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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

2HOT: an improved parallel hashed oct-tree n-body algorithm for cosmological simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (2 18 ) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (4096 3 ) particle cosmological simulations, accounting for 4 × 10 20 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

Computer Science↗
DOE OSTI2014

2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (2 18 ) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (4096 3 ) particle cosmological simulations, accounting for 4×10 20 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

Warren, Michael S.↗
NASA NTRS2006

Cassini Archive Tracking System

The Cassini Archive Tracking System (CATS) is a computer program that enables tracking of scientific data transfers from originators to the Planetary Data System (PDS) archives. Without CATS, there is no systematic means of locating products in the archive process or ensuring their completeness. By keeping a database of transfer communications and status, CATS enables the Cassini Project and the PDS to efficiently and accurately report on archive status. More importantly, problem areas are easily identified through customized reports that can be generated on the fly from any Web-enabled computer. A Web-browser interface and clearly defined authorization scheme provide safe distributed access to the system, where users can perform functions such as create customized reports, record a transfer, and respond to a transfer. CATS ensures that Cassini provides complete science archives to the PDS on schedule and that those archives are available to the science community by the PDS. The three-tier architecture is loosely coupled and designed for simple adaptation to multimission use. Written in the Java programming language, it is portable and can be run on any Java-enabled Web server.

Conner, Diane↗
DOE OSTI2023

PluginPlay: Enabling exascale scientific software one module at a time

For many computational chemistry packages, being able to efficiently and effectively scale across an exascale cluster is a heroic feat. Collective experience from the Department of Energy’s Exascale Computing Project suggests that achieving exascale performance requires far more planning, design, and optimization than scaling to petascale. In many cases, entire rewrites of software are necessary to address fundamental algorithmic bottlenecks. This in turn requires a tremendous amount of resources and development time, resources that cannot reasonably be afforded by every computational science project. It thus becomes imperative that computational science transition to a more sustainable paradigm. Key to such a paradigm is modular software. While the importance of modular software is widely recognized, what is perhaps not so widely appreciated is the effort still required to leverage modular software in a sustainable manner. The present manuscript introduces PluginPlay, https://github.com/NWChemEx-Project/PluginPlay, an inversion-of-control framework designed to facilitate developing, maintaining, and sustaining modular scientific software packages. Here this manuscript focuses on the design aspects of PluginPlay and how they specifically influence the performance of the resulting package. Although, PluginPlay serves as the framework for the NWChemEx package, PluginPlay is not tied to NWChemEx or even computational chemistry. We thus anticipate PluginPlay to prove to be a generally useful tool for a number of computational science packages looking to transition to the exascale.

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

On-demand data analytics in HPC environments at leadership computing facilities: Challenges and experiences

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Harney, John↗
DOE OSTI2017

Site Environmental Report for 2016

Lawrence Berkeley National Laboratory (LBNL, Berkeley Lab) is a multi-program scientific facility operated by the University of California (UC) for the U.S. Department of Energy (DOE). Berkeley Lab’s research is focused on the physical, biological, environmental, and computational sciences with the objective of delivering scientific knowledge and discoveries pertinent to DOE’s mission. This annual report describes environmental protection activities and potential impacts resulting from LBNL operations conducted in 2016. The format and content of this report satisfy the requirements of both DOE Order 231.1B Admin Chg 1, Environment, Safety, and Health Reporting, and the operating contract between UC and DOE (DOE Contract No. DE-AC02-05CH11231, also known as Contract 31). Activities are planned and conducted with full regard to protecting employees, the public, and the environment, as well as complying with all applicable environmental, safety, and health laws and regulations. Berkeley Lab implements an Environmental Management System (EMS) to oversee these compliance activities and improve overall environmental performance while maintaining operational capability and sustaining its overall mission. The effectiveness of the EMS and environmental programs is reviewed annually as part of the operating contract’s performance evaluation process. For fiscal year (FY) 2016, which began October 1, 2015, and ended September 30, 2016, the EMS was given a performance rating of A minus for its management of environmental activities (on a scale from A plus as the highest grade to F as the lowest). The measurement and rating system was developed jointly by Berkeley Lab, UC, and DOE. The FY 2016 rating was based on how Berkeley Lab met the objective in DOE’s FY 2017 Performance Evaluation and Measurement Plan (Appendix B in Contract 31, Section J) of providing an efficient and effective EMS. To support this rating, Berkeley Lab prepared a list of projects completed during the performance period that achieved reduced environmental impacts. The EMS was also graded through the federal Office of Management and Budget’s EMS Annual Report Data, in which elements of the International Organization for Standardization (ISO) 14001 standard are rated and the degree of integration between the EMS and Berkeley Lab’s sustainable practices is measured. Overall scores fall into one of three categories: green (highest), yellow (middle), or red (lowest). For FY 2016, Berkeley Lab received a score of green, as described in more detail in Chapter 2. An overview of environmental protection and restoration programs is provided (Chapter 3), including information about compliance activities, operating permits, and regulatory agency inspections that occurred during 2016. Six minor violations issued during these inspections are discussed, one of which was rescinded in 2017. This report also includes information on environmental monitoring performed in 2016 (Chapter 4). The results of these monitoring activities confirmed that groundwater cleanup actions continue to show improving conditions, and all emissions and discharges from LBNL operations were within environmental compliance release limits with the exception of some stormwater discharges. While most stormwater discharges measured throughout the LBNL site fall within acceptable levels established by the state's stormwater permit, iron and aluminum exceeded permitted levels. Berkeley Lab is aggressively implementing controls to reduce iron and aluminum discharge levels. The controls include construction of asphaltic berms to restrain and filter runoff to storm drains, and installation of filtration units in storm drain basins to absorb metal contaminants. The radiological dose assessments (Chapter 5) performed in 2016 concluded that the maximum dose to a hypothetical resident from Berkeley Lab’s airborne radionuclide releases was approximately 0.12% of the DOE and U.S. Environmental Protection Agency annual limit of 10 millirem per year (mrem/year); the dose from all radiation sources at Berkeley Lab was approximately 0.1% of the average natural background radiation dose of 310 mrem/yr in the United States, and about 0.4% of the DOE annual limit of 100 mrem/yr from all sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Site Environmental Report for 2017

Lawrence Berkeley National Laboratory (LBNL, Berkeley Lab) is a multi-program scientific facility operated by the University of California (UC) for the U.S. Department of Energy (DOE). Berkeley Lab’s research is focused on the physical, biological, environmental, and computational sciences with the objective of delivering scientific knowledge and discoveries pertinent to DOE’s mission. This annual report describes environmental protection activities and potential impacts resulting from LBNL operations conducted in 2017. The format and content of this report satisfy the requirements of both DOE Order 231.1B, Administrative Change 1 (Environment, Safety, and Health Reporting) and the operating contract between UC and DOE (DOE Contract No. DE-AC02-05CH11231, also known as Contract 31). Activities are planned and conducted with full regard to protecting employees, the public, and the environment, as well as complying with all applicable environmental, safety, and health laws and regulations. Berkeley Lab implements an Environmental Management System (EMS) to oversee these compliance activities and continually improve overall environmental performance while maintaining operational capability and sustaining its overall mission. The effectiveness of the EMS and environmental programs is reviewed annually as part of the performance evaluation process of Contract 31. For fiscal year (FY) 2017, which began October 1, 2016, and ended September 30, 2017, the EMS was given a performance rating of B plus for its management of environmental activities (on a scale from A plus as the highest grade to F as the lowest). The measurement and rating system was developed jointly by Berkeley Lab, UC, and DOE. The FY 2017 rating was based on how Berkeley Lab met the objective in DOE’s FY 2017 Performance Evaluation and Measurement Plan (Appendix B in Contract 31, Section J) of providing an efficient and effective EMS. Six significant accomplishments, which ranged from effective teaming on projects and with regulatory agencies to improved program assessment approaches, were factors in the rating. The EMS was also graded through the federal Office of Management and Budget’s annual EMS performance metrics, in which a reporting scorecard rates elements of the International Organization for Standardization (ISO) 14001 standard and the degree of integration between the EMS and Berkeley Lab’s sustainable practices. Overall scores fall into one of three categories: green (highest), yellow (middle), or red (lowest). For FY 2017, Berkeley Lab received a score of green, as described in more detail in Chapter 2. An overview of environmental protection and restoration programs is provided, including information about compliance activities, operating permits, and regulatory agency inspections that occurred during 2017. Thirteen minor violations issued during City of Berkeley inspections of above and underground storage tanks, treatment units, and hazardous waste storage areas are discussed in this chapter. This report also includes information on environmental monitoring performed in 2017. The results of these monitoring activities confirmed that groundwater cleanup actions continue to show improving conditions, and all emissions and discharges from LBNL operations were within environmental compliance release limits, with the exception of some stormwater discharges. Most stormwater discharges measured throughout the LBNL site fall within acceptable levels established by the state’s stormwater permit; however, iron and aluminum exceeded Numeric Action Levels. To reduce iron and aluminum discharge levels, Berkeley Lab is aggressively implementing controls such as construction of asphaltic berms and check dams to restrain and filter runoff to storm drains, and installation of filtration units in storm drain basins to collect sediment and absorb metal contaminants. The radiological dose assessments performed in 2017 concluded that the maximum potential dose to a hypothetical resident from Berkeley Lab’s airborne radionuclide releases was approximately 0.1% of the DOE and U.S. Environmental Protection Agency annual limit of 10 millirem per year (mrem/yr); the potential dose from all radiation sources at Berkeley Lab was approximately 0.06% of the average natural background radiation dose of 310 mrem/yr in the United States, and about 0.2% of the DOE annual limit of 100 mrem/yr from all sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Site Environmental Report for 2018

Lawrence Berkeley National Laboratory (LBNL, Berkeley Lab) is a multi-program scientific facility operated by the University of California (UC) for the U.S. Department of Energy (DOE). Berkeley Lab’s research is focused on the physical, biological, environmental, and computational sciences, with the objective of delivering scientific knowledge and discoveries pertinent to DOE’s mission. This annual report describes environmental protection activities and potential impacts resulting from LBNL operations conducted in 2018. The format and content of this report satisfy the requirements of both DOE Order 231.1B, Administrative Change 1 (Environment, Safety, and Health Reporting) and the operating contract between UC and DOE (DOE Contract No. DE-AC02-05CH11231, also known as Contract 31). LBNL activities are planned and conducted with full regard to protecting employees, the public, and the environment, as well as complying with all applicable environmental, safety, and health laws and regulations. As presented in Chapter 2, Berkeley Lab implements an Environmental Management System (EMS) to oversee environmental compliance activities and continually improve overall environmental performance while maintaining operational capability and sustaining its overall mission. The effectiveness of the EMS and environmental programs is reviewed annually as part of the performance evaluation process of Contract 31. For fiscal year (FY) 2018, which began October 1, 2017, and ended September 30, 2018, the EMS was given a performance rating of B plus for its management of environmental activities (on a scale from A plus as the highest grade, to F as the lowest). The measurement and rating system was developed jointly by Berkeley Lab, UC, and DOE. The FY 2018 rating was based on how Berkeley Lab met the objective in DOE’s FY 2018 Performance Evaluation and Measurement Plan (Appendix B in Contract 31, Section J) of providing an efficient and effective EMS. Six significant accomplishments, which ranged from effective teaming on projects and with regulatory agencies to improved program assessment approaches, were factors in the rating. The EMS was also graded through the federal Office of Management and Budget’s annual EMS performance metrics, in which a reporting scorecard rates elements of the International Organization for Standardization (ISO) 14001 standard and the degree of integration between the EMS and Berkeley Lab’s sustainable practices. Overall scores fall into one of three categories: green (highest), yellow (middle), or red (lowest). For FY 2018, Berkeley Lab received a score of green. A week-long formal conformity audit of the EMS was conducted at the end of July by a qualified external party, as is required every three years. The audit team found that Berkeley Lab’s EMS continues to conform to the ISO standard with no major nonconformance. An overview of environmental protection and restoration programs is provided in Chapter 3, including information about compliance activities, operating permits, and regulatory agency inspections and audits that occurred during 2018. Twenty-one minor violations and two Class II violations were issued during agency inspections of programs governing aboveground and underground storage tanks, hazardous waste treatment units, hazardous waste storage areas, hazardous materials management, and wastewater, stormwater, and air emission sources. This report also includes information on environmental monitoring performed in 2018 (Chapter 4). The results of these monitoring activities confirmed that groundwater cleanup actions have been effective in reducing concentrations of volatile organic compounds (VOCs) in the groundwater, although concentrations appear to be reaching asymptotic levels in some areas. Site groundwater plumes are stable or are attenuating, and VOCs are not migrating off site. All emissions and discharges from LBNL operations were within environmental compliance release limits except oxides of nitrogen and carbon monoxide from boilers (under 5 million BTUs) from Buildings 2, 66, and 88. The radiological dose assessments (Chapter 5) performed in 2018 concluded that the maximum potential dose to a hypothetical resident from Berkeley Lab’s airborne radionuclide releases was approximately 0.04% of the DOE and U.S. Environmental Protection Agency annual limit of 10 millirem per year (mrem/yr); the potential dose from all radiation sources at Berkeley Lab was approximately 0.15% of the average natural background radiation dose of 310 mrem/yr in the United States, and about 0.5% of the DOE annual limit of 100 mrem/yr from all sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Investigating Scientific Workload Acceleration using BlueField SmartNICs [Slides]

Modern computing platforms whose workloads generate large amounts of network traffic, such as cloud and HPC systems, often suffer from performance bottlenecks associated with the network interface. In order to alleviate the effects of this obstacle, a new generation of accelerators known as ‘SmartNICs’, which are designed to offload low level networking tasks from the processor into the NIC, have emerged.

42 ENGINEERING↗
DOE OSTI2022

2021 Site Environmental Report (SER) for the Ernest Orlando Lawrence Berkeley National Laboratory (LBNL)

Lawrence Berkeley National Laboratory (LBNL, Berkeley Lab) is a multiprogram scientific facility operated by the University of California (UC) for the U.S. Department of Energy (DOE). Berkeley Lab’s research is focused on the physical, biological, environmental, and computational sciences, with the objective of delivering scientific knowledge and discoveries pertinent to DOE’s mission. This annual report describes environmental protection activities and potential impacts resulting from operations conducted in calendar year 2021, unless otherwise indicated. The format and content of this report satisfy the requirements of both DOE Order 231.1B, Administrative Change 1 (Environment, Safety, and Health Reporting) (DOE, 2012) and the operating contract between UC and DOE (DOE Contract No. DE-AC02-05CH11231, also known as Contract 31).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Parallelization and Visual Analysis of Multidimensional Fields: Application to Ozone Production, Destruction, and Transport in Three Dimensions

This final report has four sections. We first describe the actual scientific results attained by our research team, followed by a description of the high performance computing research enhancing those results and prompted by the scientific tasks being undertaken. Next, we describe our research in data and program visualization motivated by the scientific research and also enabling it. Last, we comment on the indirect effects this research effort has had on our work, in terms of follow up or additional funding, student training, etc.

Schwan, Karsten↗
DOE OSTI2024

PanDA: Production and Distributed Analysis System

The Production and Distributed Analysis (PanDA) system is a data-driven workload management system engineered to operate at the LHC data processing scale. The PanDA system provides a solution for scientific experiments to fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, and robustness. The system has successfully proven itself through nearly two decades of steady operation in the ATLAS experiment, addressing the intricate requirements such as diverse resources distributed worldwide at about 200 sites, thousands of scientists analyzing the data remotely, the volume of processed data beyond the exabyte scale, dozens of scientific applications to support, and data processing over several billion hours of computing usage per year. PanDA’s flexibility and scalability make it suitable for the High Energy Physics community and wider science domains at the Exascale. Beyond High Energy Physics, PanDA’s relevance extends to other big data sciences, as evidenced by its adoption in the Vera C. Rubin Observatory and the sPHENIX experiment. As the significance of advanced workflows continues to grow, PanDA has transformed into a comprehensive ecosystem, effectively tackling challenges associated with emerging workflows and evolving computing technologies. The paper discusses PanDA’s prominent role in the scientific landscape, detailing its architecture, functionality, deployment strategies, project management approaches, results, and evolution into an ecosystem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Animated computer graphics models of space and earth sciences data generated via the massively parallel processor

The capability was developed of rapidly producing visual representations of large, complex, multi-dimensional space and earth sciences data sets via the implementation of computer graphics modeling techniques on the Massively Parallel Processor (MPP) by employing techniques recently developed for typically non-scientific applications. Such capabilities can provide a new and valuable tool for the understanding of complex scientific data, and a new application of parallel computing via the MPP. A prototype system with such capabilities was developed and integrated into the National Space Science Data Center's (NSSDC) Pilot Climate Data System (PCDS) data-independent environment for computer graphics data display to provide easy access to users. While developing these capabilities, several problems had to be solved independently of the actual use of the MPP, all of which are outlined.

Treinish, Lloyd A.↗
DOE OSTI2025

A terminology for scientific workflow systems

The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. Furthermore, this terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.

Community-based terminology↗
DOE OSTI2019

Parallel membership queries on very large scientific data sets using bitmap indexes

© 2019 John Wiley & Sons, Ltd. Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating-point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word-Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

Yildiz, Beytullah↗
DOE OSTI2022

The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)

Abstract. Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction.

54 ENVIRONMENTAL SCIENCES↗