September 2015 - Allocations-admins - mailman3.cels.anl.gov

[LCRC Accounts] Yearly Allocation Request for uncertainty-climate
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Emil M Constantinescu Project Name: uncertainty-climate Division: MCS Project title: Uncertainty in weather forecasting for energy systems Associated funding: Other Systems: NFS @ MCS Science: The theoretical part of the project consists in developing uncertainty models for numerical weather and climate prediction systems for use in the operation and planning of energy systems. Fusion is used to run several scenarios and obtain forecasts and ensemble-based uncertainty information about ambient variables by running weather/climate simulation codes. We also perform adjoint sensitivity analyses that indicate suitable locations for meteorological sensor siting. This information is used in energy management applications such as stochastic optimization control loops that provides optimal operational point for energy systems. A secondary objective is to develop robust data assimilation methods (i.e., correct model predictions with observations) in order to calibrate weather and climate forecasts for effective operation and planning of energy systems. Project description: We will develop general spatial partitioning models for large spatial and spatio-temporal data sets for (1) identifying potential subregions that have different underlying spatial processes to address nonstationarity at presence for large spatial data; and (2) improving the computational feasibility via parallel computation over subregions and multi-level partitions. We will explore partitioning models that allow dependence structure across the whole study domain and communication between subregions. We will extend the spatial partitioning models using Dirichlet tessellation to grouping curves such as temporal or functional data and implement them under Bayesian frameworks. We will apply the models to the spectral radiance measurements in climate change study. Industry partnership: Project URL: http://www.mcs.anl.gov/~emconsta/Projects_ScalableIMEX.php Current FY Hours Used: undetermined amount New FY Requested allocation: 320000 Q1: 80000 Q2: 80000 Q3: 80000 Q4: 80000 Justification: Storage requirements: Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request for SHARP-IF
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Rui Hu Project Name: SHARP-IF Division: NE Project title: SHARP – Intermediate Fidelity Modeling Associated funding: DOE-Nuclear Energy Other Systems: Science: An intermediate fidelity (IF) thermal-hydraulics modeling method is being pursued for a fast-running, modest-fidelity, whole-core transient analyses capability. The approach is essential for design scoping and engineering analyses and could lead to improvements in the design of the new generations of reactors and to the reduction of uncertainties in safety analysis. The IF method relies on RANS based turbulence modeling approach and advanced system modeling methods to represent the fluid flow and heat transfer physics. Project description: The RANS-based commercial CFD code STAR-CCM+ is incorporated into the SHARP framework for a multi-scale analysis of Advanced Fast Reactors. STAR-CCM+ will be continuously used for design evaluations of reactor systems and experimental facilities. Notably, advanced sodium fast reactor design concept and the natural convection shutdown test facility that is currently being built at Argonne. This type of simulations is essential to assure the safety of the proposed design. Also, the post-test simulation results of the NSTF will be compared against to the experimental results as a validation study of the CFD code. A new advanced reactor system analysis tool SAM is being developed at Argonne for sodium-cooled fast reactor (SFR) applications based on the MOOSE framework. PROTEUS is a highly scalable, 3-D deterministic neutron transport code developed at ANL. It provides a high-fidelity heterogeneous geometry neutron transport capability for coupled multi-physics nuclear reactor applications. It is planned in FY16 to develop coupled SAM/Proteus simulation capability, and perform demonstration calculations. At Argonne, the CFD code STAR-CCM+ are routinely applied to simulations that use more than 200 cores, and good parallel performance has been observed. As problem size increases, i.e. for finer meshes and for large pin bundles, larger numbers of cores would be utilized and based on data from the developers good parallel performance will continue to be expected. On the other hand, the system code SAM is based on the MOOSE framework. The parallelization will be provided by the underlying MOOSE framework that is built on PETSc. The scalability of MOOSE has been demonstrated by other MOOSE-based applications. The system code usually relies on coarse representation of the system thus does not require significant amount of computing resources. PROTEUS is a highly scalable code, and its scaling performance has been demonstrated with extremely large simulations on Intrepid and Mira. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 210000 Q1: 60000 Q2: 60000 Q3: 80000 Q4: 10000 Justification: Storage requirements: 1 TB Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request for orfpga
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Azamat Mametjanov Project Name: orfpga Division: MCS Project title: Autotuning FPGA designs Associated funding: NASA Other Systems: Science: Designing FPGAs for space applications is a complex iterative process involving simultaneous optimization of timing score, device utilization, and power consumption cite{Gaisler1,Gaisler2}. Achieving near optimal performance is challenging even for the most experienced engineers. The number of vendors offering FPGAs (e.g., Xilinx, Altera, QuickLogic, Actel/Microsmei) for space applications has also increased. Each of these vendors base their FPGAs on custom architectures and thus, the best performing design for a given application varies depending on the chosen FPGA architecture. In addition, the optimizations parameters, techniques, and best practices often differ for each vendor's FPGA. For organizations that use FPGAs from multiple vendors, this exacerbates the already lengthly FPGA design optimization process. The FPGA design techniques have evolved to provide abstractions such as IPCores (Intellectual Property Cores), which provide individual functions that can be composed to build larger designs. Therefore, in order to reduce development effort and increase code reuse, IPCores are often used in FPGA designs. However, IPCores have many tunable parameters cite{Junchao} that affect the performance of an FPGA design. In addition, the designer wants to experiment with various HDL (VHDL, Verilog etc.) and HLS (high level synthesis) code constructs. For example, the user might want to try different pipeline depths for a given piece of HLS code. Lastly, the designer also needs to experiment with different input parameters and random seeds to the synthesis and implementation (Mapping, Placement and Routing (PAR)) tools. These user settings can have dramatic impact on design performance because they influence the usage of underlying optimizations such as merging, retiming, and pipelining cite{SE1}. Thus the user tunable parameter space includes: 1) user customizable IPCores parameters, 2) various HDL and HLS code constructs, and 3) input parameters to the Synthesis, Mapping, and Placement and Routing tools. The choice of these parameters and the underlying FPGA architecture significantly affects the performance of an FPGA design. Manually exploring the aforementioned FPGA parameter space is a time consuming task and impedes designer productivity. The existing FPGA design tools (e.g., Xilinx Vivado/ISE Design Suites, Altera Quartus II) are unable to automatically deliver the best possible design for an application through the underlying optimization algorithms. Further HDL/HLS code optimization, and tuning of parameters is required at the designer level to meet the performance goals. This situation results in a potentially large gap between the achieved FPGA performance and best achievable performance on a given FPGA architecture. There is a need among NASA FPGA developers for next generation tools that can generate performance models and assist in deducing the near optimal designs through higher level inputs (performance goals, possible parameter values, different code variants etc.). Such performance optimization tools cite{Vuduc,Hart2009Orio,OrCuda2012, WN147,Tiwari2011,BalWilNor2011} are very prominent in high performance computing (HPC) domain. For scientific computing codes, these autotuning tools greatly assist performance optimizations and improve developer productivity. For example, the Orio tool cite{Hart2009Orio, OrCuda2012} (developed by ANL) takes annotated C or FORTRAN source code as input, generates optimized code variants from the annotated code, and empirically evaluates the performance of the generated codes. Orio then selects the best performing version to use for production runs. Because the search space of all possible optimized code versions can be huge and brute-force search strategy may not always be feasible, Orio provides various search methods (e.g., random search, simulated annealing) for reducing the search space and thus the empirical testing time. Significant performance gains in real scientific applications (an order of magnitude speedups for plasma physics code and climate modeling applications cite{Chung}) have been realized using the autotuning tools. There are no similar autotuning tools for FPGA design. In order to apply similar principles to FPGA designs, additional research and development is necessary. Significant gaps must be filled to automate the steps required for creating performance models on which the FPGA design decisions can be made. Building FPGA designs (synthesis and implementation) suffers from long execution times resulting in enormous compute time for empirical based autotuning. To be suitable for use in NASA labs, it is necessary to investigate new methodologies for optimizing the turnaround time. New automation tools need to be developed targeting the annotation of HDL/HLS code and FPGA design tool scripts. Finally, it is also required to develop a set of robust easy-to-use tools that interface with the existing FPGA design tools. By filling these gaps, the autotuning techniques from the HPC community can be made desirable and beneficial to NASA FPGA developers and the wider hardware development community. Project description: RNET and ANL are proposing to fully develop an empirical performance optimization tool called OrFPGA that efficiently explores the ``user tunable'' parameter space of an FPGA design and assists in deducing the near optimal design in terms of timing score, device utilization, and power consumption. The tunable parameter space will include IPCore parameters, HDL and HLS code constructs, and parameter settings for the vendor's design tools. Special automation tools will be devleoped to facilitate annotation of HDL/HLS code and design tool scripts. The computational magnitude of empirical performance tuning of FPGA designs will be addressed by novel machine learning based search algorithms (requiring minimal empirical evaluations), computational steering, leveraging intermediate performance analysis results, and parallelization techniques. The tool will support specification of prioritized performance metrics, easy-to-use interfaces for defining the parameter space, and intuitive visualization of performance models. The user will be able to accurately identify the optimal power consumption by including the environmental settings in search space and through real-time power monitoring. The benefits of the tool to NASA will be demonstrated in terms of performance metrics and cost benefits (user productivity) using real NASA designs that are used in space missions. Industry partnership: RNet Technologies, Inc. Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 200000 Q1: 50000 Q2: 50000 Q3: 50000 Q4: 50000 Justification: Storage requirements: Thank You, The LCRC Accounts System

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New LCRC report submitted for orfpga
by support＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2015/?project=orfpga

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New LCRC report submitted for 1atomCat
by support＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2015/?project=1atomCat

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New LCRC report submitted for cdft
by support＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2015/?project=cdft

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[LCRC Accounts] Yearly Allocation Request for ESMF
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Silverio Vasquez Project Name: ESMF Division: MCS Project title: Earth System Modeling Framework Associated funding: DOD, NASA, NSF, NOAA Other Systems: external: NERSC, NCAR, ORNL, NCSA, NASA, NOAA Science: The Earth System Modeling Framework (ESMF) enables the coupling of model components in high performance Earth system modeling codes. ESMF component interfaces have been implemented in most of the prominent climate and weather codes in the U.S.: CESM, NASA GEOS-5 and ModelE, NOAA NEMS, NRL COAMPS and NavGEM, and the GFDL MOM5 ocean. In general, componentization with ESMF has been implemented at the level of major physical domains, where simulated interactions require inter-component data communications (e.g. atmosphere, ocean). Over the years ESMF has evolved and now can be used in multiple ways: 1) to create interoperable component-based modeling systems; 2) as a source of remapping, time management, metadata handling, and other functions; and 3) as a file-based offline generator of interpolation weights for many different kinds of grids. ESMF grid remapping is also available through a Python interface (ESMPy). The recent technical focus has been on refining the Python interface, integrating the observational data representation with grid remapping, and improving grid remapping functions. During this year, work also included extensions to the National Unified Operational Prediction Capability (NUOPC) Layer. The NUOPC Layer is a set of code templates and conventions that increases the interoperability of ESMF-based modeling applications. Project description: ESMF software was part of the Federal Laboratory Consortium Tech Transfer award for the its contribution to the DOE Ultrascale Visualization Climate Data Analysis Toolkit (UV-CDAT). UV-CDAT uses the Python interface to the ESMF grid remapping capability (ESMPy) as its default. ESMPy supports virtually all grids used in current weather and climate applications. In the past year an initial coupled configuration of the NOAA Environmental Modeling System (NEMS) was delivered. NEMS is a coupled modeling system designed to predict the behavior of Earth's environment at a range of time scales. It is built using the ESMF infrastructure software with the NUOPC Layer conventions. NEMS includes a coupler or "mediator" and a collection of model components representing major Earth system domains and processes. On 6/30/2015, the team delivered Unified Global Coupling System(UGCS)-Seasonal 0.1, a three-way coupled GSM-CICE-MOM5 seasonal modeling application. Three-way coupling is implemented through the central NEMS mediator. In addition, the ESMF team supports several ongoing model coupling projects. These include a joint project with CESM, in which HYCOM was introduced as an alternate ocean. Currently ESMF is regression tested daily on Blues with gfortran and intel with mvapich2. On Fusion, ESMF is regression tested with g95/mvapich2, gfortran/mvapich2/mpich2, intel/mvapich2/mpich2 and pgi/mvapich2. The regression test includes: - building the ESMF software and installing it, - building and testing 6 applications, - building and running system tests, examples and unit tests bundled with the software. The entire test suite is run with debug and optimized settings. In addition, two versions of ESMF are regression tested, the head of the development, and a branch based on the last public release where limited development has continued. Industry partnership: Project URL: http://www.earthsystemmodeling.org Current FY Hours Used: undetermined amount New FY Requested allocation: 24000 Q1: 6000 Q2: 6000 Q3: 6000 Q4: 6000 Justification: Storage requirements: Thank You, The LCRC Accounts System

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Re: [allocations-admins] [LCRC Accounts] Project Request: Materials_Genome_SCs
by Bair, Raymond A. 10 Sep '15

10 Sep '15

The project looks OK to me, for 100K hours. P Mat Sci Larry Ciurtiss On 9/10/15, 1:00 PM, "allocations-admins-bounces(a)lcrc.anl.gov on behalf of John Blaas" <allocations-admins-bounces(a)lcrc.anl.gov on behalf of wilshire(a)mcs.anl.gov> wrote: >Antia is quite anxious to start computations and I have already >explained to her that she will need to submit a request for time next >year as well. I see no issue in granting the time. > >- JB > >On Thu, Sep 10, 2015 at 8:35 AM, accounts(a)lcrc.anl.gov ><accounts(a)lcrc.anl.gov> wrote: >> Hello, >> >> A new project on the LCRC cluster has been requested. Please forward >> the information on to the LCRC Allocation sub-committee. >> >> Applicant's name: Antia Sanchez Botana >> Applicant's institution: ANL >> Applicant's division: MSD >> Project Name: Materials_Genome_SCs >> Project title: Materials Genome Approach for Discovery of HTS >> Associated funding: Center for Emergent Superconductivity, an >>Energy >> Frontier Research Center funded by the US DOE, Office of Science >> Other Systems: >> Science: This project uses density functional >>theory-based methods to predict both the crystal and electronic >>structures of candidate correlated electron materials and high >>temperature superconductors. >> Our goal will be to provide a survey and database of potential >>correlated electron materials, with the capability to identify trends, >>project promising directions and explore competing orders giving >>rise/enhancing superconductivity. >> Project description: We intend to create a framework for designing >>novel superconductors combining first principles electronic-structure >>tools, materials databases, and evolutionary algorithms, capable of >>exploring large configurational spaces. >> >> In a first stage, this framework will be applied to transition metal >>nitrochlorides (TNCl (T= Ti, Zr, Hf)) and cuprates. We will use >>different doping mechanisms and atomic substitutions to design novel >>compositions. We will screen for local, configurational and >>thermodynamic stability to isolate new compounds with high probabibility >>of synthesis. >> >> We will intensively use DFT codes such as VASP, WIEN2k and Quantum >>Espresso as well as crystal structure prediction methods that merge ab >>initio total energy calculations and a specific evolutionary algorithm >>like USPEX, CALYPSO and MAISE. These have been proven to be efficient >>for systems with up to 100-200 atoms/cell. Jobs requesting up to 10 >>nodes will be launched for a typical DFT-based calculation. >> >> We plan to run intensive calculations of a 1000 different compounds >>containing up to 150 atoms/cell each. These calculations have proven to >>be efficiently run in related clusters in NERSC. >> >> >> Industry partnership: >> Project URL: >> Requested allocation: 100000 >> Q1: 0 >> Q2: 0 >> Q3: 0 >> Q4: 100000 >> Justification: >> Storage requirements: >> >> >> The requester has used undetermined amount hours of their initial >>startup project. >> >> In addition to approving an initial amount, please specify a Category >> and Subcategory for this project. For a list of the current selection >> of approved categories, please see: >> >> https://wiki.lcrc.anl.gov/wiki/Processes/Categories >> >> Once the Allocation committee has approved the project, please go to >> the Project Management page to create it: >> >> https://accounts.lcrc.anl.gov/projects.php >> >> Thank You, >> The LCRC Accounts System >> _______________________________________________ >> allocations-admins mailing list >> allocations-admins(a)lcrc.anl.gov >> https://lists.lcrc.anl.gov/mailman/listinfo/allocations-admins >_______________________________________________ >allocations-admins mailing list >allocations-admins(a)lcrc.anl.gov >https://lists.lcrc.anl.gov/mailman/listinfo/allocations-admins

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[LCRC Accounts] Yearly Allocation Request for Denso_simulations
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Kaushik Saha Project Name: Denso_simulations Division: ES Project title: Numerical Investigation of Effectiveness of Enriched Nitrogen and Oxygen Using Air Separation Membranes for Internal Combustion Engines Associated funding: WFO for Denso Corporation Other Systems: Science: This project will use an Eulerian-Lagrangian Computation Fluid Dynamics (CFD) software called CONVERGE to model gas exchange, mixture formation, diesel spray formation and combustion characteristics for the proof-of-concept study, utilizing the idea of donut-combustion, with enriched O2 at the core, surrounded by enriched N2. ‘Enriched’ indicates proportion of a specific species, such as, O2 is higher than its proportion in Air. Project description: The project is divided in two parts: In the first part, closed cycle (IVC to EVO) combustion simulations will be carried out to understand the effects of – grid resolution, initial levels of swirl, RPM, start of injection and enriched versus air configurations, on the mixture formation and combustion performance. Sector simulations will be carried out for these parametric studies to provide fundamental understanding, without consuming excessive core-hours. In the second part, open-cycle (including intake, compression, power and exhaust strokes) with full 360⁰ geometry will be carried out to first understand the gas exchange processes between N2 enriched and O2 enriched streams for different combinations of intake and exhaust conditions. With intake and exhaust manifolds included in the computational domain, periodic boundary conditions are no longer valid and therefore, sector simulations will not be good enough to capture the physics of the problem. Once an optimum setting is inferred for gas exchange, spray injection and combustion simulations will be carried out. Multiple cycles will be carried out in the simulations to reach a reasonable convergence to provide representative estimates of the flow-field and the combustion phenomenon. Industry partnership: DENSO Corporation Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 475000 Q1: 175000 Q2: 125000 Q3: 100000 Q4: 75000 Justification: Storage requirements: 3 TB. In the last year 1 TB storage space did not turn out to be sufficient. Considering the large scale open-cycle simulation and the number of runs needed for successful completion of the project it is imperative that we need 3 TB storage space. We already requested to increase our storage space beyond 1 TB and currently we have 3 TB of storage space. Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request for spray-modeling
by accounts＠lcrc.anl.gov 10 Sep '15

10 Sep '15

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Sibendu Som Project Name: spray-modeling Division: ES Project title: Numerical Simulation of Internal and Near-nozzle Flow of a GDI Fuel Injector Associated funding: DOE - Office of Vehicle Technologies Other Systems: Science: project will use the Eulerian mixture multiphase model of Computation Fluid Dynamics (CFD) software, CONVERGE to model internal and near-nozzle two-phase (liquid and vapor) flows of a Gasoline Direct Injection (GDI) nozzle with HPC resources at Argonne. The ECN Spray G conditions will be considered for these simulations. The nominal geometry of the Spray G injector will be used for these simulations. The simulations will be also carried out at different needle lifts to capture the essence of needle transients. Project description: The project is divided in three parts: In the first part, rigorous study will be undertaken to assess the influence of different thermodynamic conditions, model constants and numerical parameters. The extent of flash-boiling, will be correlated with degree of superheat. The homogeneous relaxation model used for simulating the phase change consists of different tuned model constants. It is imperative to carry out an optimization study of model constants for GDI applications. There is also room for further optimization study with the numerical setup of the simulation such as, multigrid vs. successive over-relaxation, full second-order discretization schemes vs. second-order scheme for only momentum equation, and standard vs. RNG k-ε turbulence model. Hole-to-hole variation and plume-to-plume interactions will also be investigated. In the second part, simulations will be carried out at different needle lifts to unravel the transient fluctuations owing to a moving needle during the injection period. For different thermodynamic conditions the simulations of varying needle lifts will be performed to investigate how extent of flashing gets affected by needle movement. For the third-part high fidelity Large Eddy Simulation (LES) will be undertaken to accurately capture the flow transients for a fixed needle lift. Industry partnership: Project URL: http://www.transportation.anl.gov/engines/multi_dim_model_home.html Current FY Hours Used: undetermined amount New FY Requested allocation: 600000 Q1: 150000 Q2: 150000 Q3: 150000 Q4: 150000 Justification: During the past four years, we have been working in close collaborations with the developers of CONVERGE code to ensure that it scales well both on clusters and super-computers. We have implemented METIS algorithm which enables us to load balance significantly better than the original load balancing in CONVERGE. This was the first step towards enabling CONVERGE code for HPC use. In collaboration with researchers at ALCF (during the past couple of years), we have further performed scaling studies with the code on Mira. Based on implementation of MPI-I/O, advancements in chemistry calculation with a stiffness based load balancing criteria and CFD load balancing, we have been able to show good scaling of the CONVERGE code on Mira on up to 4096 processors for a closed cycle engine simulation with about 10 million cells. These advancements were showcased during the VERIFI workshop in Nov. 2014 and we have published a detailed paper highlighting these changes. The a bove changes are not only expected to benefit calculations on Mira but also help on computing clusters like Fusion and Blues. More details and paper can be made available on request. Storage requirements: 3TB Thank You, The LCRC Accounts System

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