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  • 1.
    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.

  • 2.
    Chien, Wei Der
    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.
    An Evaluation of TensorFlow as a Programming Framework for HPC Applications2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow computation problems are expressed as Computation Graphs where nodes of a graph denote operation and edges denote data movement between operations. With increasing number of heterogeneous accelerators which might co-exist on the same cluster system, it became increasingly difficult for users to program efficient and scalable applications. TensorFlow provides a high level of abstraction and it is possible to place operations of a computation graph on a device easily through a high level API. In this work, the usability of TensorFlow as a programming framework for HPC application is reviewed. We give an introduction of TensorFlow as a programming framework and paradigm for distributed computation. Two sample applications are implemented on TensorFlow: tiled matrix multiplication and conjugate gradient solver for solving large linear systems. We try to illustrate how such problems can be expressed in computation graph for distributed computation. We perform scalability tests and comment on performance scaling results and quantify how TensorFlow can take advantage of HPC systems by performing micro-benchmarking on communication performance. Through this work, we show that TensorFlow is an emerging and promising platform which is well suited for a particular class of problem which requires very little synchronization.

  • 3.
    Dugani, Vishwanath
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH, School of Computer Science and Communication (CSC).
    Continuous system-wide profiling of High Performance Computing parallel applications: Profiling high performance applications2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Profiling of an application identifies parts of the code being executed using the hardware performance counters thus providing the application’s performance. Profiling has long been standard in the development process focused on a single execution of a single program. As computing systems have evolved, understanding the bigger picture across multiple machines has become increasingly important. As supercomputing grows in pervasiveness and scale, understanding parallel applications performance and utilization characteristics is critically important, because even minor performance improvements translate into large cost savings. The study surveys various tools for the application. After which, Perfminer was integrated in SCANIA’s Linux clusters to profile CFD and FEA applications exploiting the batch queue system features for continuous system wide profiling, which provides performance insights for high performance applications, with negligible overhead. Perfminer provides stable, accurate profiles and a cluster-scale tool for performance analysis. Perfminer effectively highlights the micro-architectural bottlenecks.

  • 4.
    Dyczynski, Matheus
    et al.
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden..
    Yu, Yasmin
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden.;Sprint Biosci, Huddinge, Sweden..
    Otrocka, Magdalena
    Karolinska Inst, Dept Med Biochem & Biophys, Sci Life Lab Stockholm, Chem Biol Consortium Sweden, Solna, Sweden..
    Parpal, Santiago
    Sprint Biosci, Huddinge, Sweden..
    Braga, Tiago
    Sprint Biosci, Huddinge, Sweden..
    Henley, Aine Brigette
    Sprint Biosci, Huddinge, Sweden..
    Zazzi, Henric
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Lerner, Mikael
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden..
    Wennerberg, Krister
    Univ Helsinki, Inst Mol Med Finland, FIMM, Helsinki, Finland..
    Viklund, Jenny
    Sprint Biosci, Huddinge, Sweden..
    Martinsson, Jessica
    Sprint Biosci, Huddinge, Sweden..
    Grander, Dan
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden..
    De Milito, Angelo
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden.;Sprint Biosci, Huddinge, Sweden..
    Tamm, Katja Pokrovskaja
    Karolinska Inst, Dept Oncol Pathol, Canc Ctr Karolinska, Stockholm, Sweden..
    Targeting autophagy by small molecule inhibitors of vacuolar protein sorting 34 (Vps34) improves the sensitivity of breast cancer cells to Sunitinib2018In: Cancer Letters, ISSN 0304-3835, E-ISSN 1872-7980, Vol. 435, p. 32-43Article in journal (Refereed)
    Abstract [en]

    Resistance to chemotherapy is a challenging problem for treatment of cancer patients and autophagy has been shown to mediate development of resistance. In this study we systematically screened a library of 306 known anti-cancer drugs for their ability to induce autophagy using a cell-based assay. 114 of the drugs were classified as autophagy inducers; for 16 drugs, the cytotoxicity was potentiated by siRNA-mediated knock-down of Atg7 and Vps34. These drugs were further evaluated in breast cancer cell lines for autophagy induction, and two tyrosine kinase inhibitors, Sunitinib and Erlotinib, were selected for further studies. For the pharmacological inhibition of autophagy, we have characterized here a novel highly potent selective inhibitor of Vps34, SB02024. SB02024 blocked autophagy in vitro and reduced xenograft growth of two breast cancer cell lines, MDA-MB-231 and MCF-7, in vivo. Vps34 inhibitor significantly potentiated cytotoxicity of Sunitinib and Erlotinib in MCF-7 and MDA-MB-231 in vitro in monolayer cultures and when grown as multicellular spheroids. Our data suggests that inhibition of autophagy significantly improves sensitivity to Sunitinib and Erlotinib and that Vps34 is a promising therapeutic target for combination strategies in breast cancer.

  • 5. Eliasson, P.
    et al.
    Gong, Jing
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Nordström, J.
    A stable and conservative coupling of the unsteady compressible navier-stokes equations at interfaces using finite difference and finite volume methods2018In: AIAA Aerospace Sciences Meeting, 2018, American Institute of Aeronautics and Astronautics Inc, AIAA , 2018, no 210059Conference paper (Refereed)
    Abstract [en]

    Stable and conservative interface boundary conditions are developed for the unsteady compressible Navier-Stokes equations using finite difference and finite volume methods. The finite difference approach is based on summation-by-part operators and can be made higher order accurate with boundary conditions imposed weakly. The finite volume approach is an edge- and dual grid-based approach for unstructured grids, formally second order accurate in space, with weak boundary conditions as well. Stable and conservative weak boundary conditions are derived for interfaces between finite difference methods, for finite volume methods and for the coupling between the two approaches. The three types of interface boundary conditions are demonstrated for two test cases. Firstly, inviscid vortex propagation with a known analytical solution is considered. The results show expected error decays as the grid is refined for various couplings and spatial accuracy of the finite difference scheme. The second test case involves viscous laminar flow over a cylinder with vortex shedding. Calculations with various coupling and spatial accuracies of the finite difference solver show that the couplings work as expected and that the higher order finite difference schemes provide enhanced vortex propagation.

  • 6.
    Jansson, Niclas
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. RIKEN Advanced Institute for Computational Science, Kobe, Japan.
    Bale, Rahul
    RIKEN Advanced Institute for Computational Science, Kobe, Japan.
    Onishi, Keiji
    RIKEN Advanced Institute for Computational Science, Kobe, Japan.
    Tsubokura, Makoto
    Kobe University and RIKEN Advanced Institute for Computational Science, Kobe Japan.
    CUBE: A scalable framework for large-scale industrial simulations2018In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846Article in journal (Refereed)
  • 7. Larsson, Torbjörn
    et al.
    Hammar, Johan
    Gong, Jing
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Barth, Michaela
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Axner, Lilit
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    ENHANCING COMPUTATIONAL AERO-ACOUSTIC PROCESSES FOR GROUNDVEHICLES RESOLVING OPEN SOURCE CFD2018In: The 13th OpenFOAM Workshop, 2018, p. 1-4Conference paper (Refereed)
  • 8.
    Laure, Erwin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Ahlin, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Malinowsky, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Svensson, Gert
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Vincent, Jonathan
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Lindgren the Swedish tier-1 system2015In: Contemporary High Performance Computing: From Petascale Toward Exascale: Volume Two, CRC Press , 2015, p. 141-162Chapter in book (Other academic)
    Abstract [en]

    The Swedish academic computing landscape is organized under the auspices of SNIC, the Swedish National Infrastructure for Computing. SNIC coordinates investments in computing and storage infrastructure at its six national centers and manages the national process for allocating research time on its computing resources. Since its formation in 2003, SNIC has significantly increased the computational capacity available to Swedish researchers and firmly put Sweden on the international computational science map. When the Partnership for Advanced Computing in Europe (PRACE) started in 2010, SNIC joined this European HPC effort and worked with the Swedish Research 142Council to allocate additional funds for a national high-end system that would also be made available to European researchers via PRACE. These efforts resulted in the installation of a CRAY XE6 supercomputer, named Lindgren, at the PDC Center for High-Performance Computing at the KTH Royal Institute of Technology in Stockholm. 

  • 9.
    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.

  • 10.
    Otero, Evelyn
    et al.
    KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.
    Gong, Jing
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Min, Misun
    Argonne National Laboratory.
    Fischer, Paul
    Argonne National Laboratory.
    Schlatter, Philipp
    KTH, School of Engineering Sciences (SCI), Mechanics. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    OpenACC accelerator for the Pn-Pn-2 algorithm in Nek50002018In: Proceedings of the 5th International Conference on Exascale Applications and Software, 2018Conference paper (Refereed)
  • 11.
    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.

  • 12.
    Zhang, Mengmeng
    et al.
    KTH.
    Melin, Tomas
    Gong, Jing
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Barth, Michaela
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Axner, Lilit
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Mixed Fidelity Aerodynamic and Aero-Structural Optimization for Wings2018In: 2018 International Conference on High Performance Computing & Simulation, 2018, p. 476-483Conference paper (Refereed)
    Abstract [en]

    Automatic multidisciplinary design optimization is one of the challenges that are faced in the processes involved in designing efficient wings for aircraft. In this paper we present mixed fidelity aerodynamic and aero-structural optimization methods for designing wings. A novel shape design methodology has been developed - it is based on a mix of the automatic aerodynamic optimization for a reference aircraft model, and the aero-structural optimization for an uninhabited air vehicle (UAV) with a high aspect ratio wing. This paper is a significant step towards making it possible to perform all the core processes for aerodynamic and aero-structural optimization that require special skills in a fully automatic manner - this covers all the processes from creating the mesh for the wing simulation to executing the high-fidelity computational fluid dynamics (CFD) analysis code. Our results confirm that the simulation tools can make it possible for a far broader range of engineering researchers and developers to design aircraft in much simpler and more efficient ways. This is a vital step in the evolution of wing design processes as it means that the extremely expensive laboratory experiments that were traditionally used when designing the wings can now be replaced with more cost effective high performance computing (HPC) simulation that utilize accurate numerical methods.

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