September 2016 - Allocations-admins - mailman3.cels.anl.gov

[LCRC Accounts] Project Request: Nuclear_meso
by accounts＠lcrc.anl.gov 06 Sep '16

06 Sep '16

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: Linyun Liang Applicant's institution: ANL Applicant's division: NE Project Name: Nuclear_meso Project title: Mesoscale model for fission-induced recrystallization in U-7Mo alloy Associated funding: DOE Other Systems: Science: Because of the high uranium loading density and stable irradiation behavior, U-Mo alloys are under investigation as candidate fuels for future high-performance research reactors to replace the currently used high-enriched uranium (HEU) fuels with low-enriched uranium (LEU) fuels. However, a major concern about its qualification is the anomalous fuel swelling at high fission density. The contribution to the swelling mainly comes from the formation fission-gas bubbles because of the low solubility of Xe atoms. At high burnups, fission-induced recrystallization, also called grain subdivision, further expedites the formation and growth of gas bubbles. The acceleration of swelling in U-Mo fuels at high fission density may be closely related to the recrystallization process. So far, the exact mechanism of fission-induced recrystallization is not clear. Several complex processes—such as fission gas bubble formation, and grain growth—are simultaneously i nvolved in this phenomenon. A good understanding of the initiation and development of recrystallization from either theory or experiment can effectively delay the grain subdivision process and further reduce the fuel swelling, which will be useful for improving the fuel performance of U-Mo alloys. Project description: We develop a mesoscale model by integrating the rate theory model and phase-field model, and we use our model to study the fission-induced recrystallization in U-7Mo alloy. The recrystallized grains are assumed already nucleated on the grain boundaries with the critical size and concentration of recrystallized nuclei determined by the rate theory model. Experimental observations indicate that fission events can produce a high density of dislocations in metallic fuels, such as U-Mo. The evolution of the dislocation is assumed to be an integral step leading to recrystallization. The increased dislocation density results in a stored energy in the grains, which provides a driving force for the growth of subgrains. The grain growth behavior is then simulated by a multi-phase phase-field model. During recrystallization, the irradiation-induced defects such as the Xe gas atoms, vacancies, and SIAs are introduced in the grain structures. These defects evolve with grain structures simultaneously, in which they either interact with the grain boundary or act as nucleation sites for recrystallization. We will systematically investigate the effect of irradiation conditions, fission defect generation and their recombination rates, sink strength, re-solution rate, grain size and morphology, and the lattice-misfit elastic energy on the recrystallization kinetics. The swelling of U-Mo due to fission gas bubble will also be simulated and compared to experimental observations. Our study aims to provide insight into understanding of the irradiation-induced recrystallization kinetics in U-Mo materials. In our multi-phase phase-field model, one phase parameter describes a single grain. For each phase parameter, we have to solve a highly non-linear and coupled partial differential equation, which is called Allen-Cahn equation. For the newly formed recrystallized grains during the irradiation processes, the new phase parameters are assigned for each sub-grain. The Allen-Cahn equations have to be solved for each phase parameters. The original grains are around 3-15 micrometers, and the recrystallized grains are around several nanometers. For the fully recrystallized fuel, the number of recrystallized grains can be thousand. Thus, we have to solve thousands of coupled Allen-Cahn equations simultaneously. In the meaning time, the diffusion and reactions of Xe gas atoms, vacancies, and SIAs are included in the system, which requires to solve the Cahn-Hilliard equations for each time step. They are coupled with the Allen-Cahn equations and can significantly increase the cost of the simulation time. Our final goal is to simulate the three dimensional system. Therefore, to effectively solve this problem, we will need about 400,000 core hours including the 16,000 core-hour to debug and test the algorithm and code and 384,000 core-hours (4 cores/node * 4 nodes * 30 hours of run time * 800 different runs) for recrystallization kinetics calculations. We will use an in-house code to solve the problem. By using the Fusion and Blues computing resources will greatly accelerated the code development and calculations. In conclusion, the total core-hours requested for this project throughout the FY2017 will be 400,000 core-hours. Industry partnership: Project URL: Requested allocation: 400000 Q1: 100000 Q2: 100000 Q3: 100000 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

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[LCRC Accounts] Project Request: GNR-Au
by accounts＠lcrc.anl.gov 06 Sep '16

06 Sep '16

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: Anh Ngo Applicant's institution: ANL Applicant's division: MSD Project Name: GNR-Au Project title: First-Principles Studies of the superlubricity on metallic substrate Associated funding: Strategic LDRD Other Systems: Science: Superlubricity is an emerging phenomenon where the friction force between the sliding materials is so small that it is almost vanished as compared to a usual stick-slip motion. Although friction is a macroscopic phenomenon, microscopic contacts are the key for the process. So far superlubricity has been demonstrated in macroscopic level including a recent success story of diamond scrolls on graphene by a CNM group [D. Berman et al, Science 348, 1118 (2015)]. Recently, Saw Hla’s group has performed a preliminary work for the lateral force measurement of individual Co-porphyrin molecules (TBrPP-Co) on graphene nanoribbons (GNR) grown on Au(111) surface. Comparison between the required lateral force of the same molecule on Au(111) surface and that on the GNR leads to an exciting finding: Almost zero lateral force is required to move the molecule on GNR while a relatively large force is needed to move the molecule on Au(111). This opens a novel researc h direction to investigate molecular superlubricity on different low dimensional substrates such as silicene, borophene, phosphine and transition metal di-chalcogenides. The project will be realized by collaboration between Hla (CNM, argonne), an STM atom manipulation expert, Guisinger (CNM, argonne), a leader in synthesis of novel 2-dimensional materials, and the theorists Anh Ngo (MSD, argonne) and Peter Zapol (MSD, argonne), who specialize in DFT calculations of molecules on surfaces. Project description: To provide insight into electronic properties of molecular superlubricity we propose to perform a first-principles study for TBrPP-Co molecules adsorbed on graphene nanoribbons (GNR) grown on Au(111) surface. The following calculations will be performed: (a) The initial calculations will focus on finding the stable geometries of TBrPP-Co molecule adsorbed on graphene nanoribbons (GNR) grown on Au(111) surface. The total energies and electronic structure of several oriented structures of GNRs on Au(111) surface will be investigated . This will help improve our understanding of physical properties of molecules adsorbed on GNR grown on Au(111) surface . These theoretical results will be compared with the available STM experiment results. (b) In our further investigations we will calculate the total force as a function of the tip – Molecule distance. We will be able to validate our theoretical results with the ongoing experimental efforts at CNM, Argonne. This work will plan to use the high-performance computing facilities at LCRC. The first-principles calculations will be carried out using the electronic-structure code VASP. The Blues cluster is particularly suitable for this research because the studied systems are of large size, usually around 600-900 atoms. Structure optimizations of the system including a Co-porphyrin molecule deposited on GNRs on Au(111) surface (M/GNRs/Au (111)) with different magnetic configurations and GNRs widths will be carried out. By comparing different Co magnetic ordering we will conclude which configuration corresponding to the ground state energy. Once ground state structure is found we will go on to address the total force as a function of the different tip heights to extract lateral force information. Energy optimization of M/GNRs/Au (111)) (~800 atoms) using GGA+U takes approximately 43008 CPU hours (16*16*168 = 43008) and we plan roughly 17 of such calculations. For total force calculations. The calculation takes roughly 6915 CPU hours (16*6*72) we plan to run 37 of these calculations. So our total request is 16*43008 + 35*(16*6*72)= 930048 core hours. In summary, the total requested allocations for this project is 930048 CPU hours. Industry partnership: Project URL: Requested allocation: 930048 Q1: 250000 Q2: 250000 Q3: 250000 Q4: 180048 Justification: VASP performance on Blues is well established, we use the latest version maintained on Blues. Storage requirements: 1 TB is sufficient 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

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[LCRC Accounts] Yearly Allocation Request for rainfall
by accounts＠lcrc.anl.gov 06 Sep '16

06 Sep '16

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Scott Collis Project Name: rainfall Division: EVS Project title: Developing radar based observations for climate model development Associated funding: DoE-CESD Climate Model Development and Validation (CMDV) Other Systems: Assorted ANL systems (my own 32 core machine). Systems at OLCF used for formal ARM work (open cluster with no quota system..) Science: The primary goal of the CMDV LLNL/ANL/BNL consortium project is to investigate if a very high resolution climate model (The DoE ACME model with regionally refined meshes at 10km) can reproduce mesoscale flows and features (~50km) that we observe in nature. Argonne's role is to create tailored retrievals and analyses to feed into a metrics based validation system. We will be focusing on the decadal record of radar data from the Australian Bureau of Meteorology's C-Band Radar in Darwin Australia. Project description: The project will use the Python-ARM Radar Toolkit (Py-ART) to ingest, correct and retrieve rainfall rates from dual polarimetric radars. Since time steps (~10 minutes) are independent the methodology is pleasantly parallel. We use the IPCluster toolkit (part of Project Jupyter) to map the radar problem to the cluster. IPCluster manages execution at the nodes and our software manages the saving and reducing of data. This project builds directly on past IP developed from the Rainfall project, we are merely switching from looking at NEXRAD radars for the purposes of hydrology to looking at C-Band radars for the purposes of climate model development. The Py-ART codebase is fairly efficient with higher demand components written in C and fortran. It is, however, memory intensive with 2-4GB per job needed. Fortunately this is perfect for the configuration of Blues. Most importantly Py-ART is thread-safe. We have already mapped the radar problem to Blues using 1000+ cores effectively due to the performance of the GPFS, Radically reducing the time to retrieve the rainfall rates. We will be using this allocation to work through many years (10+) of radar data, roughly a million radar "Volumes". In addition we will be using our allocation for analyzing the results and experimenting with novel new ways of characterizing the volume data such as feature tracking. the overwhelming majority of core hours will be used by jobs with greater than 100 cores. The team consists of myself and a newly hired postdoc, Robert Jackson. Industry partnership: Project URL: http://climatemodeling.science.energy.gov/news/new-funding-opportunity-anno… Current FY Hours Used: undetermined amount New FY Requested allocation: 60000 Q1: 10000 Q2: 20000 Q3: 10000 Q4: 20000 Justification: Storage requirements: None, we have paid for a large storage system Thank You, The LCRC Accounts System

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New LCRC report submitted for Electric_Interface
by support＠lcrc.anl.gov 06 Sep '16

06 Sep '16

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2016/?project=Electric_Inte…

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[LCRC Accounts] Yearly Allocation Request for CEES-II-Wolverton
by accounts＠lcrc.anl.gov 03 Sep '16

03 Sep '16

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Zhenpeng Yao Project Name: CEES-II-Wolverton Division: Computing, Environment, and Life Sciences Project title: DFT Computation of Hybrid and Li-ion Battery Materials Associated funding: CEES-II EFRC Other Systems: Science: The expanded use of renewable but intermittent energy sources coupled with increasing demand for electric transportation vehicles, greatly enhanced the need for advanced energy storage technologies with high energy density and long cycle life. Lithium ion battery (LIB) has been the most prominent electrochemical energy storage technology over the past decades and enabled the wireless evolution of portable electronic devices. Therefore, the development of new high energy electrode materials for rechargeable lithium batteries is becoming more essential. We are interested in several different high energy density lithium ion battery systems including the traditional conversion materials and the novel hybrid Li-ion/Li-O2 materials. The conversion-type materials (e.g., Co3O4 and NiO) can offer significantly larger capacities because of their abilities to make use of all possible valence states of the metal cations, but also it may pose significant kinetic challenges (e.g. , reversibility and voltage hysteresis). At the same time, the hybrid materials (like Li5FeO4) can realize high capacities by releasing and re-accommodating lithium and oxygen reversibly during charging and discharging, which also suffer from cyclability problems. Density functional theory (DFT) has been facilitating lithium-ion battery design and improvement by enabling predictions of operation voltage, phase diagram, lithium-ion transportation and so on for various electrode materials. Using LCRC, we will be able to provide new mechanisms and clarify discrepancies in previous experimental/computational studies on traditional conversion materials and the novel hybrid Li-ion/Li-O2 materials. Project description: Using computational resources from LCRC, we succeeded to predict new candidates for the hybrid Li-ion/Li-O2 materials by the high throughput DFT method. We will continue this project to identify the mechanisms behind the poor cyclability of current hybrid material (Li5FeO4). We will focus on studying the phase evolution during the charge and discharge of current hybrid material and look into the thermodynamic/kinetic obstacles. With computational support from LCRC, we have developed an accurate, predictive computational approach for the metastable lithiation process of a conversion material: Co3O4 for lithium-ion batteries. Based on this method coupled with in-situ transmission electron microscopy (TEM) observations, we have unraveled the detailed lithiation reaction pathway of Co3O4 by systematically exploring the energetics of stable and metastable LixCo3O4 structures using first-principle calculations. Based on what we have learned from Co3O4, we plan to apply our method to other conversion material systems in order to obtain a more general understanding of the detailed mechanism for conversion materials during charging and discharging. Our study could help future experiments to overcome the current limitations of the conversion-type electrode materials and promote the development of more advanced LIBs. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 2165760 Q1: 541440 Q2: 541440 Q3: 541440 Q4: 541440 Justification: To identify the kinetic obstacles during the charge and discharge of current hybrid material, defect formation energies and ionic migration barriers would be evaluated for all the intermediate phases (~5). Considering the number of defect types (~6), charge states (~3), geometrically different configurations (~4), and an averaged relaxation computation time of 96 cores for 24, defect related calculations would consume 5*6*3*4*96*24 = ~829,440 CPU hours. Ionic migration barrier calculations are required for all the intermediate phases (~5) considering different types of ions (~3), geometrically different pathways (~4), and an averaged nudged elastic band theory computation time of 96 cores for 72, 5*3*4*96*72 = ~414,720 CPU hours are needed. The phase prediction method used for studying the conversion reactions is effective yet computational resource consuming. For each material system we planned to work on, a series of stoichiometries (~20) along the lithiation/delithiation process will be considered. For each of these stoichiometries, there could be up to ten thousands of geometrical configurations. To save computational time, we rank all the configurations with their electrostatic energies and select lowest ones (~4) to be relaxed in DFT. The relaxation for each of them usually needs 96 cores for 24 hours on average. We will choose several typical conversion materials (~5) to study which will spend: 5*20*4*96*24 = ~921,600 CPU hours in total. To summarize, we request a total amount of 2,165,760 CPU hours in the next fiscal year (~541,440/Qtr.). Storage requirements: Thank You, The LCRC Accounts System

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New LCRC report submitted for GAS_injection
by support＠lcrc.anl.gov 03 Sep '16

03 Sep '16

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2016/?project=GAS_injection

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[LCRC Accounts] Yearly Allocation Request for gcmat
by accounts＠lcrc.anl.gov 02 Sep '16

02 Sep '16

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Jonathan Ozik Project Name: gcmat Division: GSS Project title: Global Critical Materials Modeling Associated funding: DOE Office of Energy Policy and Systems Analysis DOD Defense Logistics Agency Other Systems: Science: This project uses adaptive simulation methods to compute sensitivity and uncertainty ranges for predicting global price and production of critical materials needed for clean energy technologies. Project description: We will continue to use agent-based modeling to study the effects of producer and consumer decisions on market dynamics for rare earth elements. We also intend to understand the effects of critical parameters on model outcomes. We will continue to use Repast Simphony for agent-based modeling and the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for constructing large-scale adaptive workflows. Our goal is to continue to advance methods for adaptive simulation experiments that will be widely applicable to many kinds of models. We will carry out parameter sweeps over 100k+ to 1M+ parameter combinations, determining regions of parameter space that exhibit the best fit to empirical data, including historical prices of critical materials, and parameter sensitivity. The adaptive simulation workflows will allow us to strategically explore large parameter spaces, dynamically generating parameter combinations based on results of distributed simulation runs, using evolutionary algorithms and approximate Bayesian computation. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 240000 Q1: 60000 Q2: 60000 Q3: 60000 Q4: 60000 Justification: Storage requirements: The project generates large amount of data in the form of simulation outputs. Outputs from large parameter sweeps involving millions of simulation runs quickly add up. This data needs to be stored for analysis and for joint access by the project members. As such, a 5TB storage allocation would be very helpful. Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request for dmft_for_oxides
by accounts＠lcrc.anl.gov 02 Sep '16

02 Sep '16

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Hyowon Park Project Name: dmft_for_oxides Division: MSD Project title: DFT+DMFT Calculations of Transition Metal Oxides Associated funding: DOE Division of Basic Energy Sciences Other Systems: Science: This project aims to study electronic, magnetic and structural properties of transition metal oxides in both bulk and artificial structures including heterostructures and thin films. For this project, we use an ab-initio computational approach to describe materials with strongly correlated electrons. We study novel phases of these materials including the Mott insulating state, novel magnetism, and ferroelectricity as functions of different strain, dimensionality, pressure, and temperature. Our project can provide theoretical guides to design new functional materials in artificial structures which have been possible due to advanced experimental synthesis techniques, and can lead to the application to novel electronic or energy-related devices. Project description: A DFT+DMFT code consists of a DFT part and a DMFT part. Most of modern DFT codes are efficiently parallelized such that simulations of large supercells can be performed. A DMFT calculation requires an accurate convergence of the quantum Monte Carlo method. The quantum Monte Carlo simulation is scalable almost linearly as the number of cpus. These codes can be compiled using the Intel compilers and MPI which are already installed in LCRC machines. Therefore, the parallel calculation using the clusters in LCRC will be inevitable for performing precise and efficient DFT+DMFT simulations. During FY 2016-17, the PIs will perform DFT+DMFT calculations to study an accurate description of strongly correlated materials including the ground-state structure, total energy, spectral functions, and magnetism. Specifically, we will study the strong interplay of spin-lattice-charge couplings in various materials including LaCoO3, nickelate superlattices, ferrites, and uranium dioxides. The total energy calculations and the new implementation of atomic force calculations will be adopted for the study of strucutral properties in these materials. These DFT+DMFT outputs can be directly compared to various spectroscopic experiments such as the photoemission, X-ray scattering, and neutron scattering experiments. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 480000 Q1: 120000 Q2: 120000 Q3: 120000 Q4: 120000 Justification: Storage requirements: Thank You, The LCRC Accounts System

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[LCRC Accounts] Yearly Allocation Request for radix
by accounts＠lcrc.anl.gov 02 Sep '16

02 Sep '16

Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Pavan Balaji Project Name: radix Division: MCS Project title: Scalable Parallel System Software Associated funding: DoE core, SciDAC, NSF, FASTOS Other Systems: breadboard (unlimited) ALCF Mira/Cetus/Tukey (50,000,000 cpu hours) Science: Our mission is to develop the technologies required to dramatically increase the productivity of scientists developing applications for parallel supercomputers. The focus is fourfold: integration of parallel programming tools, reuse of parallel program components, development of scientific computing toolkits and portable libraries, and exploration of requirements of future parallel computers. Project description: This allocation will be used for optimizations to various systems software components: (1) programming models and runtime systems, (2) data I/O and file systems, (3) fault tolerance, (4) operating systems, and (5) data analysis and visualization. Primary goal of the project is to perform research on improving the core systems software on high-end computing platforms. The radix allocation covers a large number of cross-cutting projects involving subsets of researchers in the group. A few core projects are presented here, but some of the techniques used are shared between them and are not easily separable (e.g., MPI improvements or resilience improvements): 1. Fault Tolerance Techniques: - Local memory management * Advanced SDC Detection through Data Monitoring + Adapt the prediction techniques used in the SZ lossy compressor for SDC detection * Checkpoint size reduction to improve checkpoint/restart performance and energy efficiency + Adapt FTI for using lossy compression of checkpoints * Impact of lossy compression on Checkpoint/restart + Investigate fundamentally and experimentally the relation between discretization/truncation error with lossy compression error * End-to-end Detection of SDCs in workflows featuring a lossy compression stage + Compare combination of SDC detectors and lossy compressors with respect to the accuracy of SDC detection. - Distributed memory management * Integration of checkpointing in workflow environments + Adapt DECAF and/or SWIFT with FTI and perform performance eveluation * MPI Fault Tolerance + Improvements to MPICH to deal with faults * Resilient-MPI and FTI Integration + Experimentation and evaluation of FTI coupled with resilient-MPI 2. Lightweight, topology-aware, and thread-safe communication: - Improvements to MPI+threads * MPICH improvements to play well with threads, including improved locking and lock-free data structures. * Communication progress improvements in the presence of threads. - Lightweight communication * Low instruction-count and low-overhead irregular data movement. - Topology-aware communication * Optimizations to MPI virtual topology functionality 3. Data I/O, RMA and Active Messages: - Dynamic Locality-Aware Workflows * Dynamically distribute the tasks among nodes using Asynchronous Dynamic Load Balancer - Development and Evaluation of Composable Data Services * Exploration of lightweight, customized, domain-specific storage services to improve the performance of data-intensive HPC applications - High Performance Parallel I/O * The ROMIO MPI-IO implementation provides the foundation for many components of the HPC I/O stack. Continue tuning, debugging, and research efforts. 4. Data analysis and visualization: Testing and benchmarking of improvements to data movement infrastructure such as DIY and Decaf software to prepare for next-generation machine architectures and to apply those improvements to applications across the lab that use those software platforms, such as cosmology, climate science, and materials science. The following areas will be tested on Fusion and Blues: - Relaxed BSP synchronization (e.g., for iterative algorithms, to overlap communication with computation and support irregular communication/computation patterns) * irregular communication patterns + short-circuit message completion - Load-balancing algorithms (work stealing, adaptive decompositions) for unstructured time-varying mesh partitions (e.g., reactor design) * work stealing algorithms + adaptive (e.g. k-d tree) domain decompositions - Integration with many-core thread models * Relaxed BSP communication with interchangeable thread models + Worker thread pool for executing block callback functions - Application domain decompositions * Add support for AMR (block-based and patch-based combustion and astrophysics codes) + Add support for directly using existing simulation decompositions (e.g., in climate codes) Industry partnership: Project URL: http://www.mcs.anl.gov/group/pmrs/ Current FY Hours Used: undetermined amount New FY Requested allocation: 2000000 Q1: 500000 Q2: 500000 Q3: 500000 Q4: 500000 Justification: 2M core hours requested: We have four core projects. All projects have been demonstrated at scale in the previous years and form the core of the system software stack used in production on the Fusion and Blues machines. We expect to run around ~40 studies for each project on Blues and on Fusion, scaling from 1 node to 256 nodes (average runtime of 1 hour) - Blues: 4 (projects) x 40 (studies) x 512 (1 to 256 nodes) x 16 (cores) x 1 (hour) = 1.3M - Fusion: 4 (projects) x 40 (studies) x 512 (1 to 256 nodes) x 8 (cores) x 1 (hour) = 655K A small fraction of this would be used for debugging. Storage requirements: No large storage requirements, since the data generated will not be persistent. Thank You, The LCRC Accounts System

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New LCRC report submitted for rainfall
by support＠lcrc.anl.gov 02 Sep '16

02 Sep '16

Full project report at https://accounts.lcrc.anl.gov/reporting/project/2016/?project=rainfall

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