global solution is not strongly sensitive to the value of the ion inertial scale, while (2) the ion inertial scale dynamics will also be similar to the original system, but it occurs at a larger spatial scale, and (3) structures at intermediate scales, such as magnetic islands, grow in a self-similar manner. To investigate the validity and limitations of our scaling hypotheses, we carry out many simulations of a two-dimensional magnetosphere with the magnetohydrodynamics with embedded particle-in-cell (MHD-EPIC) model. The PIC model covers the dayside reconnection site. The simulation results confirm that the hypotheses are true as long as the increased ion inertial length remains less than about 5% of the magnetopause standoff distance. Since the theoretical arguments are general, we expect these results to carry over to three dimensions. The computational cost is reduced by the third and fourth powers of the scaling factor in two-and three-dimensional simulations, respectively, which can be many orders of magnitude. The present results suggest that global simulations that resolve kinetic scales for reconnection are feasible. This is a crucial step for applications to the magnetospheres of Earth, Saturn, and Jupiter and to the solar corona.

  • 12.
    Yu, Yiqun
    et al.
    Beihang Univ, Sch Space & Environm, Beijing, Peoples R China..
    Delzanno, Gian Luca
    Los Alamos Natl Lab, Los Alamos, NM USA..
    Jordanova, Vania
    Los Alamos Natl Lab, Los Alamos, NM USA..
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    PIC simulations of wave-particle interactions with an initial electron velocity distribution from a kinetic ring current model2018In: Journal of Atmospheric and Solar-Terrestrial Physics, ISSN 1364-6826, E-ISSN 1879-1824, Vol. 177, p. 169-178Article in journal (Refereed)
    Abstract [en]

    Whistler wave-particle interactions play an important role in the Earth inner magnetospheric dynamics and have been the subject of numerous investigations. By running a global kinetic ring current model (RAM-SCB) in a storm event occurred on Oct 23-24 2002, we obtain the ring current electron distribution at a selected location at MLT of 9 and L of 6 where the electron distribution is composed of a warm population in the form of a partial ring in the velocity space (with energy around 15 keV) in addition to a cool population with a Maxwellian-like distribution. The warm population is likely from the injected plasma sheet electrons during substorm injections that supply fresh source to the inner magnetosphere. These electron distributions are then used as input in an implicit particle-in-cell code (iPIC3D) to study whistler-wave generation and the subsequent wave-particle interactions. We find that whistler waves are excited and propagate in the quasi-parallel direction along the background magnetic field. Several different wave modes are instantaneously generated with different growth rates and frequencies. The wave mode at the maximum growth rate has a frequency around 0.62 omega(ce), which corresponds to a parallel resonant energy of 2.5 keV. Linear theory analysis of wave growth is in excellent agreement with the simulation results. These waves grow initially due to the injected warm electrons and are later damped due to cyclotron absorption by electrons whose energy is close to the resonant energy and can effectively attenuate waves. The warm electron population overall experiences net energy loss and anisotropy drop while moving along the diffusion surfaces towards regions of lower phase space density, while the cool electron population undergoes heating when the waves grow, suggesting the cross-population interactions.

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