Adverse Drug Reaction Prediction Using Scores Produced by Large-Scale Drug-Protein Target Docking on High-Performance Computing Machines
Abstract not provided
59 BASIC BIOLOGICAL SCIENCES↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Checkpointing Strategies for Shared High-Performance Computing Platforms
Input/output (I/O) from various sources often contend for scarcely available bandwidth. For example, checkpoint/restart (CR) protocols can help to ensure application progress in failure-prone environments. However, CR I/O alongside an application's normal, requisite I/O can increase I/O contention and might negatively impact performance. In this work, we consider different aspects (system-level scheduling policies and hardware) that optimize the overall performance of concurrently executing CR-based applications that share I/O resources. We provide a theoretical model and derive a set of necessary constraints to minimize the global waste on a given platform. Our results demonstrate that Young/Daly's optimal checkpoint interval, despite providing a sensible metric for a single, undisturbed application, is not sufficient to optimally address resource contention at scale. We show that by combining optimal checkpointing periods with contention-aware system-level I/O scheduling strategies, we can significantly improve overall application performance and maximize the platform throughput. Finally, we evaluate how specialized hardware, namely burst buffers, may help to mitigate the I/O contention problem. Altogether, these results provide critical analysis and direct guidance on how to design efficient, CR ready, large -scale platforms without a large investment in the I/O subsystem.
97 MATHEMATICS AND COMPUTING↗
EspressoDB: A scientific database for managing high-performance computing workflows
EspressoDB is a programmatic object-relational mapping (ORM) data management framework implemented in Python and based on the Django web framework. EspressoDB was developed to streamline data management, centralize and promote data integrity, while providing domain flexibility and ease of use. It is designed to directly integrate in utilized software to allow dynamical access to vast amount of relational data at runtime.
Chang, Chia↗
High Performance Computing Facility Operational Assessment, CY 2011 Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory's Leadership Computing Facility (OLCF) continues to deliver the most powerful resources in the U.S. for open science. At 2.33 petaflops peak performance, the Cray XT Jaguar delivered more than 1.4 billion core hours in calendar year (CY) 2011 to researchers around the world for computational simulations relevant to national and energy security; advancing the frontiers of knowledge in physical sciences and areas of biological, medical, environmental, and computer sciences; and providing world-class research facilities for the nation's science enterprise. Users reported more than 670 publications this year arising from their use of OLCF resources. Of these we report the 300 in this review that are consistent with guidance provided. Scientific achievements by OLCF users cut across all range scales from atomic to molecular to large-scale structures. At the atomic scale, researchers discovered that the anomalously long half-life of Carbon-14 can be explained by calculating, for the first time, the very complex three-body interactions between all the neutrons and protons in the nucleus. At the molecular scale, researchers combined experimental results from LBL's light source and simulations on Jaguar to discover how DNA replication continues past a damaged site so a mutation can be repaired later. Other researchers combined experimental results from ORNL's Spallation Neutron Source and simulations on Jaguar to reveal the molecular structure of ligno-cellulosic material used in bioethanol production. This year, Jaguar has been used to do billion-cell CFD calculations to develop shock wave compression turbo machinery as a means to meet DOE goals for reducing carbon sequestration costs. General Electric used Jaguar to calculate the unsteady flow through turbo machinery to learn what efficiencies the traditional steady flow assumption is hiding from designers. Even a 1% improvement in turbine design can save the nation billions of gallons of fuel.
99 GENERAL AND MISCELLANEOUS↗
Twenty-Five-Year High Performance Computing Facility Master Plan and Recommendation
The current Terascale Simulation Facility (TSF) Complex facilities at Lawrence Livermore National Laboratory (LLNL) include buildings B-115, B-117, B-439, B-451, and B-453 (the TSF). B115 and B117 are 60 years old, have permanent limitations with respect to the ability to upgrade their electrical and mechanical infrastructure, and have significant structural deficiencies that must be addressed and are prohibitively costly given the age and limitations of the buildings. They are slated for Deactivation and Decommissioning (D&D).
97 MATHEMATICS AND COMPUTING↗
Coordinated Fault Tolerance for High-Performance Computing
Our work to meet our goal of end-to-end fault tolerance has focused on two areas: (1) improving fault tolerance in various software currently available and widely used throughout the HEC domain and (2) using fault information exchange and coordination to achieve holistic, systemwide fault tolerance and understanding how to design and implement interfaces for integrating fault tolerance features for multiple layers of the software stack—from the application, math libraries, and programming language runtime to other common system software such as jobs schedulers, resource managers, and monitoring tools.
97 MATHEMATICS AND COMPUTING↗
High-performance computing for model-driven decision support related to the LANL Chromium Site [Slides]
Abstract Not Provided
42 ENGINEERING↗
High Performance Computing Facility Operational Assessment, 2012 Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory’s Leadership Computing Facility (OLCF) continues to deliver the most powerful resources in the United States for open science. At 2.33 petaflops peak performance, the Cray XT Jaguar delivered more than 1.4 billion core hours in calendar year (CY) 2012 to researchers around the world for performing computational simulations relevant to national and energy security; advancing the frontiers of knowledge in physical sciences and areas of biological, medical, environmental, and computer sciences; and providing world-class research facilities for the nation’s science enterprise. OLCF users achieved numerous wide-ranging research accomplishments and technological innovations in 2012. It is not possible to fully summarize the breadth and impact of their productivity within this brief report—OLCF researchers published more than 300 research articles in 2012 alone. Here we identify selected accomplishments that advance the state of the art in science and engineering research and development. The work was recognized through peer-review publications in notable journals such as Science, Nature, and The Proceedings of the National Academy of Sciences (PNAS), among the many noted in this report. Exploration of the nuclear landscape carried out by Innovative and Novel Computational Impact on Theory and Experiment (INCITE) researchers and their theoretical prediction of isotopes were featured in Nature in 2012 (see Section 3.2.2). On the other end of the physical-scale spectrum, researchers executed a 22,000-year transient Earth System Model simulation that explained the misleading lag of carbon dioxide behind Antarctic temperature records and pointed to carbon dioxide’s role in driving global climate change over glacial cycles. This deglaciation study and related work by OLCF users were featured in Nature and PNAS in 2012 (see Section 3.2.3). Other representative achievements by OLCF users include generation of spectroscopic and photometric data for more accurate distance measurements, necessary for planning future National Science Foundation and Department of Energy (DOE) missions such as the Large Synoptic Survey Telescope and the Palomar Transient Factory; calculations to enable the experimental verification of Bose glass; and screening of 2 million compounds against a targeted receptor in a matter of days, as opposed to the months that would be required for computing clusters, creating a vast library of molecular compounds that can be used for future screenings of potential drug candidates. These and other accomplishments are described in Section 3.
OA↗
HPGMG 1.0: A Benchmark for Ranking High Performance Computing Systems
This document provides an overview of the benchmark - HPGMG - for ranking large scale general purpose computers for use on the Top500 list. We provide a rationale for the need for a replacement for the current metric HPL, some background of the Top500 list and the challenges of developing such a metric; we discuss our design philosophy and methodology, and an overview of the specification of the benchmark. The primary documentation with maintained details on the specification can be found at hpgmg.org and the Wiki and benchmark code itself can be found in the repository https://bitbucket.org/hpgmg/hpgmg.
97 MATHEMATICS AND COMPUTING↗
High Performance Computing Facility Operational Assessment, 2013 Oak Ridge Leadership Computing Facility
Explore the source record for details and available documents.
OLCF Operational Assessment OA OAR↗
Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information
Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.
Aimone, James Bradley↗
Energy Performance Testing of Asetek's RackCDU System at NREL's High Performance Computing Data Center
In this study, we report on the first tests of Asetek's RackCDU direct-to-chip liquid cooling system for servers at NREL's ESIF data center. The system was simple to install on the existing servers and integrated directly into the data center's existing hydronics system. The focus of this study was to explore the total cooling energy savings and potential for waste-heat recovery of this warm-water liquid cooling system. RackCDU captured up to 64% of server heat into the liquid stream at an outlet temperature of 89 degrees F, and 48% at outlet temperatures approaching 100 degrees F. This system was designed to capture heat from the CPUs only, indicating a potential for increased heat capture if memory cooling was included. Reduced temperatures inside the servers caused all fans to reduce power to the lowest possible BIOS setting, indicating further energy savings potential if additional fan control is included. Preliminary studies manually reducing fan speed (and even removing fans) validated this potential savings but could not be optimized for these working servers. The Asetek direct-to-chip liquid cooling system has been in operation with users for 16 months with no necessary maintenance and no leaks.
97 MATHEMATICS AND COMPUTING↗
Analysis of Alternatives (AoA) of Open Colllaboration and Research Capabilities Collaboratipon in Research and Engineering in Advanced Technology and Education and High-Performance Computing Innovation Center (HPCIC) on the LVOC.
The Livermore Valley Open Campus (LVOC), a joint initiative of the National Nuclear Security Administration (NNSA), Lawrence Livermore National Laboratory (LLNL), and Sandia National Laboratories (SNL), enhances the national security missions of NNSA by promoting greater collaboration between world-class scientists at the national security laboratories, and their partners in industry and academia. Strengthening the science, technology, and engineering (ST&E) base of our nation is one of the NNSA’s top goals. By conducting coordinated and collaborative programs, LVOC enhances both the NNSA and the broader national science and technology base, and helps to ensure the health of core capabilities at LLNL and SNL. These capabilities must remain strong to enable the laboratories to execute their primary mission for NNSA.
99 GENERAL AND MISCELLANEOUS↗
W14_CONTAMINANTREMEDIATION: High Performance Computing (HPC) for Multi-scale Decision Analyses: From Pore-scale Processes to Field-scale Contaminant Remediation
This report describes computing terms relating to contaminant remediation efforts used at LANL.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Using High Performance Computing to Support Water Resource Planning
In recent years, decision support modeling has embraced deliberation-withanalysis— an iterative process in which decisionmakers come together with experts to evaluate a complex problem and alternative solutions in a scientifically rigorous and transparent manner. Simulation modeling supports decisionmaking throughout this process; visualizations enable decisionmakers to assess how proposed strategies stand up over time in uncertain conditions. But running these simulation models over standard computers can be slow. This, in turn, can slow the entire decisionmaking process, interrupting valuable interaction between decisionmakers and analytics.
54 ENVIRONMENTAL SCIENCES↗
Validation of MCNP6 Version 1.0 with the ENDF/B-VII.1 Cross Section Library for Uranium Metal, Oxide, and Solution Systems on the High Performance Computing Platform Moonlight
In this document, the code MCNP is validated with ENDF/B-VII.1 cross section data under the purview of ANSI/ANS-8.24-2007, for use with uranium systems. MCNP is a computer code based on Monte Carlo transport methods. While MCNP has wide reading capability in nuclear transport simulation, this validation is limited to the functionality related to neutron transport and calculation of criticality parameters such as k eff .
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing
The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.
97 MATHEMATICS AND COMPUTING↗
Interaction and Impact Studies for Distributed Energy Resource, Transactive Energy, and Electric Grid, using High Performance Computing ?based Modeling and Simulation
The electric utility industry is undergoing significant transformations in its operation model, including a greater emphasis on automation, monitoring technologies, and distributed energy resource management systems (DERMS). With these changes and new technologies, while driving greater efficiencies and reliability, these new models may introduce new vectors of cyber attack. The appropriate cybersecurity controls to address and mitigate these newly introduced attack vectors and potential vulnerabilities are still widely unknown and performance of the control is difficult to vet. This proposal argues that modeling and simulation (M&S) is a necessary tool to address and better understand these problems introduced by emerging technologies for the grid. M&S will provide electric utilities a platform to model its transmission and distribution systems and run various simulations against the model to better understand the operational impact and performance of cybersecurity controls.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗