Closing the Data Gaps for Storage and Transportation of Spent Nuclear Fuel.
Abstract not provided.
Sorenson, Ken B.↗
SEARCH · Engineering Papers
Results for “DATA STORAGE”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Carbon Storage Wellbore Materials Data Tool
For presentation at the Carbon Management Review Meeting 2023, Pittsburgh, PA, August 28-September 1, 2023.
Montross, Scott↗
BASTet (Berkeley Analysis and Storage Toolkit)
BASTet is an advanced software library written in Python. BASTet serves as the analysis and storage library for the OpenMSI project. BASTet is an integrate framework for: i) storage of spectral imaging data, ii) storage of derived analysis data, iii) provenance of analyses, iv) integration and execution of analyses via complex workflows. BASTet implements the API for the HDF5 storage format used by OpenMSI. Analyses that are developed using BASTet benefit from direct integration with storage format, automatic tracking of provenance, and direct integration with command-line and workflow execution tools. BASTet also defines interfaces to enable developers to directly integrate their analysis with OpenMSI's web-based viewing infrastruture without having to know OpenMSI. BASTet also provides numerous helper classes and tools to assist with the conversion of data files, ease parallel implementation of analysis algorithms, ease interaction with web-based functions, description methods for data reduction. BASTet also includes detailed developer documentation, user tutorials, iPython notebooks, and other supporting documents.
Bowen, Benjamin↗
Towards Efficient Scientific Data Management Using Cloud Storage
A software prototype allows users to backup and restore data to/from both public and private cloud storage such as Amazon's S3 and NASA's Nebula. Unlike other off-the-shelf tools, this software ensures user data security in the cloud (through encryption), and minimizes users operating costs by using space- and bandwidth-efficient compression and incremental backup. Parallel data processing utilities have also been developed by using massively scalable cloud computing in conjunction with cloud storage. One of the innovations in this software is using modified open source components to work with a private cloud like NASA Nebula. Another innovation is porting the complex backup to- cloud software to embedded Linux, running on the home networking devices, in order to benefit more users.
He, Qiming↗
Broom: application for non-redundant storage of high throughput sequencing data
Abstract Motivation The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies. Results We present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format. Availability and implementation C++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp. Supplementary information Supplementary data are available at Bioinformatics online.
Albayrak, Levent↗
Data for The Value of Reversible Carbon Storage in a Zero-Emissions World
Atmospheric carbon dioxide removal (CDR) is required to stabilize global temperature. CDR can be achieved via ecosystem-based approaches that are cost-effective but reversible (e.g., soil and forest management) or by more durable but expensive approaches (e.g., direct air capture coupled with geologic storage). Here, we examine trade-offs between these approaches, focusing on timing, climate impacts, and cost. We simulated reversible carbon accrual for a range of CDR contract structures using a general minimalist model of ecosystem carbon cycling, and parameterized it to simulate US agricultural soil management─specifically cover cropping─as a case study. We then quantified the resulting impact on atmospheric carbon and global temperature using a climate model emulator. We find that maintaining a patchwork of reversible CDR projects by replacing lapsed projects with new projects can reduce warming by 22–195 μ°C in 2100 and that the magnitude of this cooling effect depends on how effectively the patchwork is maintained. Long-term maintenance of reversible CDR projects requires institutional stability that cannot be guaranteed over multiple decades. Consequently, effective CDR ultimately requires replacing reversible projects with durable projects. To address this problem, we modeled the cost of replacing reversible agricultural soil CDR with geologic CDR. We found that using reversible CDR as a bridge to durable CDR is potentially more cost-effective as a global cooling strategy (0.20–0.81 billion USD per μ°C avoided) than perpetual maintenance of reversible CDR (0.32–1.31 billion USD per μ°C avoided) or an immediate transition to durable CDR (1.37–2.19 billion USD per μ°C avoided). However, we emphasize that institutional commitments to maintain reversible CDR projects cannot be guaranteed. Reliance on reversible CDR as a bridge to durable CDR therefore carries an unknown amount of risk and will only function if efforts to maintain reversible CDR are robust.
Carbon↗
An Advanced Network and distributed Storage Laboratory (ANDSL) for Data Intensive Science (Final Report)
The original intent of this project was to build and operate an Advanced Network and Distributed Storage Laboratory (ANDSL) for Data Intensive Science that will prepare the Open Science Grid (OSG) community for a new generation of wide area communication capabilities operating at a 100Gb rate. The plan was to engage a team of four dedicated scientists that will design, develop and operate a state of the art laboratory that will be fully integrated into the organizational and operational infrastructure of the OSG. The ANDSL was planned to include host-based hardware and software required to profile, emulate and study real-life distributed storage workloads at rates of 100 Gbps. The laboratory was designed to utilize the Department Of Energy’s Advanced Network Initiative (ANI) 100GbTestbed as connections to the fabric become available so that OSG stakeholders can use it to profile real-life data intensive science applications as well as experiment with advanced storage and data management technologies. The goal of the original program of work was to close the adoption gap, decreasing the time between availability of production 100Gbps links and the end-to-end use of such a network to enable scientific discovery.
100Gb networks↗
Cloud object store for archive storage of high performance computing data using decoupling middleware
Cloud object storage is enabled for archived data, such as checkpoints and results, of high performance computing applications using a middleware process. A plurality of archived files, such as checkpoint files and results, generated by a plurality of processes in a parallel computing system are stored by obtaining the plurality of archived files from the parallel computing system; converting the plurality of archived files to objects using a log structured file system middleware process; and providing the objects for storage in a cloud object storage system. The plurality of processes may run, for example, on a plurality of compute nodes. The log structured file system middleware process may be embodied, for example, as a Parallel Log-Structured File System (PLFS). The log structured file system middleware process optionally executes on a burst buffer node.
97 MATHEMATICS AND COMPUTING↗
Data compression: The end-to-end information systems perspective for NASA space science missions
The unique characteristics of compressed data have important implications to the design of space science data systems, science applications, and data compression techniques. The sequential nature or data dependence between each of the sample values within a block of compressed data introduces an error multiplication or propagation factor which compounds the effects of communication errors. The data communication characteristics of the onboard data acquisition, storage, and telecommunication channels may influence the size of the compressed blocks and the frequency of included re-initialization points. The organization of the compressed data are continually changing depending on the entropy of the input data. This also results in a variable output rate from the instrument which may require buffering to interface with the spacecraft data system. On the ground, there exist key tradeoff issues associated with the distribution and management of the science data products when data compression techniques are applied in order to alleviate the constraints imposed by ground communication bandwidth and data storage capacity.
Tai, Wallace↗
Novel Boron Nitride MXenes as Promising Energy Storage Materials
Data accompanying the Nanoscale journal article titled: Novel Boron Nitride MXenes as Promising Energy Storage Materials
Muraleedharan, Murali Gopal↗
ARCHIE: Data Analysis Acceleration with Array Caching in Hierarchical Storage
© 2018 IEEE. Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.
Dong, Bin↗
Striped tertiary storage arrays
Data stripping is a technique for increasing the throughput and reducing the response time of large access to a storage system. In striped magnetic or optical disk arrays, a single file is striped or interleaved across several disks; in a striped tape system, files are interleaved across tape cartridges. Because a striped file can be accessed by several disk drives or tape recorders in parallel, the sustained bandwidth to the file is greater than in non-striped systems, where access to the file are restricted to a single device. It is argued that applying striping to tertiary storage systems will provide needed performance and reliability benefits. The performance benefits of striping for applications using large tertiary storage systems is discussed. It will introduce commonly available tape drives and libraries, and discuss their performance limitations, especially focusing on the long latency of tape accesses. This section will also describe an event-driven tertiary storage array simulator that is being used to understand the best ways of configuring these storage arrays. The reliability problems of magnetic tape devices are discussed, and plans for modeling the overall reliability of striped tertiary storage arrays to identify the amount of error correction required are described. Finally, work being done by other members of the Sequoia group to address latency of accesses, optimizing tertiary storage arrays that perform mostly writes, and compression is discussed.
Drapeau, Ann L.↗
A comprehensive analysis of transient pressure and rate data from CO 2 storage projects in a depleted pinnacle reef oil field complex, Michigan, USA
Pressure and rate data are commonly recorded as part of a basic monitoring program in CCS projects. This paper discusses the application of multiple analytical techniques to interpret pressure and rate transient data from CO 2 injection and storage operations. The techniques of interest, i.e., injection-falloff analysis, injectivity/productivity index analysis and pressure pulse arrival time analysis, are commonly used in the oil and gas industry to assess reservoir properties, but not well known in the CCS literature (especially the last two). Injection-falloff analysis involves log-log pressure derivative plotting for the falloff data and history-matching of the entire injection-falloff sequence to determine permeability. In the injectivity/productivity index analysis, rate-normalized pressure buildup is plotted against material balance time or ratio of cumulative injection to injection rate to determine the injectivity index (ratio of injection rate to stabilized pressure buildup) which can be related to the permeability-thickness product. The arrival time analysis identifies the arrival of a pressure disturbance (~0.1 psi change from ambient) to determine the hydraulic diffusivity from which permeability can be estimated. The applicability of these techniques is demonstrated via illustrative examples from multiple wells in different pinnacle carbonate reefs undergoing CO 2 -EOR in Northern Michigan. The paper ends with a discussion of the relative merits of each interpretive technique, as well as recommendations that could be useful for other field projects.
42 ENGINEERING↗
Proactive Data Containers for Scientific Storage (Final Report)
Emerging HPC systems are expected to be deployed with an unprecedented level of complexity, due to a deep system memory/storage hierarchy and heterogeneity of the storage hardware. This hierarchy is expected to range from CPU cache through several levels of volatile memory to nonvolatile memory, traditional hard disks, and tape. Simple and efficient methods of data management and movement through this hierarchy is critical for scientific applications using exascale systems. Existing storage system and I/O (SSIO) technologies face severe challenges in dealing with these requirements. POSIX and MPI I/O standards that are the basis for existing I/O libraries and parallel file systems present fundamental challenges in the areas of scalable metadata operations, semantics-based data movement performance tuning, asynchronous operation, and support for scalable consistency of distributed operations. Moving toward new paradigms for SSIO in the extreme-scale era, we have proposed to investigate novel object-based data abstractions and storage mechanisms that take advantage of the deep storage hierarchy and enable proactive automated performance tuning. In order to achieve these overarching goals, we initiated an effort to develop a fundamental new data abstraction, called Proactive Data Containers (PDC). A PDC is a container within a locus of storage (memory, NVRAM, disk, etc.) that stores science data in an object-centric manner. Managing data as objects enables powerful optimization opportunities for data movement and transformations. The R&D focus of this project are: 1) formulation of object-oriented PDCs and their mapping in different levels of the exascale storage hierarchy; 2) efficient strategies for moving data in deep storage hierarchies using PDCs; 3) techniques for transforming and reorganizing data based on application requirements; and 4) novel analysis paradigms for enabling data transformations and user-defined analysis on data in PDCs. Toward achieving these overarching goals, we designed an object-centric application programing interface (API) for HPC, scalable metadata management for object-centric storage systems, and data movement optimizations such as Data Elevator for moving data between two levels of storage devices and TAPIOCA for efficient aggregation of data on compute nodes. We then implemented several components of the PDC system. They include metadata management, data placement services, remote procedure calls, data aggregation, etc. We have put them together into the overall PDC framework.
97 MATHEMATICS AND COMPUTING↗
Orchestration of materials science workflows for heterogeneous resources at large scale
In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.
97 MATHEMATICS AND COMPUTING↗
The Materials Data Facility: Data Services to Advance Materials Science Research
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloudhosted services, data publication and data discovery, with features to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific)and automatically-extractedmetadata in a registrywhile the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. TheMDF services empower individual researchers, research projects, and institutions to (I) publish research datasets, regardless of size, from local storage, institutional data stores, or cloud storage, without involvement of thirdparty publishers; (II) build, share, and enforce extensible domain-specific custom metadata schemas; (III) interact with published data and metadata via representational state transfer (REST) application program interfaces (APIs) to facilitate automation, analysis, and feedback; and (IV) access a data discovery model that allows researchers to search, interrogate, and eventually build on existing published data. We describe MDF’s design, current status, and future plans.
data management↗
Active Flash: Out-of-core Data Analytics on Flash Storage
Next generation science will increasingly come to rely on the ability to perform efficient, on-the-fly analytics of data generated by high-performance computing (HPC) simulations, modeling complex physical phenomena. Scientific computing workflows are stymied by the traditional chaining of simulation and data analysis, creating multiple rounds of redundant reads and writes to the storage system, which grows in cost with the ever-increasing gap between compute and storage speeds in HPC clusters. Recent HPC acquisitions have introduced compute node-local flash storage as a means to alleviate this I/O bottleneck. We propose a novel approach, Active Flash, to expedite data analysis pipelines by migrating to the location of the data, the flash device itself. We argue that Active Flash has the potential to enable true out-of-core data analytics by freeing up both the compute core and the associated main memory. By performing analysis locally, dependence on limited bandwidth to a central storage system is reduced, while allowing this analysis to proceed in parallel with the main application. In addition, offloading work from the host to the more power-efficient controller reduces peak system power usage, which is already in the megawatt range and poses a major barrier to HPC system scalability. We propose an architecture for Active Flash, explore energy and performance trade-offs in moving computation from host to storage, demonstrate the ability of appropriate embedded controllers to perform data analysis and reduction tasks at speeds sufficient for this application, and present a simulation study of Active Flash scheduling policies. These results show the viability of the Active Flash model, and its capability to potentially have a transformative impact on scientific data analysis.
99 GENERAL AND MISCELLANEOUS↗
Prefix coding of histograms for minimal storage.
Prefix coding of histograms for minimal storage
HISTOGRAM↗