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DOE OSTI2016

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as well as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Adv. Simulation for Additive Manufacturing: 11/2014 Wkshp. Report for U.S. DOE/EERE/AMO

The overarching question for the workshop was as following: How do we best utilize advanced modeling and high-performance computing (HPC) to address key challenges and opportunities in order to realize the full potential of additive manufacturing; and what are the key challenges of additive manufacturing to which modeling and simulation can contribute solutions, and what will it take to meet these challenges?

42 ENGINEERING↗
DOE OSTI2015

Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing

The characterization, modeling, analysis, and tuning of software performance has been a central topic in High Performance Computing (HPC) since its early beginnings. The overall goal is to make HPC software run faster on particular hardware, either through better scheduling, on-node resource utilization, or more efficient distributed communication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Electric Grid Expansion Planning with High Levels of Variable Generation

Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind power across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Final Report Extreme Computing and U.S. Competitiveness DOE Award. DE-FG02-11ER26087/DE-SC0008764

The Council has acted on each of the grant deliverables during the funding period. The deliverables are: (1) convening the Council’s High Performance Computing Advisory Committee (HPCAC) on a bi-annual basis; (2) broadening public awareness of high performance computing (HPC) and exascale developments; (3) assessing the industrial applications of extreme computing; and (4) establishing a policy and business case for an exascale economy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Planning for Pre-Exascale Platform Environment (Fiscal Year 2015 Level 2 Milestone 5216)

This Plan for ASC Pre-Exascale Platform Environments document constitutes the deliverable for the fiscal year 2015 (FY15) Advanced Simulation and Computing (ASC) Program Level 2 milestone Planning for Pre-Exascale Platform Environment. It acknowledges and quantifies challenges and recognized gaps for moving the ASC Program towards effective use of exascale platforms and recommends strategies to address these gaps. This document also presents an update to the concerns, strategies, and plans presented in the FY08 predecessor document that dealt with the upcoming (at the time) petascale high performance computing (HPC) platforms. With the looming push towards exascale systems, a review of the earlier document was appropriate in light of the myriad architectural choices currently under consideration. The ASC Program believes the platforms to be fielded in the 2020s will be fundamentally different systems that stress ASC’s ability to modify codes to take full advantage of new or unique features. In addition, the scale of components will increase the difficulty of maintaining an errorfree system, thus driving new approaches to resilience and error detection/correction. The code revamps of the past, from serial- to vector-centric code to distributed memory to threaded implementations, will be revisited as codes adapt to a new message passing interface (MPI) plus “x” or more advanced and dynamic programming models based on architectural specifics. Development efforts are already underway in some cases, and more difficult or uncertain aspects of the new architectures will require research and analysis that may inform future directions for program choices. In addition, the potential diversity of system architectures may require parallel if not duplicative efforts to analyze and modify environments, codes, subsystems, libraries, debugging tools, and performance analysis techniques as well as exploring new monitoring methodologies. It is difficult if not impossible to selectively eliminate some of these activities until more information is available through simulations of potential architectures, analysis of systems designs, and informed study of commodity technologies that will be the constituent parts of future platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Delivering HPC Systems to 132 Dock

The intention of this document is to provide the subcontractor with information to enable trucks delivering HPC (High Performance Computing) systems to the 03-0132, computer rooms with the information they need to do so successfully.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Results and Analysis of the Infrastructure Request for Information (DE-SOL-0008318)

The Department of Energy (DOE) Office of Nuclear Energy (NE) released a request for information (RFI) (DE-SOL-0008318) for “University, National Laboratory, Industry and International Input on Potential Office of Nuclear Energy Infrastructure Investments” on April 13, 2015. DOE-NE solicited information on five specific types of capabilities as well as any others suggested by the community. The RFI proposal period closed on June 19, 2015. From the 26 responses, 34 individual proposals were extracted. Eighteen were associated with a DOE national laboratory, including Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Idaho National Laboratory (INL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratory (SNL). Oak Ridge National Laboratory (ORNL) was referenced in a proposal as a proposed capability location, although the proposal did not originate with ORNL. Five US universities submitted proposals (Massachusetts Institute of Technology, Pennsylvania State University, Rensselaer Polytechnic Institute, University of Houston and the University of Michigan). Three industrial/commercial institutions submitted proposals (AREVA NP, Babcock and Wilcox (B&W) and the Electric Power Research Institute (EPRI)). Eight major themes emerged from the submissions as areas needing additional capability or support for existing capabilities. Two submissions supported multiple areas. The major themes are: Advanced Manufacturing (AM), High Performance Computing (HPC), Ion Irradiation with X-Ray Diagnostics (IIX), Ion Irradiation with TEM Visualization (IIT), Radiochemistry Laboratories (RCL), Test Reactors, Neutron Sources and Critical Facilities (RX) , Sample Preparation and Post-Irradiation Examination (PIE) and Thermal-Hydraulics Test Facilities (THF).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

GEOS. User Tutorials

GEOS is a massively parallel, multi-physics simulation application utilizing high performance computing (HPC) to address subsurface reservoir stimulation activities with the goal of optimizing current operations and evaluating innovative stimulation methods. GEOS enables coupling of di erent solvers associated with the various physical processes occurring during reservoir stimulation in unique and sophisticated ways, adapted to various geologic settings, materials and stimulation methods. Developed at the Lawrence Livermore National Laboratory (LLNL) as a part of a Laboratory-Directed Research and Development (LDRD) Strategic Initiative (SI) project, GEOS represents the culmination of a multi-year ongoing code development and improvement e ort that has leveraged existing code capabilities and sta expertise to design new computational geosciences software.

58 GEOSCIENCES↗
DOE OSTI2016

Advanced Simulation and Computing FY17 Implementation Plan, Version 0

The Stockpile Stewardship Program (SSP) is an integrated technical program for maintaining the safety, surety, and reliability of the U.S. nuclear stockpile. The SSP uses nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. It includes stockpile surveillance, experimental research, development and engineering programs, and an appropriately scaled production capability to support stockpile requirements. This integrated national program requires the continued use of experimental facilities and programs, and the computational capabilities to support these programs. The Advanced Simulation and Computing Program (ASC) is a cornerstone of the SSP, providing simulation capabilities and computational resources that support annual stockpile assessment and certification, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This requires a balance of resource, including technical staff, hardware, simulation software, and computer science solutions. ASC is now focused on increasing predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The program continues to improve its unique tools for solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), and quantifying critical margins and uncertainties. Resolving each issue requires increasingly difficult analyses because the aging process has progressively moved the stockpile further away from the original test base. Where possible, the program also enables the use of high performance computing (HPC) and simulation tools to address broader national security needs, such as foreign nuclear weapon assessments and counter nuclear terrorism.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

CDAC Student Report: Summary of LLNL Internship

Multiple objectives motivated me to apply for an internship at LLNL: I wanted to experience the work environment at a national lab, to learn about research and job opportunities at LLNL in particular, and to gain greater experience with code development, particularly within the realm of high performance computing (HPC). This summer I was selected to participate in LLNL's Computational Chemistry and Material Science Summer Institute (CCMS). CCMS is a 10 week program hosted by the Quantum Simulations group leader, Dr. Eric Schwegler. CCMS connects graduate students to mentors at LLNL involved in similar re- search and provides weekly seminars on a broad array of topics from within chemistry and materials science. Dr. Xavier Andrade and Dr. Erik Draeger served as my co-mentors over the summer, and Dr. Andrade continues to mentor me now that CCMS has concluded. Dr. Andrade is a member of the Quantum Simulations group within the Physical and Life Sciences at LLNL, and Dr. Draeger leads the HPC group within the Center for Applied Scientific Computing (CASC). The two have worked together to develop Qb@ll, an open-source first principles molecular dynamics code that was the platform for my summer research project.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Fully-Coupled Thermo-Electrical Modeling and Simulation of Transition Metal Oxide Memristors

Transition metal oxide (TMO) memristors have recently attracted special attention from the semiconductor industry and academia. Memristors are one of the strongest candidates to replace flash memory, and possibly DRAM and SRAM in the near future. Moreover, memristors have a high potential to enable beyond-CMOS technology advances in novel architectures for high performance computing (HPC). The utility of memristors has been demonstrated in reprogrammable logic (cross-bar switches), brain-inspired computing and in non-CMOS complementary logic. Indeed, the potential use of memristors as logic devices is especially important considering the inevitable end of CMOS technology scaling that is anticipated by 2025. In order to aid the on-going Sandia memristor fabrication effort with a memristor design tool and establish a clear physical picture of resistance switching in TMO memristors, we have created and validated with experimental data a simulation tool we name the Memristor Charge Transport (MCT) Simulator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Cross Domain Deterrence: Livermore Technical Report, 2014-2016

Lawrence Livermore National Laboratory (LLNL) is an original collaborator on the project titled “Deterring Complex Threats: The Effects of Asymmetry, Interdependence, and Multi-polarity on International Strategy,” (CDD Project) led by the UC Institute on Global Conflict and Cooperation at UCSD under PIs Jon Lindsay and Erik Gartzke , and funded through the DoD Minerva Research Initiative. In addition to participating in workshops and facilitating interaction among UC social scientists, LLNL is leading the computational modeling effort and assisting with empirical case studies to probe the viability of analytic, modeling and data analysis concepts. This report summarizes LLNL work on the CDD Project to date, primarily in Project Years 1-2, corresponding to Federal fiscal year 2015. LLNL brings two unique domains of expertise to bear on this Project: (1) access to scientific expertise on the technical dimensions of emerging threat technology, and (2) high performance computing (HPC) expertise, required for analyzing the complexity of bargaining interactions in the envisioned threat models. In addition, we have a small group of researchers trained as social scientists who are intimately familiar with the International Relations research. We find that pairing simulation scientists, who are typically trained in computer science, with domain experts, social scientists in this case, is the most effective route to developing powerful new simulation tools capable of representing domain concepts accurately and answering challenging questions in the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

High Energy Physics Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and High Energy Physics, June 10-12, 2015, Bethesda, Maryland

The U.S. Department of Energy (DOE) Office of Science (SC) Offices of High Energy Physics (HEP) and Advanced Scientific Computing Research (ASCR) convened a programmatic Exascale Requirements Review on June 10–12, 2015, in Bethesda, Maryland. This report summarizes the findings, results, and recommendations derived from that meeting. The high-level findings and observations are as follows. Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude — and in some cases greater — than that available currently. The growth rate of data produced by simulations is overwhelming the current ability of both facilities and researchers to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. Data rates and volumes from experimental facilities are also straining the current HEP infrastructure in its ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. A close integration of high-performance computing (HPC) simulation and data analysis will greatly aid in interpreting the results of HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. Long-range planning between HEP and ASCR will be required to meet HEP’s research needs. To best use ASCR HPC resources, the experimental HEP program needs (1) an established, long-term plan for access to ASCR computational and data resources, (2) the ability to map workflows to HPC resources, (3) the ability for ASCR facilities to accommodate workflows run by collaborations potentially comprising thousands of individual members, (4) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, (5) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Strengthening LLNL Missions through Laboratory Directed Research and Development in High Performance Computing

High performance computing (HPC) has been a defining strength of Lawrence Livermore National Laboratory (LLNL) since its founding. Livermore scientists have designed and used some of the world’s most powerful computers to drive breakthroughs in nearly every mission area. Today, the Laboratory is recognized as a world leader in the application of HPC to complex science, technology, and engineering challenges. Most importantly, HPC has been integral to the National Nuclear Security Administration’s (NNSA’s) Stockpile Stewardship Program—designed to ensure the safety, security, and reliability of our nuclear deterrent without nuclear testing. A critical factor behind Lawrence Livermore’s preeminence in HPC is the ongoing investments made by the Laboratory Directed Research and Development (LDRD) Program in cutting-edge concepts to enable efficient utilization of these powerful machines. Congress established the LDRD Program in 1991 to maintain the technical vitality of the Department of Energy (DOE) national laboratories. Since then, LDRD has been, and continues to be, an essential tool for exploring anticipated needs that lie beyond the planning horizon of our programs and for attracting the next generation of talented visionaries. Through LDRD, Livermore researchers can examine future challenges, propose and explore innovative solutions, and deliver creative approaches to support our missions. The present scientific and technical strengths of the Laboratory are, in large part, a product of past LDRD investments in HPC. Here, we provide seven examples of LDRD projects from the past decade that have played a critical role in building LLNL’s HPC, computer science, mathematics, and data science research capabilities, and describe how they have impacted LLNL’s mission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Current Capabilities at SNL for the Integration of Small Modular Reactors onto Smart Microgrids Using Sandia's Smart Microgrid Technology High Performance Computing and Advanced Manufacturing.

Smart grids are a crucial component for enabling the nation’s future energy needs, as part of a modernization effort led by the Department of Energy. Smart grids and smart microgrids are being considered in niche applications, and as part of a comprehensive energy strategy to help manage the nation’s growing energy demands, for critical infrastructures, military installations, small rural communities, and large populations with limited water supplies. As part of a far-reaching strategic initiative, Sandia National Laboratories (SNL) presents herein a unique, three-pronged approach to integrate small modular reactors (SMRs) into microgrids, with the goal of providing economically-competitive, reliable, and secure energy to meet the nation’s needs. SNL’s triad methodology involves an innovative blend of smart microgrid technology, high performance computing (HPC), and advanced manufacturing (AM). In this report, Sandia’s current capabilities in those areas are summarized, as well as paths forward that will enable DOE to achieve its energy goals. In the area of smart grid/microgrid technology, Sandia’s current computational capabilities can model the entire grid, including temporal aspects and cyber security issues. Our tools include system development, integration, testing and evaluation, monitoring, and sustainment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Sandia QIS Capabilities.

Sandia National Laboratories has developed a broad set of capabilities in quantum information science (QIS), including elements of quantum computing, quantum communications, and quantum sensing. The Sandia QIS program is built atop unique DOE investments at the laboratories, including the MESA microelectronics fabrication facility, the Center for Integrated Nanotechnologies (CINT) facilities (joint with LANL), the Ion Beam Laboratory, and ASC High Performance Computing (HPC) facilities. Sandia has invested $75 M of LDRD funding over 12 years to develop unique, differentiating capabilities that leverage these DOE infrastructure investments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗