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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The Next Frontier in Computing

Exascale computing refers to computing systems capable of at least one exaflop or a billion calculations per second (1018). That is 50 times faster than the most powerful supercomputers being used today and represents a thousand-fold increase over the first petascale computer that came into operation in 2008. How we use these large-scale simulation resources is the key to solving some of today’s most pressing problems, including clean energy production, nuclear reactor lifetime extension and nuclear stockpile aging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

UCI Final Report for SciDAC GPS-TTBP (3/1/2008-2/28/2011) Gyrokinetic Particle Simulation of Turbulent Transport in Burning Plasmas

“We propose to further develop the global, particle-in-cell, gyrokinetic toroidal code (GTC) for simulations of plasma turbulence and transport, and to advance understanding and our ability to control radial transport of heat, momentum, and particles in magnetically confined plasmas with temperatures up to those required for nuclear fusion power production regimes called burning plasmas. Building on the excellent physics capabilities and high performance computing, GTC will be extensively applied to simulate experimentally relevant parameter regimes including electromagnetic fluctuations, kinetic electrons, multiple ion species, realistic collision operators, and shaped plasmas. Collaborative code development will be pursued to accelerate implementations of new physics capabilities including long time steady state simulation with profile evolution, and core-edge coupling. The new simulation capabilities and access to tera- and petascale computers will allow us to address new physics and parameter regimes important for burning plasma experiments such as International Thermonuclear Experimental Reactor (ITER).” Among the three key goals and objectives (code development, comprehensive physics simulation, and core-edge coupling), we have achieved fully the first two goals, namely, 1. “to further develop the global, particle-in-cell, gyrokinetic toroidal code (GTC) for simulations of plasma turbulence and transport, and to advance understanding and our ability to control radial transport of heat, momentum, and particles in magnetically confined plasmas with temperatures up to those required for nuclear fusion power production regimes called burning plasmas.” 2. “GTC will be extensively applied to simulate experimentally relevant parameter regimes including electromagnetic fluctuations, kinetic electrons, multiple ion species, realistic collision operators, and shaped plasmas.” Regarding the third objective, 3. “Collaborative code development will be pursued to accelerate implementations of new physics capabilities including long time steady state simulation with profile evolution, and core-edge coupling.” While we have productively engaged in collaborative code development and made satisfactory progress on implementing new physics including long time steady state simulation with profile evolution, we have not achieved the goal of core-edge coupling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Construction of Blaze at the University of Illinois at Chicago: A Shared, High-Performance, Visual Computer for Next-Generation Cyberinfrastructure-Accelerated Scientific, Engineering, Medical and Public Policy Research

The Blaze high-performance visual computing system serves the high-performance computing research and education needs of University of Illinois at Chicago (UIC). Blaze consists of a state-of-the-art, networked, computer cluster and ultra-high-resolution visualization system called CAVE2(TM) that is currently not available anywhere in Illinois. This system is connected via a high-speed 100-Gigabit network to the State of Illinois' I-WIRE optical network, as well as to national and international high speed networks, such as the Internet2, and the Global Lambda Integrated Facility. This enables Blaze to serve as an on-ramp to national cyberinfrastructure, such as the National Science Foundation’s Blue Waters petascale computer at the National Center for Supercomputing Applications at the University of Illinois at Chicago and the Department of Energy’s Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. DOE award # DE-SC005067, leveraged with NSF award #CNS-0959053 for “Development of the Next-Generation CAVE Virtual Environment (NG-CAVE),” enabled us to create a first-of-its-kind high-performance visual computing system. The UIC Electronic Visualization Laboratory (EVL) worked with two U.S. companies to advance their commercial products and maintain U.S. leadership in the global information technology economy. New applications are being enabled with the CAVE2/Blaze visual computing system that is advancing scientific research and education in the U.S. and globally, and help train the next-generation workforce.

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

Collaborative Research: Continuous Dynamic Grid Adaptation in a Global Atmospheric Model - Application and Refinement

This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the "physics" of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited. 3a. EULAG Advances EULAG is a non-hydrostatic, parallel computational model for all-scale geophysical flows. EULAG's name derives from its two computational options: EULerian (flux form) or semi-LAGrangian (advective form). The model combines nonoscillatory forward-in-time (NFT) numerical algorithms with a robust elliptic Krylov solver. A signature feature of EULAG is that it is formulated in generalized time-dependent curvilinear coordinates. In particular, this enables grid adaptivity. In total, these features give EULAG novel advantages over many existing dynamical cores. For EULAG itself, numerical advances included refining boundary conditions and filters for optimizing model performance in polar regions. We also added flexibility to the model's underlying formulation, allowing it to work with the pseudo-compressible equation set of Durran in addition to EULAG's standard anelastic formulation. Work in collaboration with others also extended the demonstrated range of validity of soundproof models, showing that they are more broadly applicable than some had previously thought. Substantial testing of EULAG included application and extension of the Jablonowski-Williamson baroclinic wave test - an archetype of planetary weather - and further analysis of multi-scale interactions arising from collapse of temperature fronts in both the baroclinic wave test and simulations of the Held-Suarez idealized climate. These analyses revealed properties of atmospheric gravity waves not seen in previous work and further demonstrated the ability of EULAG to simulate realistic behavior over several orders of magnitude of length scales. Additional collaborative work enhanced capability for modeling atmospheric flows with adaptive moving meshes and demonstrated the ability of EULAG to move into petascale computing. 3b. CAM-EULAG Advances We have developed CAM-EULAG in collaboration with former project postdoc, now University of Cape Town Assistant Professor, Babatunde Abiodun. Initial study documented good model performance in aqua-planet simulations. In particular, we showed that the grid adaptivity (stretching) implemented in CAM-EULAG allows higher resolution in selected regions without causing anomalous behavior such as spurious wave reflection. We then used the stretched-grid version to analyze simulated extreme precipitation events in West Africa, comparing the precipitation and event environment with observed behavior. The model simulates fairly well the spatial scale and the interannual and intraseasonal variability of the extreme events, although its extreme precipitation intensity is weaker than observed. In addition, both observations and the simulations show possible forcing of extreme events by African easterly waves. 3c. Other Contributions Through our collaborations, we have made contributions to a wide range of outcomes. For research focused on terrestrial behavior, these have included (1) upwind schemes for gas dynamics, (2) a nonlinear perspective on the dynamics of the Madden-Julian Oscillation, (3) numerical realism of thermal convection, (4) extreme precipitation processes, (5) unstructured meshes, (6) porous-media analogies for simulating flow in urban environments, (7) Monge-Ampere enhancements of semi-Lagrangian methods and (8) further extensions of the generality of EULAG applications to geophysical problems. The generality of EULAG for multi-scale flow was also demonstrated through solar applications. Simulations of solar magnetohydrodynamics showed that EULAG produces the solar cycle fairly well. Although the model's solar cycle is significantly longer than observed, it shows remarkable agreement with observations in features of spatial evolution. These efforts in environments that are extreme from a geophysical perspective have helped to establish the robustness of EULAG as an all-scale code for geophysical applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Permutationally Invariant Polynomial Expansions with Unrestricted Complexity

A general strategy is presented for constructing and validating permutationally invariant polynomial (PIP) expansions for chemical systems of any stoichiometry. Demonstrations are made for three categories of gas-phase dynamics and kinetics: collisional energy-transfer trajectories for predicting pressure-dependent kinetics, three-body collisions for describing transient van der Waals adducts relevant to atmospheric chemistry, and nonthermal reactivity via quasiclassical trajectories. In total, 30 systems are considered with up to 15 atoms and 39 degrees of freedom. Permutational invariance is enforced in PIP expansions with as many as 13 million terms and 13 permutationally distinct atom types by taking advantage of petascale computational resources. The quality of the PIP expansions is demonstrated through the systematic convergence of in-sample and out-of-sample errors with respect to both the number of training data and the order of the expansion, and these errors are shown to predict errors in the dynamics for both reactive and nonreactive applications. Here, the parallelized code distributed as part of this work enables the automation of PIP generation for complex systems with multiple channels and flexible user-defined symmetry constraints and for automatically removing unphysical unconnected terms from the basis set expansions, all of which are required for simulating complex reactive systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Methodology for the Simulation of Molecular Motors at Different Scales

Millisecond-scale conformational transitions represent a seminal challenge for traditional molecular dynamics simulations, even with the help of high-end supercomputer architectures. Such events are particularly relevant to the study of molecular motors—proteins or abiological constructs that convert chemical energy into mechanical work. Here, we present a hybrid-simulation scheme combining an array of methods including elastic network models, transition path sampling, and advanced free-energy methods, possibly in conjunction with generalized-ensemble schemes to deliver a viable option for capturing the millisecond-scale motor steps of biological motors. The methodology is already implemented in large measure in popular molecular dynamics programs, and it can leverage the massively parallel capabilities of petascale computers. The applicability of the hybrid method is demonstrated with two examples, namely cyclodextrin-based motors and V-type ATPases.

Chemistry↗
DOE OSTI2013

A lightweight I/O scheme to facilitate spatial and temporal queries of scientific data analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data postprocessing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Tian, Yuan↗
DOE OSTI2014

A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations

Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N 3 ) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix, based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Institute for Scalable Application Development Software

Work by the University of Wisconsin as part of the DOE SciDAC CScADS includes the following accomplishments: Research on tool componentization, with concentration on the: InstructionAPI and InstructionSemanticsAPI ParseAPI DataflowAPI Co-organized a series of high successful workshops with Prof. John Mellor-Crummey, Rice University, on Performance Tools for Petascale Computing, held in Snowbird, Utah and Lake Tahoe, California in July or August of 2007 through 2012. Investigated the use of multicore in numerical libraries Dyninst porting to 32- and 64bit Power/PowerPC (including BlueGene) and 32- and 64-bit Pentium platforms. Applying our toolkits to advanced problems in binary code parsing associated with dealing with legacy and malicious code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Wedgehog Manual and Interface Guide, Jayenne-7_0_0

Wedgehog is an IMC package developed by the CCS–2’s Monte Carlo Team1 . Wedgehog is designed to interface with a time-split radiation-hydrodynamics (rad-hydro) code and supports one-dimensional R, two-dimensional RZ or three dimensional XY Z geometry. The geometry is confined to logically rectangular Adaptive Mesh Refinement (AMR) meshes with unit aspect ratios and 2:1 refinement between adjacent cells. Wedgehog supports both two and three temperature simulations. Available scattering models for photon-electron interaction include Compton and isotropic scattering. It maintains a census of particles in between time steps to preserve the contents of the radiation field. Wedgehog can accommodate frequency dependence by using a “Multigroup” (MG) approximation that essentially averages the frequency-dependent opacity information into discrete bins. Additionally, Wedge hog includes a capability to use Opacity Distribution Factors (ODFs) that may be generated with the XCP-5 code TOPS. A previous version of Wedgehog that utilizes IBM’s Cell chip is available for the Roadrunner petascale computer at Los Alamos National Laboratory (circa 2006-2013, estimated). However, as Roadrunner is going out of service in the near future, we have dropped support for the interfaces that were deprecated in Jayenne 6 4 0 and removed in Jayenne-7 0 0. The Roadrunner version had the same interface as the mainline code and is available in RZ and XYZ geometry with all the same features except for flux tallies across a surface (i.e., tally sphere and RZ line-segment).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Accurate Numerical Simulations Of Chemical Phenomena Involved in Energy Production and Storage with MADNESS and MPQC: ALCF-2 Early Science Program Technical Report

In order to solve the electronic structure of large molecular systems on petascale computers using MADNESS, a numerical tool kit, are required fast and accurate implementations for linear algebra. MADNESS uses multiresolution analysis and low separation rank which translates high dimensional functions in tensor products using Legendre polynomial. The multiple tensor products make to the singular value decomposition and matrix multiplication the most intense operations in MADNESS. This work discusses the interfacing of Eigen3 as a C++ substitute of LAPACK and introduces Elemental for the diagonalization of large matrices. Furthermore, the present paper shows the performance these libraries on Blue Gene/ Q.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗
DOE OSTI2015

Active Storage with Analytics Capabilities and I/O Runtime System for Petascale Systems

Computational scientists must understand results from experimental, observational and computational simulation generated data to gain insights and perform knowledge discovery. As systems approach the petascale range, problems that were unimaginable a few years ago are within reach. With the increasing volume and complexity of data produced by ultra-scale simulations and high-throughput experiments, understanding the science is largely hampered by the lack of comprehensive I/O, storage, acceleration of data manipulation, analysis, and mining tools. Scientists require techniques, tools and infrastructure to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis, statistical analysis and knowledge discovery. The goal of this work is to enable more effective analysis of scientific datasets through the integration of enhancements in the I/O stack, from active storage support at the file system layer to MPI-IO and high-level I/O library layers. We propose to provide software components to accelerate data analytics, mining, I/O, and knowledge discovery for large-scale scientific applications, thereby increasing productivity of both scientists and the systems. Our approaches include 1) design the interfaces in high-level I/O libraries, such as parallel netCDF, for applications to activate data mining operations at the lower I/O layers; 2) Enhance MPI-IO runtime systems to incorporate the functionality developed as a part of the runtime system design; 3) Develop parallel data mining programs as part of runtime library for server-side file system in PVFS file system; and 4) Prototype an active storage cluster, which will utilize multicore CPUs, GPUs, and FPGAs to carry out the data mining workload.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Distributed-memory Parallel Algorithms for Matching and Coloring

Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization, in concert. We present preliminary results on weak and strong scalability studies conducted on an IBM Blue Gene/P machine employing up to tens of thousands of processors. The results show that the algorithms hold strong potential for computing at petascale.

Combinatorial scientific computing↗
DOE OSTI2011

The Argonne Leadership Computing Facility 2010 annual report.

Researchers found more ways than ever to conduct transformative science at the Argonne Leadership Computing Facility (ALCF) in 2010. Both familiar initiatives and innovative new programs at the ALCF are now serving a growing, global user community with a wide range of computing needs. The Department of Energy's (DOE) INCITE Program remained vital in providing scientists with major allocations of leadership-class computing resources at the ALCF. For calendar year 2011, 35 projects were awarded 732 million supercomputer processor-hours for computationally intensive, large-scale research projects with the potential to significantly advance key areas in science and engineering. Argonne also continued to provide Director's Discretionary allocations - 'start up' awards - for potential future INCITE projects. And DOE's new ASCR Leadership Computing (ALCC) Program allocated resources to 10 ALCF projects, with an emphasis on high-risk, high-payoff simulations directly related to the Department's energy mission, national emergencies, or for broadening the research community capable of using leadership computing resources. While delivering more science today, we've also been laying a solid foundation for high performance computing in the future. After a successful DOE Lehman review, a contract was signed to deliver Mira, the next-generation Blue Gene/Q system, to the ALCF in 2012. The ALCF is working with the 16 projects that were selected for the Early Science Program (ESP) to enable them to be productive as soon as Mira is operational. Preproduction access to Mira will enable ESP projects to adapt their codes to its architecture and collaborate with ALCF staff in shaking down the new system. We expect the 10-petaflops system to stoke economic growth and improve U.S. competitiveness in key areas such as advancing clean energy and addressing global climate change. Ultimately, we envision Mira as a stepping-stone to exascale-class computers that will be faster than petascale-class computers by a factor of a thousand. Pete Beckman, who served as the ALCF's Director for the past few years, has been named director of the newly created Exascale Technology and Computing Institute (ETCi). The institute will focus on developing exascale computing to extend scientific discovery and solve critical science and engineering problems. Just as Pete's leadership propelled the ALCF to great success, we know that that ETCi will benefit immensely from his expertise and experience. Without question, the future of supercomputing is certainly in good hands. I would like to thank Pete for all his effort over the past two years, during which he oversaw the establishing of ALCF2, the deployment of the Magellan project, increases in utilization, availability, and number of projects using ALCF1. He managed the rapid growth of ALCF staff and made the facility what it is today. All the staff and users are better for Pete's efforts.

54 ENVIRONMENTAL SCIENCES↗