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At least 415 records · Page 23

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DOE OSTI2023

Finalizing Transition to the New Data Center at BNL

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes

The OSU/PNNL Superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons (PAHs) at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. Toward this end, we have linked over 150 PAHs found at Superfund sites to the effect of these same chemicals in zebrafish, creating a rich dataset that links environmental exposure to biological response. To quantify this response, we have implemented a dose-response modelling pipeline to calculate benchmark dose parameters which enable potency comparison across over 500 chemicals and 10 zebrafish-specific phenotypes. Our portal provides public access to this dataset via an interactive web site designed to support exploration and re-use of these data by the scientific community at http://srp.pnnl.gov.

Gosline, Sara JC↗

DOE OSTI2024

Ongoing Cooperative Engagement Facilitates Agile Pandemic and Outbreak Response: Lessons Learned Through Cooperative Engagement Between Uganda and the United States

Pathogens threaten human lives and disrupt economies around the world. This has been clearly illustrated by the current COVID-19 pandemic and outbreaks in livestock and food crops. Here, to manage pathogen emergence and spread, cooperative engagement programs develop and strengthen biosafety, biosecurity, and biosurveillance capabilities among local researchers to detect pathogens. In this case study, we describe the efforts of a collaboration between the Los Alamos National Laboratory and the Uganda Virus Research Institute, the primary viral diagnostic laboratory in Uganda, to implement and ensure the sustainability of sequencing for biosurveillance. We describe the process of establishing this capability along with the lessons learned from both sides of the partnership to inform future cooperative engagement efforts in low- and middle-income countries. We found that by strengthening sequencing capabilities at the Uganda Virus Research Institute before the COVID-19 pandemic, the institute was able to successfully sequence SARS-CoV-2 samples and provide data to the scientific community. We highlight the need to strengthen and sustain capabilities through in-country training, collaborative research projects, and trust.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A modERN resource: identification of Drosophila transcription factor candidate target genes using RNAi

Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change -3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies (ERR, sens, and zfh2) and two previously uncharacterized TFs (sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC (http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A unique, ring-like radio source with quadrilateral structure detected with machine learning

ABSTRACT We report the discovery of a unique object in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) using the machine learning anomaly detection framework astronomaly. This strange, ring-like source is 30′ from the MGCLS field centred on Abell 209, and is not readily explained by simple physical models. With an assumed host galaxy at redshift 0.55, the luminosity (1025 W Hz−1) is comparable to powerful radio galaxies. The source consists of a ring of emission 175 kpc across, quadrilateral enhanced brightness regions bearing resemblance to radio jets, two ‘ears’ separated by 368 kpc, and a diffuse envelope. All of the structures appear spectrally steep, ranging from −1.0 to −1.5. The ring has high polarization (25 per cent) except on the bright patches (<10 per cent). We compare this source to the Odd Radio Circles recently discovered in ASKAP data and discuss several possible physical models, including a termination shock from starburst activity, an end-on radio galaxy, and a supermassive black hole merger event. No simple model can easily explain the observed structure of the source. This work, as well as other recent discoveries, demonstrates the power of unsupervised machine learning in mining large data sets for scientifically interesting sources.

Astronomy & Astrophysics↗

DOE OSTI2024

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators

Tensor algebra operations represent an important class of algorithms used across many applications, including machine learning, scientific computing, and data analytics. As a result, the efficient generation of custom accelerators for tensor operations has received increased attention. Previous efforts have produced automated tools enabling users to prototype and explore optimized accelerators. However, little effort has been focused on the host-accelerator interaction in these tools. Efficient use of hardware accelerators requires knowledge about the accelerator's capabilities (operations, data formats, and opcode support), the host CPU microarchitecture (e.g., memory hierarchy), the host-accelerator interface, and the application's features (which code regions should be mapped onto an accelerator). Manually rewriting the original applications to facilitate improved custom accelerator mapping is an error-prone and time-consuming endeavor. To cope with this, we propose AXI4MLIR, a new framework to automatically generate and optimize the communication between the host CPU and arbitrary accelerators that implement linear algebra algorithms. AXI4MLIR extends the MLIR compiler framework to automatically generate efficient host-accelerator driver code for accelerators with AXI-based interfaces. Our compiler extensions enable automatic driver code generation while carefully considering the host's memory hierarchy and target accelerator features. To demonstrate the flexibility and utility of AXI4MLIR, we test it with diverse use cases that include different types of accelerators, tiling scenarios, and dataflow schemes. We compare our experimental results to manual implementations of host-accelerator driver code and find that our approach can reduce CPU cache references by 56% and deliver up to a 1.65x speedup.

Bohm Agostini, Nicolas↗

DOE OSTI2020

ARIC 2019 workshop report: The 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities: Chicago, IL, USA November 5, 2019

The advancements in sensor technology and ubiquity of connected devices has enabled the generation of large volume of disparate, dynamic and geographically distributed data both by scientific communities and citizens. With astonishing technological innovations and convergence, there have been major changes in peoples daily activities and social interaction. The socio-technological innovations motivate the concept of smart and connected cities. A smart city, however, is subjected to the same challenges as a conventional city, such as environmental damages, hazard impacts, access to services and resources, due to continuous population and economic growth. Therefore, it is imperative to improve our understanding of Resilient and Intelligent Cities in order to leverage technologies and artificial intelligence to tackle the challenges cities face, which range from climate change, public health, traffic congestion, economic growth, to digital divide, social equity, political movements, and cultural conflicts, among others.

Kar, Bandana↗

DOE OSTI2022

The SODA Approach: Leveraging High-Level Synthesis for Hardware/Software Co-design and Hardware Specialization: Invited

Novel "converged" applications combine phases of scientific simulation with data analysis and machine learning. Each computational phase can benefit from specialized accelerators. However, algorithms evolve so quickly that mapping them on existing accelerators is suboptimal or even impossible. This paper presents the SODA (Software Defined Accelerators) framework, a modular, multi-level, open-source, no-human-in-the-loop, hardware synthesizer that enables end-to-end generation of specialized accelerators. SODA is composed of SODA-Opt, a high-level frontend developed in MLIR that interfaces with domain-specific programming frameworks and allows performing system level design, and Bambu, a state-of-the-art high-level synthesis engine that can target different device technologies. The framework implements design space exploration as compiler optimization passes. We show how the modular, yet tight, integration of the high-level optimizer and lower-level HLS tools enables the generation of accelerators optimized for the computational patterns of converged applications. We then discuss some of the research opportunities that such a framework allows, including system-level design, profile driven optimization, and supporting new optimization metrics.

Bohm Agostini, Nicolas↗

DOE OSTI2020

Indira: intent-based networking and rendering for networks (Indira) v1

Intelligent Network Deployment Intent Renderer Application an interactive network assistant that will help us configure a data path between two scientific end-point groups (EPGs) to optimize the transfer of elephant data flows. This tool is able to talk to scientists gather network requirements and then convert to network programming code and automate the end-to-end provisioning.

Pouyoul, Eric↗

DOE OSTI2020

The Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Activity for Continental Shallow Convection

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

FaceBase 3: analytical tools and FAIR resources for craniofacial and dental research

ABSTRACT The FaceBase Consortium was established by the National Institute of Dental and Craniofacial Research in 2009 as a ‘big data’ resource for the craniofacial research community. Over the past decade, researchers have deposited hundreds of annotated and curated datasets on both normal and disordered craniofacial development in FaceBase, all freely available to the research community on the FaceBase Hub website. The Hub has developed numerous visualization and analysis tools designed to promote integration of multidisciplinary data while remaining dedicated to the FAIR principles of data management (findability, accessibility, interoperability and reusability) and providing a faceted search infrastructure for locating desired data efficiently. Summaries of the datasets generated by the FaceBase projects from 2014 to 2019 are provided here. FaceBase 3 now welcomes contributions of data on craniofacial and dental development in humans, model organisms and cell lines. Collectively, the FaceBase Consortium, along with other NIH-supported data resources, provide a continuously growing, dynamic and current resource for the scientific community while improving data reproducibility and fulfilling data sharing requirements.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Discovering the 3D Structure and Dynamics of the Sun-Interstellar Medium System on a Global Scale Los Alamos LDRD Report

We discover the 3D plasma structure of the Sun-interstellar medium interaction region, called the heliosheath. This is enabled by (1) the completion of a full solar cycle of observations of hydrogen energetic neutral atoms (ENAs) emanating from this region and detected by the Los Alamos-led IBEX-Hi ENA imager on NASA’s Interstellar Boundary Explorer (IBEX) mission, and (2) the development of tools to fully exploit all data from the IBEX-Hi imager, leading to a significant increase in the effective instrument sensitivity. This effort prepares us for optimizing the design and data exploitation of the IMAP-Hi imager, the Los Alamos-led next generation imager that will fly on the Interstellar Mapping and Acceleration Probe (IMAP) mission, currently under production. This project realized its goals as evidenced by two main outcomes: (1) The publication of the first-ever empirical 3D map of the interstellar boundary, and (2) the development and release of a new validated ENA data set that has tripled the amount of data available for scientific analysis of the outer heliosphere.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Software Defined Architectures for Portability and Performance

The Software Defined Architectures for Portability and Performance (SODAPOP) project developed a co-design framework to partition and map converged applications on specialized heterogeneous architectures. We started from key domain applications that combine scientific simulation with data analytics and machine learning as drivers to integrate our framework. The framework includes high-level compilers that interfaces with high-level programming frameworks, domain-specific optimization passes, and hardware-oriented optimizations. The framework leverages hardware generators to enable specialization and facilitate exploration of custom system designs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

The OSU/PNNL superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. The PNNL Superfund have implemented a dose-response modelling pipeline to calculate benchmark dose parameters that enable the comparison of potency across chemicals and phenotypes. Our portal provides public access to this dataset and an interactive web site designed to enable exploration and re-use of this data by the scientific community at http://srp.pnnl.gov.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

OSU-PNNL Superfund Research Program Center SRP↗

DOE OSTI2025

Biomass for Carbon Removal and Storage (BiCRS) Variables Matrices

The Biomass for Carbon Removal and Storage (BiCRS) Variables Matrices provide insights related to measured, monitored, reported and verified (MMRV) components (variables) across a BiCRS value chain. Suggested variable values and components to be measured with literature references and context obtained from industry and scientists are provided for several biomass sources, conversion routes and storage scenarios. The Variables Matrices are meant to support life cycle assessment and MMRV efforts for BiCRS projects. These variables and insights may need to be updated in the future as additional information, scientific advancements, and data become available (Version 1 issued in 2025).

09 BIOMASS FUELS↗

DOE OSTI2021

EXPERIMENTAL STUDIES OF NONLINEAR INTEGRABLE OPTICS

State-of-the-art accelerators at energy and intensity frontiers require increasingly bright and powerful particle beams. In conventional linear lattices, intense beams suffer from collective instabilities, resulting in beam losses and maximum beam intensity limits. This thesis presents experimental studies of a novel lattice design concept, the nonlinear integrable optics (NIO), aimed at enhancing beam stability limits with little to no beam dynamics degradation. Single-particle beam dynamics measurements of two NIO devices, the quasi-integrable octupole system and the fully integrable Danilov-Nagaitsev system, were carried out at the purpose-built Integrable Optics Test Accelerator (IOTA) at Fermilab. Their simulation, hardware design, and commissioning process are presented. Extensive model and analysis algorithm development and benchmarking is described. Electron beam data from two scientific runs is analyzed, yielding frequency and phase space dynamics consisten t with m odels. These results demonstrate viability and advantages of the NIO design, providing the groundwork for proton studies in the strong space-charge regime and future integrable accelerators.

Kuklev, Nikita↗

DOE OSTI2021

Soil moisture and temperature data along the northeast facing hillslope at the Lower Montane site in the East River Watershed, Colorado

Soil moisture, temperature and electrical conductivity have been monitored with 5TE sensors (Decagon Inc.) located at 10 and 50 cm depth at 4 different locations along the northeast-facing slope at the Lower Montane site in the East River Watershed, Colorado from Oct 2016 to Oct 2018. The purpose of this data is to inform hydrobiogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). The data is reported in 4 csv files labeled their by location identifiers (ER-SMN1-5) and location metadata is provided in SM_loc.csv. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

54 ENVIRONMENTAL SCIENCES↗