User & HPC Application Support

The code MGLET has been designed for the numerical simulation of complex turbulent flows. MGLET uses a Finite Volume method to solve the incompressible Navier-Stokes equations. It uses a Cartesian grid with staggered arrangement of the variables that enables an efficient formulation of the spatial approximations. An explicit third order low-storage Runge-Kutta method is used for time integration. The pressure is computed within the framework of fractional step or Chorin's projection method, respectively. Therefore, at every Runge-Kutta substep, a linear system of equations, a Poisson-equation, has to be solved. Geometrically complex surfaces are represented by an Immersed Boundary Method.

We present atomic-scale computer simulations in equiatomic L10-CoPt where Molecular Dynamics and Monte Carlo techniques have both been applied to study the vacancy-atom exchange and kinetics relaxation. The atomic potential is determined using a Tight-Binding formalism within the Second-Moment Approximation. It is used to evaluate the different saddle-point energies involved in a vacancy-atom exchange between nearest-neighbour sites. The potential and the saddle-point energies have been used to simulate the relaxation of the long-range order in CoPt using a Monte Carlo technique. A vacancy migration energy of 0.73 ± 0.15 eV and an order-disorder transition temperature of 935 K have been found.

Heterometallic molecular chromium wheels are fascinating new magnetic materials. We reexamine the available experimental susceptibility data on MCr7 wheels in terms of a simple isotropic Heisenberg Hamiltonian for M=Fe, Ni, Cu, and Zn and find in that FeCr7 needs to be described with an iron-chromium exchange that is different from all other cases. In a second step we model the behavior of the proton spin lattice relaxation rate as a function of applied magnetic field for low temperatures as it is measured in Nuclear Magnetic Resonance (NMR) experiments. It appears that CuCr7 and NiCr7 show an unexpectedly reduced relaxation rate at certain level crossings.

Monte Carlo simulations have been performed to investigate the relaxation of the L1 0 long-range order in dimensionally reduced systems. The effect of the number of (001)-type monatomic layers and of the pair interaction energies on these kinetics has been examined. The vacancy migration energies have been deduced from the Arrhenius plots of the relaxation times. A substantial increase in the migration energy for small film thickness is observed. The results agree with previous Monte Carlo simulations and with recent experimental results in L1 0 thin films and multilayers.

As the complexity and size of challenges in science and engineering are continually increasing, it is highly important that applications are able to scale strongly to very large numbers of cores (>100,000 cores) to enable HPC systems to be utilised efficiently. This paper presents results of strong scaling tests performed with an MPI only and a hybrid MPI + OpenMP version of the Lattice QCD application BQCD on the European Tier-0 system SuperMUC at LRZ.

As the complexity and size of challenges in science and engineering are continually increasing, it is highly important that applications are able to scale strongly to very large numbers of cores (>100,000 cores) to enable HPC systems to be utilised efficiently. This paper presents results of strong scaling tests performed with an MPI only and a hybrid MPI + OpenMP version of the Lattice QCD application BQCD on the European Tier-0 system SuperMUC at LRZ.

November 2002, "Magister", Supervisors : Dr. H. Bouzar (Tizi-Ouzou, Algeria) with the collaboration of Dr. V. Pierron-Bohnes and Dr. C. Goyhenex (Strasbourg, France). Mouloud Mammeri University, Tizi-Ouzou (Algeria) and Louis Pasteur University, Strasbourg (France).

High-performance computing (HPC) is recognized as one of the pillars for further advance of science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging challenges in order to reach Exascale level of performance, which is expected by the year 2020. The much larger embedded and mobile market allows for rapid development of IP blocks, and provides more flexibility in designing an application-specific SoC, un turn giving possibility in balancing performance, energy-efficiency and cost. In the Mont-Blanc project, we advocate for HPC systems be built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and NICs from the embedded and mobile domain, and offthe-shelf HPC networking, storage, cooling and integration solutions. We present the system's architecture, and evaluation including both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability, and estimate the maximum scalability point of this approach across a set of applications.

We present a novel, highly scalable and optimized solver for turbulent flows based on high-order discontinuous Galerkin discretizations of the incompressible Navier-Stokes equations aimed to minimize time-to-solution. The solver uses explicit-implicit time integration with variable step size. The central algorithmic component is the matrix-free evaluation of discretized finite element operators. The node-level performance is optimized by sum-factorization kernels for tensor-product elements with unique algorithmic choices that reduce the number of arithmetic operations, improve cache usage, and vectorize the arithmetic work across elements and faces. These ingredients are integrated into a framework scalable to the massive parallelism of supercomputers by the use of optimal-complexity linear solvers, such as mixed-precision, hybrid geometric-polynomial-algebraic multigrid solvers for the pressure Poisson problem. The application problem under consideration are fluid dynamical simulations of the human respiratory system under mechanical ventilation conditions, using unstructured/structured

Tensor network methods are incredibly effective for simulating quantum circuits. This is due to their ability to efficiently represent and manipulate the wave-functions of large interacting quantum systems. We describe the challenges faced when scaling tensor network simulation approaches to Exascale compute platforms and introduce QuantEx, a framework for tensor network circuit simulation at Exascale.

Running from October 2011 to June 2015, the aim of the European project Mont-Blanc has been to develop an approach to Exascale computing based on embedded power-efficient technology. The main goals of the project were to i) build an HPC prototype using currently available energy-efficient embedded technology, ii) design a Next Generation system to overcome the limitations of the built prototype and iii) port a set of representative Exascale applications to the system. This article summarises the contributions from the Leibniz Supercomputing Centre (LRZ) and the Juelich Supercomputing Centre (JSC), Germany, to the Mont-Blanc project.

In spring 2015, the Leibniz Supercomputing Centre (Leibniz-Rechenzentrum, LRZ), installed their new Peta-Scale System SuperMUC Phase2. Selected users were invited for a 28 day extreme scale-out block operation during which they were allowed to use the full system for their applications. The following projects participated in the extreme scale-out workshop: BQCD (Quantum Physics), SeisSol (Geophysics, Seismics), GPI-2/GASPI (Toolkit for HPC), Seven-League Hydro (Astrophysics), ILBDC (Lattice Boltzmann CFD), Iphigenie (Molecular Dynamic), FLASH (Astrophysics), GADGET (Cosmological Dynamics), PSC (Plasma Physics), waLBerla (Lattice Boltzmann CFD), Musubi (Lattice Boltzmann CFD), Vertex3D (Stellar Astrophysics), CIAO (Combustion CFD), and LS1-Mardyn (Material Science). The projects were allowed to use the machine exclusively during the 28 day period, which corresponds to a total of 63.4 million core-hours, of which 43.8 million core-hours were used by the applications, resulting in a ut...

Ferdinand Jamitzky Leibniz Supercomputing Centre (LRZ) Boltzmannstrasse 1 85748 Garching, Germany [email protected] Helmut Satzger Leibniz Supercomputing Centre (LRZ) Boltzmannstrasse 1 85748 Garching, Germany [email protected] Gerald Mathias Ludwig-MaximiliansUniversität München, Lehrstuhl für BioMolekulare Optik Oettingenstrasse 67 80538 München, Germany [email protected]

This study compares the performance of high-order discontinuous Galerkin finite elements on modern hardware. The main computational kernel is the matrix-free evaluation of differential operators by sum factorization, exemplified on the symmetric interior penalty discretization of the Laplacian as a metric for a complex application code in fluid dynamics. State-of-the-art implementations of these kernels stress both arithmetics and memory transfer. The implementations of SIMD vectorization and shared-memory parallelization are detailed. Computational results are presented for dual-socket Intel Haswell CPUs at 28 cores, a 64-core Intel Knights Landing, and a 16-core IBM Power8 processor. Up to polynomial degree six, Knights Landing is approximately twice as fast as Haswell. Power8 performs similarly to Haswell, trading a higher frequency for narrower SIMD units. The performance comparison shows that simple ways to express parallelism through for loops perform better on medium and high core counts than a more elaborate task-based parallelization with dynamic scheduling according to dependency graphs, despite less memory transfer in the latter algorithm.

QXTools is a framework for simulating quantum circuits using tensor network methods. Weak simulation is the primary use case where given a quantum circuit and input state QXTools will efficiently calculate the probability amplitude of a given output configuration or set of configurations. Given this ability one can sample from the output distribution using random sampling approaches. QXTools is intended to be used by researchers interested in simulating circuits larger than those possible with full wave-function simulators or those interested in research and development of tensor network circuit simulation methods. See Brennan et al. (2021) for more complete background and scaling results and Brayford et al. (2021) for details about deploying in containerised environments.

The simulation of quantum circuits using the tensor network method is very computationally demanding and requires significant High Performance Computing (HPC) resources to find an efficient contraction order and to perform the contraction of the large tensor networks. In addition, the researchers want a workflow that is easy to customize, reproduce and migrate to different HPC systems. In this paper, we discuss the issues associated with the deployment of the QuantEX quantum computing simulation software within containers on different HPC systems. Also, we compare the performance of the containerized software with the software running on “bare metal”.