@article{KnoopGronemeierSuehringetal.2017, author = {Knoop, Helge and Gronemeier, Tobias and S{\"u}hring, Matthias and Steinbach, Peter and Noack, Matthias and Wende, Florian and Steinke, Thomas and Knigge, Christoph and Raasch, Siegfried and Ketelsen, Klaus}, title = {Porting the MPI-parallelized LES model PALM to multi-GPU systems and many integrated core processors: an experience report}, journal = {International Journal of Computational Science and Engineering. Special Issue on: Novel Strategies for Programming Accelerators}, edition = {Special Issue on: Novel Strategies for Programming Accelerators}, publisher = {Inderscience}, year = {2017}, abstract = {The computational power and availability of graphics processing units (GPUs), such as the Nvidia Tesla, and Many Integrated Core (MIC) processors, such as the Intel Xeon Phi, on high performance computing (HPC) systems is rapidly evolving. However, HPC applications need to be ported to take advantage of such hardware. This paper is a report on our experience of porting the MPI+OpenMP parallelised large-eddy simulation model (PALM) to multi-GPU as well as to MIC processor environments using the directive-based high level programming paradigm OpenACC and OpenMP, respectively. PALM is a Fortran-based computational fluid dynamics software package, used for the simulation of atmospheric and oceanic boundary layers to answer questions linked to fundamental atmospheric turbulence research, urban modelling, aircraft safety and cloud physics. Development of PALM started in 1997, the project currently entails 140 kLOC and is used on HPC farms of up to 43,200 cores. The main challenges we faced during the porting process are the size and complexity of the PALM code base, its inconsistent modularisation and the complete lack of a unit-test suite. We report the methods used to identify performance issues as well as our experiences with state-of-the-art profiling tools. Moreover, we outline the required porting steps in order to properly execute our code on GPUs and MIC processors, describe the problems and bottlenecks that we encountered during the porting process, and present separate performance tests for both architectures. These performance tests, however, do not provide any benchmark information that compares the performance of the ported code between the two architectures.}, language = {en} } @incollection{NoackWendeZitzlsbergeretal.2017, author = {Noack, Matthias and Wende, Florian and Zitzlsberger, Georg and Klemm, Michael and Steinke, Thomas}, title = {KART - A Runtime Compilation Library for Improving HPC Application Performance}, volume = {10524}, booktitle = {High Performance Computing: ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Frankfurt, Germany, June 18-22, 2017, Revised Selected Papers}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-67630-2_29}, pages = {389 -- 403}, year = {2017}, abstract = {The effectiveness of ahead-of-time compiler optimization heavily depends on the amount of available information at compile time. Input-specific information that is only available at runtime cannot be used, although it often determines loop counts, branching predicates and paths, as well as memory-access patterns. It can also be crucial for generating efficient SIMD-vectorized code. This is especially relevant for the many-core architectures paving the way to exascale computing, which are more sensitive to code-optimization. We explore the design-space for using input-specific information at compile-time and present KART, a C++ library solution that allows developers tocompile, link, and execute code (e.g., C, C++ , Fortran) at application runtime. Besides mere runtime compilation of performance-critical code, KART can be used to instantiate the same code multiple times using different inputs, compilers, and options. Other techniques like auto-tuning and code-generation can be integrated into a KART-enabled application instead of being scripted around it. We evaluate runtimes and compilation costs for different synthetic kernels, and show the effectiveness for two real-world applications, HEOM and a WSM6 proxy.}, language = {en} } @article{HeinzeDipankarHenkenetal.2017, author = {Heinze, Rieke and Dipankar, Anurag and Henken, Cintia Carbajal and Moseley, Christopher and Sourdeval, Odran and Tr{\"o}mel, Silke and Xie, Xinxin and Adamidis, Panos and Ament, Felix and Baars, Holger and Barthlott, Christian and Behrendt, Andreas and Blahak, Ulrich and Bley, Sebastian and Brdar, Slavko and Brueck, Matthias and Crewell, Susanne and Deneke, Hartwig and Di Girolamo, Paolo and Evaristo, Raquel and Fischer, J{\"u}rgen and Frank, Christopher and Friederichs, Petra and G{\"o}cke, Tobias and Gorges, Ksenia and Hande, Luke and Hanke, Moritz and Hansen, Akio and Hege, Hans-Christian and Hose, Corinna and Jahns, Thomas and Kalthoff, Norbert and Klocke, Daniel and Kneifel, Stefan and Knippertz, Peter and Kuhn, Alexander and van Laar, Thriza and Macke, Andreas and Maurer, Vera and Mayer, Bernhard and Meyer, Catrin I. and Muppa, Shravan K. and Neggers, Roeland A. J. and Orlandi, Emiliano and Pantillon, Florian and Pospichal, Bernhard and R{\"o}ber, Niklas and Scheck, Leonhard and Seifert, Axel and Seifert, Patric and Senf, Fabian and Siligam, Pavan and Simmer, Clemens and Steinke, Sandra and Stevens, Bjorn and Wapler, Kathrin and Weniger, Michael and Wulfmeyer, Volker and Z{\"a}ngl, G{\"u}nther and Zhang, Dan and Quaas, Johannes}, title = {Large-eddy simulations over Germany using ICON: a comprehensive evaluation}, volume = {143}, journal = {Quarterly Journal of the Royal Meteorological Society}, number = {702}, doi = {10.1002/qj.2947}, pages = {69 -- 100}, year = {2017}, abstract = {Large-eddy simulations (LES) with the new ICOsahedral Non-hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small-scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary-layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small- to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small- to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.}, language = {en} }