The Computer Science and Mathematics Division (CSMD) is ORNL's premier source of basic and applied research in high-performance computing, applied mathematics, and intelligent systems. Basic and applied research programs are focused on computational sciences, intelligent systems, and information technologies.
Our mission includes working on important national priorities with advanced computing systems, working cooperatively with U.S. Industry to enable efficient, cost-competitive design, and working with universities to enhance science education and scientific awareness. Our researchers are finding new ways to solve problems beyond the reach of most computers and are putting powerful software tools into the hands of students, teachers, government researchers, and industrial scientists.
Oak Ridge National Laboratory to Co-Lead DOE's New HPC for Manufacturing Program (September 24, 2015)
Oak Ridge National Laboratory is collaborating with Lawrence Livermore and Lawrence Berkeley National Laboratories (LLNL and LBNL) to lead a new US Department of Energy program designed to fund and foster public-private R&D projects that enhance US competitiveness in clean energy manufacturing.
"The Manufacturing Demonstration Facility has worked with numerous industry partners to overcome challenges in areas of advanced manufacturing, and ORNL is excited by the prospect of extending and accelerating this success through modeling and simulation," said John Turner, Group Leader for Computational Engineering and Energy Sciences and ORNL lead for HPC4Mfg. He added, "We look forward to collaborating with colleagues at LLNL and LBNL, and with industry partners, to apply our computational expertise to challenging clean energy manufacturing problems." Read the article [here].
ACME continues to soar with yet another award (September 15, 2015)
Launched in 2014, ACME is a multi-laboratory initiative to harness the power of supercomputers like ORNL's Titan and Argonne's Mira to develop fully coupled state-of-the-science Earth system models for climate change research and scientific and energy applications. Eight national labs, the National Center for Atmospheric Research, four academic institutions, and one private sector company are collaborating on the 10-year project. Researchers from ORNL's Climate Change Science Institute (CCSI) were instrumental in developing and defending the project plan and are leading or co-leading several project teams, including the teams responsible for new land model development (Peter Thornton), assessing and improving model performance on high-performance computing platforms (Patrick Worley), and developing and evaluating simulation workflow tools (Kate Evans).
CSMD staff mentor HVA student (September 15, 2015)
Harding Valley Academy's Feldman ready to shed light on climate change
Following an internship at Oak Ridge National Laboratory (where he was mentored by CSMD's Kate Evans), 18-year-old Sam Feldman not only has chosen his profession - he's passionate about helping to reverse the effects of man-made climate change.
"That's when I first found out about climate change research, and that got me interested," Feldman, a Hardin Valley Academy 2015 graduate and advanced placement honors student, said. "All the data we studied pointed toward it being a reality. And the fact that ice sheets are melting [at the earth's poles]. ... I specifically studied ice sheets so I learned a lot about how they are currently melting."
Pavel Shamis (ORNL) and Gilad Shainer (Mellanox) announce the UCX Unified Communication X Framework. (September 15, 2015)
UCX is a collaboration between industry, laboratories, and academia to create an open-source production grade communication framework for data centric and HPC applications
Jay Jay Billings Interview (September 15, 2015)
CSMD researcher Jay Jay Billings was the subject of the latest User Spotlight interview which is part of the Eclipse Newsletter. The Eclipse technology is a vendor-neutral, open development platform supplying frameworks and exemplary, extensible tools (the "Eclipse Platform").
To read the full interview please go here - To read the full interview please go [here].
Safer Batteries through Coupled Multiscale Modeling
John Turner, Srikanth Allu, Mark Berrill, Wael Elwasif, Sergiy Kalnaus, Abhishek Kumar, Damien Lebrun-Grandie, Sreekanth Pannala, and Srdjan Simunovic
Batteries are highly complex electrochemical systems, with performance and safety governed by coupled nonlinear electrochemical-electrical-thermal-mechanical processes over a range of spatiotemporal scales. We describe a new, open source computational environment for battery simulation known as VIBE - the Virtual Integrated Battery Environment. VIBE includes homogenized and pseudo-2D electrochemistry models such as those by Newman-Tiedemann-Gu (NTG) and Doyle-Fuller-Newman (DFN, a.k.a. DualFoil) as well as a new advanced capability known as AMPERES (Advanced MultiPhysics for Electrochemical and Renewable Energy Storage). AMPERES provides a 3D model for electrochemistry and full coupling with 3D electrical and thermal models on the same grid. VIBE/AMPERES has been used to create three-dimensional battery cell and pack models that explicitly simulate all the battery components (current collectors, electrodes, and separator). The models are used to predict battery performance under normal operations and to study thermal and mechanical response under adverse conditions.
This work was performed at ORNL and sponsored by DOE's EERE office.
Scalable and Fault Tolerant Failure Detection and Consensus
A. Katti, G. Di Fatta, T. Naughton, and C. Engelmann
Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum's User Level Failure Mitigation proposal has introduced an operation (MPI_Comm_shrink) to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. The MPI_Comm_shrink operation requires a fault tolerant failure detection and consensus algorithm. This work developed two novel failure detection and consensus algorithms to support this operation. The algorithms are based on Gossip protocols and are inherently fault-tolerant and scalable. The first algorithm is based on global knowledge: each process maintains a local view of the entire system state to achieve consensus on failed processes. A Gossip protocol is used to detect failures and to exponentially propagate them in the system until the local views converge. The second algorithm does not rely on global knowledge and adopts a heuristic method to achieve consensus on failures. The algorithms were implemented and tested using the Extreme-scale Simulator (xSim), ORNL's performance/resilience investigation toolkit for simulating future-generation extreme-scale high-performance computing systems. The results show that in both algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory usage and network bandwidth costs and a perfect synchronization in achieving global consensus.
This work was performed at the University of Reading, UK and ORNL. The work at ORNL was funded by DOE's Advanced Scientific Computing Research office's Exascale Operating System and Runtime (ExaOS/R) program
Publication: A. Katti, G. Di Fatta, T. Naughton, and C. Engelmann, "Scalable and Fault Tolerant Failure Detection and Consensus", Proceedings of the European MPI Users` Group Meeting (EuroMPI'15), Bordeaux, France, September 21-24, 2015.
Quantifying Scheduling Challenges for Exascale System Software
Oscar Mondragon, University of New Mexico, Patrick Bridges, University of New Mexico, Terry Jones, ORNL
The move towards high-performance computing (HPC) applications comprised of coupled codes and the need to dramatically reduce data movement is leading to a reexamination of time-sharing vs. space-sharing in HPC systems. In this paper, we discuss and begin to quantify the performance impact of a move away from strict space-sharing of nodes for HPC applications. Specifically, we examine the potential performance cost of time-sharing nodes between application components, we determine whether a simple coordinated scheduling mechanism can address these problems, and we research how suitable simple constraint-based optimization techniques are for solving scheduling challenges in this regime. Our results demonstrate that current general-purpose HPC system software scheduling and resource allocation systems are subject to significant performance deficiencies which we quantify for six representative applications. Based on these results, we discuss areas in which additional research is needed to meet the scheduling challenges of next-generation HPC systems.
This work is funded by DOE-ASCR and is part of the Hobbes project.
Publication: Oscar Mondragon, Patrick Bridges, and Terry Jones. "Quantifying Scheduling Challenges for Exascale System Software". in Runtime and Operating Systems for Supercomputers (ROSS 2015). Portland, OR. June, 2015.
Block Preconditioners for Implicit Atmospheric Climate Simulations
P. A. Lott (LLNL), C.S. Woodward (LLNL), K.J. Evans (ORNL)
In this paper, we introduce a scalable preconditioner within the Community Atmospheric Model (CAM) model that is designed to improve the efficiency of the linear system solves in the implicit dynamics solver. Performing accurate and efficient numerical simulation of global atmospheric climate models is challenging due to the disparate length and time scales over which physical processes interact. Implicit solvers enable the physical system to be integrated with a time step commensurate with processes being studied rather than to maintain stability. The dominant cost of an implicit time step is the ancillary linear system solves, so the preconditioner, which is based on an approximate block factorization of the linearized shallow-water equations, has been implemented within the spectral element dynamical core of CAM to minimize this expense. In this paper, we discuss the development and scalability of the preconditioner for a suite of test cases with the implicit shallow-water solver within CAM, and show how the choice of solver parameter settings affects the behavior of both the solver and preconditioner. We also present the remaining steps to gain efficiency using this solver strategy.
This work is funded by DOE BER/ASCR and is part of the Multiscale BER SciDAC project
Publication: P. A. Lott, C.S. Woodward, K.J. Evans (2014). Algorithmically scalable block preconditioner for fully implicit shallow water equations in CAM-SE. Comp. Geosci., 19:49-61, doi: 10.1007/s10596-014-9447-6
Table2Graph: A Scalable Graph Construction from Relational Tables using MapReduce framework
Sangkeun Lee, Byung H. Park (Lead), Seung-Hwan Lim, and Mallikarjun Shankar
Identifying correlations and relationships between entities within and across different data sets (or databases) is of great importance in many domains. The data warehouse-based integration, which has been most widely practiced, is found to be inadequate to achieve such a goal. Instead we explored an alternate solution that turns multiple disparate data sources into a single heterogeneous graph model so that matching between entities across different source data would be expedited by examining their linkages in the graph. We found, however, while a graph-based model provides outstanding capabilities for this purposes, construction of one such model from relational source databases were time consuming and primarily left to ad hoc proprietary scripts. This led us to develop a reconfigurable and reusable graph construction tool that is designed to work at scale.
This work was sponsored by LDRD funds.
Publication: Sangkeun Lee, Byung H. Park, Seung-Hwan Lim, and Mallikarjun Shankar, Table2Graph: A Scalable Graph Construction from Relational Tables using Map-Reduce, IEEE conference on BigData Services, 2015
Climate Change Impact on Diseases
Paul E. Parham, Joanna Waldock, George K. Christophides, Deborah Hemming, Folashade Agusto, Katherine J. Evans, Nina Fefferman, Holly Gaff, Abba Gumel, Shannon LaDeau, Suzanne Lenhart, Ronald E. Mickens, Elena N. Naumova, Richard S. Ostfeld, Paul D. Ready, Matthew B. Thomas, Jorge Velasco-Hernandez, Edwin Michael
Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission. This complexity has generated considerable debate over the past 10–15 years. In this review article, the authors seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.
Publication: Parham PE et al. 2015 Climate, environmental and socio-economic change: weighing up the balance in vector- borne disease transmission. Phil. Trans. R. Soc. B 370: 20130551. http://dx.doi.org/10.1098/rstb.2013.0551
Analyzing the Interplay of Failures and Workload on a Leadership-Class Supercomputer
Esteban Meneses, University of Pittsburgh, Xiang Ni, University of Illinois at Urbana-Champaign, Terry Jones, ORNL, and Don Maxwell, ORNL
The unprecedented computational power of current supercomputers now makes possible the exploration of complex problems in many scientific fields, from genomic analysis to computational fluid dynamics. Modern machines are powerful because they are massive: they assemble millions of cores and a huge quantity of disks, cards, routers, and other components. But it is precisely the size of these machines that glooms the future of supercomputing. A system that comprises many components has a high chance to fail, and fail often. In order to make the next generation of supercomputers usable, it is imperative to use some type of fault tolerance platform to run applications on large machines. Most fault tolerance strategies can be optimized for the peculiarities of each system and boost efficacy by keeping the system productive. In this paper, we aim to understand how failure characterization can improve resilience in several layers of the software stack: applications, runtime systems, and job schedulers. We examine the Titan supercomputer, one of the fastest systems in the world. We analyze a full year of Titan in production and distill the failure patterns of the machine. By looking into Titan's log files and using the criteria of experts, we provide a detailed description of the types of failures. In addition, we inspect the job submission files and describe how the system is used. Using those two sources, we cross correlate failures in the machine to executing jobs and provide a picture of how failures affect the user experience. We believe such characterization is fundamental in developing appropriate fault tolerance solutions for Cray systems similar to Titan.
Publication: Esteban Meneses, Xiang Ni, Terry Jones, and Don Maxwell. "Analyzing the Interplay of Failures and Workload on a Leadership-Class Supercomputer". in CUG 2015. Chicago, IL. April, 2015.
Towards a Science of Tumor Forecast for Clinical Oncology
T. Yankeelov, V. Quaranta , K.J. Evans, and E. Rericha
We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.
This work was sponsored by DOE.
Publication: T. Yankeelov, V. Quaranta , K.J. Evans, and E. Rericha (2015). Towards a Science of Tumor Forecast for Clinical Oncology. Cancer Research
A Simple, Optical Method to Determine How Two-dimensional Layers are Stacked in a Crystal
Alexander A. Puretzky, Liangbo Liang, Xufan Li, Kai Xiao, Kai Wang, Masoud Mahjouri-Samani, Leonardo Basile, Juan Carlos Idrobo, Bobby G. Sumpter, Vincent Meunier, David B. Geohegan
In this work it was discovered that layers of two-dimensional (2D) materials with different atomic registries have characteristic Raman spectra fingerprints in the low frequency spectral range that can be used to characterize stacking patterns of these materials. Stacked monolayers of 2D materials present a new class of hybrid materials with tunable optoelectronic properties determined by their stacking orientation, order, and atomic registry. Fast optical determination of the exact atomic registration between different layers, in few-layer 2D stacks is a key factor for rapid development of these materials and their applications. Using two- and three-layer MoSe2 and WSe2 crystals synthesized by chemical vapor deposition we show that the generally unexplored low frequency Raman modes (< 50 cm-1) that originate from interlayer vibrations can serve as fingerprints to characterize not only the number of layers, but also their stacking configurations. Ab initio calculations and group theory analysis corroborate the experimental assignments and show that the calculated low frequency mode fingerprints are related to the 2D crystal symmetries.
This work was sponsored by DOE.
Publication: "Low-Frequency Raman Fingerprints of Two-Dimensional Metal Dichalcogenide Layer Stacking Configuration", Alexander A. Puretzky, Liangbo Liang, Xufan Li, Kai Xiao, Kai Wang, Masoud Mahjouri-Samani, Leonardo Basile, Juan Carlos Idrobo, Bobby G. Sumpter, Vincent Meunier, David B. Geohegan, ACS Nano (2015). DOI:10.1021/acsnano.5b01884
Web-based Visual Analytics for Extreme Scale Climate Science
C.A. Steed, K.J. Evans, J.F. Harney, B.C. Jewell, G. Shipman, B.E. Smith, P.E. Thornton, and D.N Williams
In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, inefficient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.
This work is funded by DOE-BER and is part of the ACME project.
Publication: C.A. Steed, K.J. Evans, J.F. Harney, B.C. Jewell, G. Shipman, B.E. Smith, P.E. Thornton, and D.N Williams. "Web-based Visual Analytics for Extreme Scale Climate Science", In Proceedings IEEE International Conference on Big Data, Oct. 2014. p383-392.
Fast Fault Injection and Sensitivity Analysis for Collective Communications
Manjunath Gorentla Venkata, Kun Feng, Dong Li
The collective communication operations, which are widely used in parallel applications for global communication and synchronization, are critical for application's performance and scalability. However, how faulty collective communications impact the application and how errors propagate between the application processes is largely unexplored. One of the critical reasons for this situation is the lack of fast evaluation method to investigate the impacts of faulty collective operations. The traditional random fault injection methods relying on a large amount of fault injection tests to ensure statistical significance require a significant amount of resources and time. These methods result in prohibitive evaluation cost when applied to the collectives.
In this work, we explore a novel tool named Fast Fault Injection and Sensitivity Analysis Tool (FastFIT) to conduct fast fault injection and characterize the application sensitivity to faulty collectives. The tool achieves fast exploration by reducing the exploration space and predicting the application sensitivity using Machine Learning (ML) techniques. A basis for these techniques is implicit correlations between MPI semantics, application context, critical application features, and application responses to faulty collective communications. The experimental results show that our approach reduces the fault injection points and tests by 97% for representative benchmarks (NAS Parallel Benchmarks (NPB)) and a realistic application (Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)) on a production supercomputer. Further, we explore statistically generalizing the application sensitivity to faulty collective communications for these workloads, and present correlation between application features and the sensitivity.
This work is funded by OLCF and is part of the FastFIT project.
Publication: Kun Feng, Manjunath Gorentla Venkata, Dong Li, Xian-He Sun "Fast Fault Injection and Sensitivity Analysis for Collective Communications". In Proceedings of IEEE International Conference on Cluster Computing (Cluster), 2015.
Transfer of Lustre Expertise to the Community
Michael Brim, Jesse Hanley, Jason Hill, Neena Imam, Josh Lothian, Rick Mohr (UTK/NICS), Sarp Oral, Joel Reed, Jeffrey Rossiter
The Lustre 101 web-based course series is focused on administration and monitoring of large-scale deployments of the Lustre parallel file system. Course content is drawn from nearly a decade of experience in deploying and operating leadership-class Lustre file systems at the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL).
A primary concern in deploying a large system such as Lustre is building the operational experience and insight to triage and resolve intermittent service problems. Although there is no replacement for experience, it is also true that there is no adequate training material for becoming a Lustre administration expert. The overall goal of the Lustre 101 course series is to distill and disseminate to the Lustre community the working knowledge of ORNL administration and technical staff in the hope that others can avoid the trials and tribulations of large-scale Lustre administration and monitoring.
The Lustre Administration Essentials course is targeted at experienced system administrators who are relatively new to Lustre, but may have prior experience with other distributed and parallel file systems. Topics in this course include an introduction to Lustre, hardware selection and benchmarking strategies, Lustre software installation and basic configuration, Lustre tuning and LNet configuration, and basic file system administration and monitoring approaches.
This work was sponsored by DoD-HPC Research Program at ORNL
For more information please visit - http://lustre.ornl.gov/lustre101-courses/
System-Level Support for Composition of Applications
Brian Kocoloski, John Lange, Hasan Abbasi, David E. Bernholdt (ORNL PI), Terry R. Jones, Jay Dayal, Noah Evans, Michael Lang, Jay Lofstead, Kevin Pedretti, and Patrick G. Bridges
This paper presents a preliminary design study and initial evaluation of an operating system/runtime (OS/R) environment, Hobbes, with explicit support for composing HPC applications from multiple cooperating components. The design is based on our previously presented vision and makes systematic use of both virtualization and lightweight operating systems techniques to support multiple communicating application enclaves per node. In addition, it also includes efficient inter-enclave communication tools to enable application composition. Furthermore, we show that our Hobbes OS/R supports the composition of applications across multiple isolated enclaves with little to no performance overhead.
Work was performed at University of Pittsburgh, ORNL, Georgia Institute of Technology, Los Alamos National Laboratory, Sandia National Laboratories, and the University of New Mexico. Sponsored by the DOE, Office of Science, Advanced Scientific Computing Research (ASCR) program.
This work is funded by DOE-ASCR and is part of the Hobbes project.
Publication: Brian Kocoloski, John Lange, Hasan Abbasi, David E. Bernholdt, Terry R. Jones, Jay Dayal, Noah Evans, Michael Lang, Jay Lofstead, Kevin Pedretti, and Patrick G. Bridges, System-Level Support for Composition of Applications, in Proceedings of the 5th International Workshop on Runtime and Operating Systems for Supercomputers (ROSS 2015), 2015.
Fidelity of Climate Extremes in High Resolution Global Climate Simulations
Michael Brim, Jesse Hanley, Jason Hill, Neena Imam, Josh Lothian, Rick Mohr (UTK/NICS), Sarp Oral, Joel Reed, Jeffrey Rossiter
Precipitation extremes have tangible societal impacts. Here, we assess if current state of the art global climate model simulations at high spatial resolutions (0.35◦x0.35◦) capture the ob- served behavior of precipitation extremes in the past few decades over the continental US. We design a correlation-based regionalization framework to quantify precipitation extremes, where samples of extreme events for a grid box may also be drawn from neighboring grid boxes with statistically equal means and statistically significant temporal correlations. We model precipitation extremes with the Generalized Extreme Value (GEV) distribution fits to time series of annual maximum precipitation. Non-stationarity of extremes is captured by including a time-dependent parameter in the GEV distribution. Our analysis reveals that the high-resolution model substantially improves the simulation of stationary precipitation extreme statistics particularly over the Northwest Pacific coastal region and the Southeast US. Observational data exhibits significant non-stationary behavior of extremes only over some parts of the Western US, with declining trends in the extremes. While the high-resolution simulations improve upon the low resolution model in simulating this non-stationary behavior, the trends are statistically significant only over some of those regions.
This work is funded by DOE-BER and is part of the ACME project.
Publication: Mahajan S., K. J. Evans, M. Branstetter, V. Anantharaj and J. K. Leifeld (2015): Fidelity of precipitation extremes in high-resolution global climate simulations, Procedia Computer Science
Microscopic Calculations Reveal a Surprising Conventional Nature of Exotic Pairing States
Y. Wang, T. Berlijn, P.J. Hirschfeld, D.J. Scalapino, T.A. Maier
Early calculations of the iron-based superconductors based on a spin fluctuation model of pairing had great success in predicting the superconducting ground state and the qualitative systematics of its variation with doping, etc. Recent proposals, however, have argued that these treatments have neglected the true symmetry of the crystalline layer containing Fe, which has pnictogen and chalcogen atoms in buckled positions, providing a strong potential on the electrons in the Fe plane and enforcing a unit cell with 2 Fe atoms. Several recent phenomenological treatments of the implications of this symmetry for pairing have argued that this aspect had been missed in the earlier 1-Fe unit cell calculations and that this potential can force a completely different electronic ground state, where so-called eta-pairing states with non-zero total momentum and exotic properties such as odd parity spin singlet symmetry and possible time reversal symmetry breaking contribute to the superconducting condensate. This work uses concrete and realistic microscopic calculations for 2-Fe and 1-Fe models to demonstrate that the earlier 1-Fe calculations correctly accounted for this glide-plane symmetry and correctly predicted its implications on the observable superconducting gap. It furthermore shows that eta-pairing naturally arises in systems where both orbitals with even and orbitals with odd mirror reflection symmetry in z contribute to the Fermi surface states. In contrast to the recent proposals, however, this study finds that eta-pairing contributes with the usual even parity symmetry and that time reversal symmetry is not broken. This work has established a clear framework for the study of such questions in other unconventional superconductors, where similar questions have also arisen.
This work is funded by DOE and NSF.
Publication: "Glide-Plane Symmetry and Superconducting Gap Structure of Iron-Based Superconductors", Y. Wang, T. Berlijn, P.J. Hirschfeld, D.J. Scalapino, T.A. Maier, Physical Review Letters 114, 107002 (2015).
Wide-Area In Transit Data Processing For Real-Time Monitoring
S. Klasky, N. Podhorszki, Q. Liu, H. Abbasi, J. Choi, Y. Tian, J. Mu, J. Logan, D. Pugmire, G. Ostrouchov, T. Kurc, L. Wu, K. Wu, X. Yan, M. Wolf, G. Eisenhauer, M. Aktas, M. Parashar
ICEE addresses the challenges of building a remote data analysis framework, motivated from real-world scientific applications. ICEE is designed to support data stream processing for near real-time remote analysis over wide-area networks. This solution is based on in-memory stream data processing in which we can reduce the time-to-solution compared with conventional batch-based processing.
Work was performed by ORNL, LBNL, PPPL, Georgia Tech, Rutgers University, KISTI (Korea), A*STAR Computational Resource Centre (Singapore), and the ICEE SciDAC project for the wide-area-network movement.
Presentation: ICEE: Enabling data stream processing for remote data analysis over wide area networks. Supercomputing Frontiers 2015, March 17, 2015.
Publication: ICEE: Enabling data stream processing for remote data analysis over wide area networks. Special Issue in Supercomputing frontiers and innovations, 2015
Memory Scalability and Efficiency Analysis of Parallel Codes
Tomislav Janjusic and Christos Kartsaklis, ORNL
Memory scalability is an enduring problem and bottleneck that plagues many parallel codes. Parallel codes designed for High Performance Systems are typically designed over the span of several, and in some instances 10+, years. As a result, optimization practices, which were appropriate for earlier systems, may no longer be valid and thus require careful optimization consideration. Specifically, parallel codes whose memory footprint is a function of their scalability must be carefully considered for future exa-scale systems.
In this work we present a methodology and tool to study the memory scalability of parallel codes. Using our methodology we evaluate an application's memory footprint as a function of scalability, which we coined memory efficiency, and describe our results. In particular, using our in-house tools we can pinpoint the specific application components, which contribute to the application's overall memory foot-print (application data- structures, libraries, etc.).
This work was performed at ORNL using OLCF funding.
Publication: Tomislav Janjusic, and Christos Kartsaklis. "Memory Scalability and Efficiency Analysis of Parallel Codes". in CUG 2015. Chicago, IL. April, 2015.
October 9, 2015 - Vivek Seshadri: Can DRAM do more than just store data?
October 13, 2015 - Edmond Chow: Very Fine-Grained Parallelization of Sparse Linear Algebra Computations
October 15, 2015 - Ian Foster: Accelerating Discovery Via Science Services
October 23, 2015 - Alvin R. Lebeck: Molecular-Scale Nanophotonics for Network-on-Chip and Probabilistic Computing Functional Units