Results for “Data Intensive Science” — Page 4

DOE OSTI2020

End-to-end online performance data capture and analysis for scientific workflows

With the increased prevalence of employing workflows for scientific computing and a push towards exascale computing, it has become paramount that we are able to analyze characteristics of scientific applications to better understand their impact on the underlying infrastructure and vice-versa. Such analysis can help drive the design, development, and optimization of these next generation systems and solutions. Here, we present the architecture, integrated with existing well-established and newly developed tools, to collect online performance statistics of workflow executions from various, heterogeneous sources and publish them in a distributed database (Elasticsearch). Using this architecture, we are able to correlate online workflow performance data, with data from the underlying infrastructure, and present them in a useful and intuitive way via an online dashboard. We have validated our approach by executing two classes of real-world workflows, both under normal and anomalous conditions. The first is an I/O-intensive genome analysis workflow; the second, a CPU- and memory-intensive material science workflow. Based on the data collected in Elasticsearch, we are able to demonstrate that we can correctly identify anomalies that we injected. The resulting end-to-end data collection of workflow performance data is an important resource of training data for automated machine learning analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Data Intensive Scientific Workflows on a Federated Cloud: CRADA Final Report

The Fermilab Scientific Computing Division and the KISTI Global Science Experimental Data Hub Center have built a prototypical large-scale infrastructure to handle scientific workflows of stakeholders to run on multiple cloud resources. The demonstrations have been in the areas of (a) Data-Intensive Scientific Workflows on Federated Clouds, (b) Interoperability and Federation of Cloud Resources, and (c) Virtual Infrastructure Automation to enable On-Demand Services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Management and Storage of Scientific Data

Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2019

Provenance–aware workflow for data quality management and improvement for large continuous scientific data streams

Data quality assessment, management and improvement is integral part of any big data intensive scientific research to ensure accurate, reliable and reproducible science discoveries. The task of maintaining the quality of data, however, is non-trivial and pose a challenge for a program like Department of Energy’s Atmospheric Radiation Measurement (ARM) that collects data from hundreds of instruments across the world,and distributes thousands of streaming data products that are continuously growing in near-real-time in an archive 1.7 Petabyte in size and growing. We present a computational data processing workflow to collect the data quality issue via an easy and intuitive web-based portal that allows reporting of any quality issues for any site, facility or instruments at a granularity down to individual variable in the data files. Portal allows instrument specialists and scientists to provide corrective actions in form of symbolic equation. A parallel processing framework applies the data improvement to large volume of data in efficient parallel environment, while optimizing data transfer and file I/O operations. Corrected files are systematically versioned and archived. A provenance tracking module tracks and records any change made to the data during its entire life cycle which are communicated transparently to the scientific users. Developed in Python using open source technologies, software architecture enables efficient and fast management and improvement of data in an operational data center environment.

Kumar, Jitendra↗

DOE OSTI2019

Integrating Data Science Methods and Life Cycle Assessment (LCA): Application to the Power Sector - ISSST2019

This work was presented at the International Symposium on Sustainable Systems and Transportation (ISSST) 2019 Conference in Portland, OR on June 26, 2019. Data science is radically transforming the scientific landscape. Data-intensive frameworks such as LCA can uniquely benefit from the establishment of large-scale and national datasets, and the use of data-science methods. Additionally, the synthesis and application of these data repositories in LCA can aid in improving emissions inventories, technological characterization of product systems, and uncertainty quantification. In this work, a series of case studies are used to explore the intersection of data science and LCA of the power sector. The objective of this work is to provide a broad cross-section of data sources, methods, and approaches that have been applied to characterize the life cycle of power systems, but are generalizable across multiple LCA research domains.

Littlefield, James↗

DOE OSTI2023

New technologies as decision aids for the advancement of ecological risk assessment

Moore's law states that the number of transistors that can be placed on an integrated circuit doubles every two years (Moore, 1975). This has led to a steady increase in the processing power of computers over time, and technology is now enhancing and advancing software and scientific applications, which has enabled computationally intensive methods such as machine learning, data science, modeling, and simulation. The advancement of computers and data-driven algorithms is profoundly impacting people's lives. It is changing the way we work, the way we learn, and the way we interact with the world around us. Here, this editorial will discuss how scientists can benefit from the latest technology advancements and related tools by incorporating them into the ecological risk assessment (ERA) to study ecosystems as a way to create refined assessments and accelerate the turnaround times.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Using NVMe Gen3 PCIe SSD Cards in High-density Servers for High-performance Big Data Transfer Over Multiple Network Channels

This Technical Note describes how the Zettar team came up with a data transfer cluster design that convincingly proved the feasibility of using high-density servers for high-performance Big Data transfers. It then outlines the tests, operations, and observations that address a potential over-heating concern regarding the use of Non-Volatile Memory Host Controller Interface Specification (NVMHCI aka NVM Express or NVMe) Gen 3 PCIe SSD cards in high-density servers. Finally, it points out the possibility of developing a new generation of high-performance Science DMZ data transfer system for the data-intensive research community and commercial enterprises.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Deep Learning and Structural Imaging of Materials

Deep learning has had a transformative effect on numerous domains and is actively utilized by many scientists in data-intensive fields such as high-energy physics and cosmology. Materials science, and in particular, the structural imaging of materials with electrons and X-rays are projected to enter the age of scientific data torrents, positioning them as new application spaces for modern artificial intelligence. In this contribution, we provide a synopsis on the foundations and latest progress in deep learning and present an in-depth application of utilizing modern deep artificial neural networks in scanning transmission electron microscopy to extract structural material properties. We use this case study to expose the strengths of deep learning-based models and discuss their current limitations, in the process highlighting their potential use in other data-intensive structural imaging modalities.

Laanait, Nouamane↗

DOE OSTI2014

FTP 100: An Ultra-High Speed Data Transfer Service Over Next Generation 100 Gigabit Per Second Network

Data-intensive applications, including high energy and nuclear physics, astrophysics, climate modeling, nano-scale materials science, genomics, and financing, are expected to generate exabytes of data over the coming years, which must be transferred, visualized, and analyzed by geographically distributed teams of users. High-performance network capabilities must be available to these users at the application level in a transparent, virtualized manner. Moreover, the application users must have the capability to move large datasets from local and remote locations across network environments to their home institutions. To solve these challenges, the main goal of our project is to design and evaluate high-performance data transfer software to support various data-intensive applications. First, we have designed a middleware software that provides access to Remote Direct Memory Access (RDMA) functionalities. This middleware integrates network access, memory management and multitasking in its core design. We address a number of issues related to its efficient implementation, for instance, explicit buffer management and memory registration, and parallelization of RDMA operations, which are vital to delivering the benefit of RDMA to the applications. Built on top of this middleware, an implementation and experimental evaluation of the RDMA-based FTP software, RFTP, is described and evaluated. This application has been implemented by our team to exploit the full capabilities of advanced RDMA mechanisms for ultra-high speed bulk data transfer applications on Energy Sciences Network (ESnet). Second, we designed our data transfer software to optimize TCP/IP based data transfer performance such that RFTP can be fully compatible with today’s Internet. Our kernel optimization techniques with Linux system calls sendfile and splice, can reduce data copy cost. In this report, we summarize the technical challenges of our project, the primary software design methods, the major project milestones achieved, as well as the testbed evaluation work and demonstrations during our project life time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A Code Generator for High-Performance Tensor Contractions on GPUs

Tensor contractions are higher dimensional generalizations of matrix-matrix multiplication. They form the compute-intensive core for many applications in computational science and data science. Although high-performance library implementations are widely available for dense matrix-matrix multiplication on GPUs, the same is not true for contraction of dense tensors. In this paper, we describe a high- performance GPU code generator for arbitrary tensor contractions. It exploits domain-specific properties about data reuse in tensor contractions to devise an effective code generation schema, coupled with an effective model-driven search to determine parameters for mapping of computation to threads and staging of data through the GPU memory hierarchy. Experimental evaluation using a set of tensor contraction benchmarks demonstrates performance improvement and/or significantly reduced code generation time over other state-of-the-art tensor contraction libraries and code generators.

Kim, Jinsung↗

DOE OSTI2017

HEP Analysis Ecosystem Workshop Report

Over the past 20 years the HEP community has developed and gravitated around an analysis ecosystem centered on ROOT. ROOT and its ecosystem both dominate HEP analysis and impact the full event processing chain, providing foundation libraries, I/O services etc. that have prevalence in the field. The analysis tools landscape is however evolving in ways that can have a durable impact on the analysis ecosystem and a strong influence on the analysis and core software landscape a decade from now, a timescale currently in an intensive planning round with the HEP Software Foundation(HSF) Community White Paper process. Data intensive analysis is growing in importance in other sciences and in the wider world. Powerful tools and new development initiatives, both within our field and in the wider open source community, have emerged. Creative developers have an ever more powerful open source toolkit available and are applying it towards innovations that leverage both open source and the ROOT ecosystem. ROOT itself is approaching a major re-engineering with ROOT 7, leveraging the powerful evolution of C++, with a major overhaul in its interfaces as seen both by its users and by satellite tools in the ROOT ecosystem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tool and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the- art methods.

Guo, Luanzheng [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗

DOE OSTI2019

A Software Defined Storage Approach to Exascale Storage Services (Final Technical Report)

The Mochi project began in 2015 in recognition of a set of changes that were set to alter the landscape of storage and I/O for DOE science. On the one hand, an influx of new classes of applications including an initial set of data intensive and machine learning applications were operating with new types of data and in new ways, as compared to traditional computational science simulation codes. Simultaneously, new memory, storage, and networking technologies were driving a need to adapt software designs to a rapidly changing design space. The goal of the three-year Mochi project was to develop and demonstrate an approach for composing storage services which provides new levels of functionality, performance, and reliability for science applications at extreme scale. The team, including a mix of Laboratory, university, and industry collaborators, focused on systems and applications of interest to DOE Office of Science, tracking developments at NERSC and the LCFs as well as application trends within ECP and SciDAC. Technical work focused on three themes: composition (understand the building blocks needed to provide storage services that meet DOE mission needs and how these components are flexibly composed), application interfacing (what service compositions are most effective for common DOE application use cases), and consistency and synchronization (taking advantage of knowledge about, and from, the system and scientific applications to provide better service than would otherwise be possible). The project was highly successful, producing 13 publications and tens of thousands of lines of open source software that is now being used by multiple ECP teams, HPC software vendors, and international collaborators. The uptake of Mochi technologies speaks to both the value of the service composition model and the quality of architecture and software developed by the team. Through continued funding under ASCR and ECP, the Mochi team and its technologies are on track to further contribute to DOE science successes into the exascale timeframe.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Final Report

A partnership across government, academic, and private sectors has created a novel system that enables climate researchers to solve current and emerging data analysis and visualization challenges. The Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) software project utilizes the Python application programming interface (API) combined with C/C++/Fortran implementations for performance-critical software that offers the best compromise between "scalability" and “ease-of-use.” The UV-CDAT system is highly extensible and customizable for high-performance interactive and batch visualization and analysis for climate science and other disciplines of geosciences. For complex, climate data-intensive computing, UV-CDAT’s inclusive framework supports Message Passing Interface (MPI) parallelism as well as taskfarming and other forms of parallelism. More specifically, the UV-CDAT framework supports the execution of Python scripts running in parallel using the MPI executable commands and leverages Department of Energy (DOE)-funded general-purpose, scalable parallel visualization tools such as ParaView and VisIt. This is the first system to be successfully designed in this way and with these features. The climate community leverages these tools and others, in support of a parallel client-server paradigm, allowing extreme-scale, server-side computing for maximum possible speed-up.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Active Subspace Methods for Data-Intensive Inverse Problems (Final Report)

Fostered by advances in computing, simulation and data science have emerged as the third and fourth paradigms enabling scientific discovery. Many mathematical challenges arise when integrating these paradigms to produce new knowledge. One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements. As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable; modern scientists need more computationally efficient and mathematically rigorous methods. When measurements and simulated predictions contain uncertainty, the Bayesian perspective on statistical inference provides a popular mathematical framework for developing computational methods to solve inverse problems. Markov chain Monte Carlo (MCMC) methods are a mainstay of this framework; they characterize a probability distribution on the space of model inputs that is conditioned on the given measurements. However, despite recent advances, MCMC methods remain impractical for expensive simulations with more than a handful of inputs. At its heart, MCMC searches the space of inputs for corresponding outputs that match measurements, and so it is afflicted with the curse of dimensionality. Additionally, MCMC methods become less efficient as the volume of the measurement data increases, particularly if the measurements are redundant. To make MCMC—and Bayesian methods, in general—amenable to today’s computer models with millions of inputs and terabytes of measurement data, one must devise methods to reduce the dimension of both the simulation inputs and the measurements. For many models used in practice, the outputs vary primarily on a low-dimensional manifold of the nominally high-dimensional input space. Recently developed active subspace methods discover this low-dimensional manifold and exploit it to enable analyses typically suited for low-dimensional settings (e.g., statistical inversion of complex systems). We hypothesize that active subspace methods can provide the necessary dimension reduction, enabling existing Bayesian methods to find solutions for a large class of otherwise intractable statistical inverse problems. We will test the hypothesis with a series of mathematical analyses and computational experiments that will proceed along three research veins: (i) integrating active subspace methods into methods for statistical inversion, (ii) addressing current challenges in active subspace methods, and (iii) incorporating methods for nonlinear dimensionality reduction into active subspace methods. We will apply the methods we develop to real inverse problems in chemical kinetics and turbulent flame modeling. Our team combines expertise in methods for inverse problems, dimension reduction, and reduced order modeling. With a history of active collaboration and thoughtful engagement, we bring enthusiasm and fresh perspectives to a well studied subject. We are excited by the potential opportunity to work together to extend and apply active subspace methods to data-intensive inverse problems.

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↗