High Level Introduction to HDF5
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Parallel Data Analysis Directly on Scientific File Formats
Parallel Data Analysis Directly on Scientific File Formats Spyros Blanas 1 Kesheng Wu 2 Surendra Byna 2 Bin Dong 2 Arie Shoshani 2 1 The Ohio State University 2 Lawrence Berkeley National Laboratory [email protected] {kwu, sbyna, dbin, ashosani}@lbl.gov ABSTRACT and physics, produce massive amounts of data. The size of Scientific experiments and large-scale simulations produce these datasets typically ranges from hundreds of gigabytes massive amounts of data. Many of these scientific datasets to tens of petabytes. For example, the Intergovernmen- are arrays, and are stored in file formats such as HDF5 tal Panel on Climate Change (IPCC) multi-model CMIP-5 and NetCDF. Although scientific data management systems, archive, which is used for the AR-5 report [22], contains over petabytes such as SciDB, are designed to manipulate arrays, there are 10 of climate model data. Scientific experiments, challenges in integrating these systems into existing analy- such as the LHC experiment routinely store many gigabytes sis workflows. Major barriers include the expensive task of of data per second for future analysis. As the resolution of preparing and loading data before querying, and convert- scientific data is increasing rapidly due to novel measure- ing the final results to a format that is understood by the ment techniques for experimental data and computational existing post-processing and visualization tools. As a con- advances for simulation data, the data volume is expected sequence, integrating a data management system into an to grow even further in the near future. existing scientific data analysis workflow is time-consuming Scientific data are often stored in data formats that sup- and requires extensive user involvement. -
A Metadata Based Approach for Supporting Subsetting Queries Over Parallel HDF5 Datasets
A Metadata Based Approach For Supporting Subsetting Queries Over Parallel HDF5 Datasets Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Vignesh Santhanagopalan, B.S. Graduate Program in Computer Science and Engineering The Ohio State University 2011 Thesis Committee: Dr. Gagan Agrawal, Advisor Dr. Radu Teodorescu ABSTRACT A key challenge in scientific data management is to manage the data as the data sizes are growing at a very rapid speed. Scientific datasets are typically stored using low-level formats which store the data as binary. This makes specification of the data and processing very hard. Also, as the volume of data is huge, parallel configurations must be used to process the data to enable efficient access. We have developed a data virtualization approach for supporting subsetting queries on scientific datasets stored in native format. The data is stored in Hierarchical Data Format (HDF5) which is one of the popular formats for storing scientific data. Our system supports SQL queries using the Select, From and Where clauses. We support queries based on the dimensions of the dataset and also queries which are based on the dimensions and attributes (which provide extra information about the dataset) of the dataset. In order to support the different types of queries, we have the pre-processing and post-processing modules. We also parallelize the selection queries involving the dimensions and the attributes. Our system offers the following advantages. We provide SQL like abstraction for specifying subsets of interest which is a powerful mechanism. -
HUDDL for Description and Archive of Hydrographic Binary Data
University of New Hampshire University of New Hampshire Scholars' Repository Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping 4-2014 HUDDL for description and archive of hydrographic binary data Giuseppe Masetti University of New Hampshire, Durham, [email protected] Brian R. Calder University of New Hampshire, Durham, [email protected] Follow this and additional works at: https://scholars.unh.edu/ccom Recommended Citation G. Masetti and Calder, Brian R., “Huddl for description and archive of hydrographic binary data”, Canadian Hydrographic Conference 2014. St. John's, NL, Canada, 2014. This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. Canadian Hydrographic Conference April 14-17, 2014 St. John's N&L HUDDL for description and archive of hydrographic binary data Giuseppe Masetti and Brian Calder Center for Coastal and Ocean Mapping & Joint Hydrographic Center – University of New Hampshire (USA) Abstract Many of the attempts to introduce a universal hydrographic binary data format have failed or have been only partially successful. In essence, this is because such formats either have to simplify the data to such an extent that they only support the lowest common subset of all the formats covered, or they attempt to be a superset of all formats and quickly become cumbersome. Neither choice works well in practice. -
Towards Interactive, Reproducible Analytics at Scale on HPC Systems
Towards Interactive, Reproducible Analytics at Scale on HPC Systems Shreyas Cholia Lindsey Heagy Matthew Henderson Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Drew Paine Jon Hays Ludovico Bianchi Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Devarshi Ghoshal Fernando Perez´ Lavanya Ramakrishnan Lawrence Berkeley National Laboratory University Of California, Berkeley Lawrence Berkeley National Laboratory Berkeley, USA Berkeley, USA Berkeley, USA [email protected] [email protected] [email protected] Abstract—The growth in scientific data volumes has resulted analytics at scale. Our work addresses these gaps. There are in a need to scale up processing and analysis pipelines using High three key components driving our work: Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform • Reproducible Analytics: Reproducibility is a key com- provides core capabilities for interactivity but was not designed ponent of the scientific workflow. We wish to to capture for HPC systems. In this paper, we outline our efforts that the entire software stack that goes into a scientific analy- bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC sis, along with the code for the analysis itself, so that this systems. Our work is grounded in a real world science use case can then be re-run anywhere. In particular it is important - applying geophysical simulations and inversions for imaging to be able reproduce workflows on HPC systems, against the subsurface. -
Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P
Parsing Hierarchical Data Format (HDF) Files Karl Nyberg Grebyn Corporation P. O. Box 47 Sterling, VA 20167-0047 703-406-4161 [email protected] ABSTRACT This paper presents a description of the creation of a library to parse Hierarchical Data Format (HDF) Files in Ada. It describes a “work in progress” with discussion of current performance, limitations and future plans. Categories and Subject Descriptors D.2.2 [Design Tools and Techniques]: Software Libraries; E.2 [Data Storage Representations]: Object Representation; I.2.10 [Vision and Scene Understanding]: Representations, data structures, and transforms. General Terms Algorithms, Performance, Experimentation, Data Formats. Keywords Ada, HDF. 1. INTRODUCTION Large quantities of scientific data are published each year by NASA. These data are often accompanied by metadata files that describe the contents of individual files of the data. One example of this data is the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) [1]. Each file of data (consisting of satellite imagery) in HDF (Hierarchical Data Format) [2] is accompanied by a metadata file in XML (Extensible Markup Language) [3], encoded according to a published DTD (Document Type Description) that indicates the components and types of data in the metadata file. Each ASTER data file consists of an image of the satellite as it passes over the earth [4]. Information on the location of the data collected as the satellite passes is contained in the metadata file. Over time, multiple images of the same location on earth are obtained. For many purposes of analysis (erosion, building patterns, deforestation, glacier movement, etc.), these images of the same location are compared over time. -
A New Data Model, Programming Interface, and Format Using HDF5
NetCDF-4: A New Data Model, Programming Interface, and Format Using HDF5 Russ Rew, Ed Hartnett, John Caron UCAR Unidata Program Center Mike Folk, Robert McGrath, Quincey Kozial NCSA and The HDF Group, Inc. Final Project Review, August 9, 2005 THG, Inc. 1 Motivation: Why is this area of work important? While the commercial world has standardized on the relational data model and SQL, no single standard or tool has critical mass in the scientific community. There are many parallel and competing efforts to build these tool suites – at least one per discipline. Data interchange outside each group is problematic. In the next decade, as data interchange among scientific disciplines becomes increasingly important, a common HDF-like format and package for all the sciences will likely emerge. Jim Gray, Distinguished “Scientific Data Management in the Coming Decade,” Jim Gray, David Engineer at T. Liu, Maria A. Nieto-Santisteban, Alexander S. Szalay, Gerd Heber, Microsoft, David DeWitt, Cyberinfrastructure Technology Watch Quarterly, 1998 Turing Award Volume 1, Number 2, February 2005 winner 2 Preservation of scientific data … the ephemeral nature of both data formats and storage media threatens our very ability to maintain scientific, legal, and cultural continuity, not on the scale of centuries, but considering the unrelenting pace of technological change, from one decade to the next. … And that's true not just for the obvious items like images, documents, and audio files, but also for scientific images, … and MacKenzie Smith, simulations. In the scientific research community, Associate Director standards are emerging here and there—HDF for Technology at (Hierarchical Data Format), NetCDF (network the MIT Libraries, Common Data Form), FITS (Flexible Image Project director at Transport System)—but much work remains to be MIT for DSpace, a groundbreaking done to define a common cyberinfrastructure. -
C Language Tutorial
C Language Tutorial Version 0.042 March, 1999 Original MS-DOS tutorial by Gordon Dodrill, Coronado Enterprises. Moved to Applix by Tim Ward Typed by Karen Ward C programs converted by Tim Ward and Mark Harvey with assistance from Kathy Morton for Visual Calculator Pretty printed by Eric Lindsay Applix 1616 microcomputer project Applix Pty Ltd Introduction The C programming language was originally developed by Dennis Ritchie of Bell Laboratories, and was designed to run on a PDP-11 with a UNIX operating system. Although it was originally intended to run under UNIX, there was a great interest in running it on the IBM PC and com- patibles, and other systems. C is excellent for actually writing system level programs, and the entire Applix 1616/OS operating system is written in C (except for a few assembler routines). It is an excellent language for this environment because of the simplicity of expression, the compactness of the code, and the wide range of applicability. It is not a good "beginning" language because it is somewhat cryptic in nature. It allows the programmer a wide range of operations from high level down to a very low level approaching the level of assembly language. There seems to be no limit to the flexibility available. One experienced C programmer made the statement, "You can program anything in C", and the statement is well supported by my own experience with the language. Along with the resulting freedom however, you take on a great deal of responsibility. It is very easy to write a program that destroys itself due to the silly little errors that, say, a Pascal compiler will flag and call a fatal error. -
Using Netcdf and HDF in Arcgis
Using netCDF and HDF in ArcGIS Nawajish Noman Dan Zimble Kevin Sigwart Outline • NetCDF and HDF in ArcGIS • Visualization and Analysis • Sharing • Customization using Python • Demo • Future Directions Scientific Data and Esri • Direct support - NetCDF and HDF • OPeNDAP/THREDDS – a framework for scientific data networking, integrated use by our customers • Users of Esri technology • National Climate Data Center • National Weather Service • National Center for Atmospheric Research • U. S. Navy (NAVO) • Air Force Weather • USGS • Australian Navy • Australian Bur.of Met. • UK Met Office NetCDF Support in ArcGIS • ArcGIS reads/writes netCDF since version 9.2 • An array based data structure for storing multidimensional data. T • N-dimensional coordinates systems • X, Y, Z, time, and other dimensions Z Y • Variables – support for multiple variables X • Temperature, humidity, pressure, salinity, etc • Geometry – implicit or explicit • Regular grid (implicit) • Irregular grid • Points Gridded Data Regular Grid Irregular Grid Reading netCDF data in ArcGIS • NetCDF data is accessed as • Raster • Feature • Table • Direct read • Exports GIS data to netCDF CF Convention Climate and Forecast (CF) Convention http://cf-pcmdi.llnl.gov/ Initially developed for • Climate and forecast data • Atmosphere, surface and ocean model-generated data • Also for observational datasets • The CF conventions generalize and extend the COARDS (Cooperative Ocean/Atmosphere Research Data Service) convention. • CF is now the most widely used conventions for geospatial netCDF data. It has the best coordinate system handling. NetCDF and Coordinate Systems • Geographic Coordinate Systems (GCS) • X dimension units: degrees_east • Y dimension units: degrees_north • Projected Coordinate Systems (PCS) • X dimension standard_name: projection_x_coordinate • Y dimension standard_name: projection_y_coordinate • Variable has a grid_mapping attribute. -
Introduction CFD General Notation System (CGNS)
CGNS Tutorial Introduction CFD General Notation System (CGNS) Christopher L. Rumsey NASA Langley Research Center Outline • Introduction • Overview of CGNS – What it is – History – How it works, and how it can help – The future • Basic usage – Getting it and making it work for you – Simple example – Aspects for data longevity 2 Introduction • CGNS provides a general, portable, and extensible standard for the description, storage, and retrieval of CFD analysis data • Principal target is data normally associated with computed solutions of the Navier-Stokes equations & its derivatives • But applicable to computational field physics in general (with augmentation of data definitions and storage conventions) 3 What is CGNS? • Standard for defining & storing CFD data – Self-descriptive – Machine-independent – Very general and extendable – Administered by international steering committee • AIAA recommended practice (AIAA R-101A-2005) • Free and open software • Well-documented • Discussion forum: [email protected] • Website: http://www.cgns.org 4 History • CGNS was started in the mid-1990s as a joint effort between NASA, Boeing, and McDonnell Douglas – Under NASA’s Advanced Subsonic Technology (AST) program • Arose from need for common CFD data format for improved collaborative analyses between multiple organizations – Existing formats, such as PLOT3D, were incomplete, cumbersome to share between different platforms, and not self-descriptive (poor for archival purposes) • Initial development was heavily influenced by McDonnell Douglas’ “Common -
A COMMON DATA MODEL APPROACH to NETCDF and GRIB DATA HARMONISATION Alessandro Amici, B-Open, Rome @Alexamici
A COMMON DATA MODEL APPROACH TO NETCDF AND GRIB DATA HARMONISATION Alessandro Amici, B-Open, Rome @alexamici - http://bopen.eu Workshop on developing Python frameworks for earth system sciences, 2017-11-28, ECMWF, Reading. alexamici / talks MOTIVATION: FORECAST DATA AND TOOLS ECMWF Weather forecasts, re-analyses, satellite and in- situ observations N-dimensional gridded data in GRIB Archive: Meteorological Archival and Retrieval System (MARS) A lot of tools: Metview, Magics, ecCodes... alexamici / talks MOTIVATION: CLIMATE DATA Copernicus Climate Change Service (C3S / ECMWF) Re-analyses, seasonal forecasts, climate projections, satellite and in-situ observations N-dimensional gridded data in many dialects of NetCDF and GRIB Archive: Climate Data Store (CDS) alexamici / talks HARMONISATION STRATEGIC CHOICES Metview Python Framework and CDS Toolbox projects Python 3 programming language scientic ecosystems xarray data structures NetCDF data model: variables and coordinates support for arbitrary metadata CF Conventions support on IO label-matching broadcast rules on coordinates parallelized and out-of-core computations with dask alexamici / talks ECMWF NETCDF DIALECT >>> import xarray as xr >>> ta_era5 = xr.open_dataset('ERA5-t-2016-06.nc', chunks={}).t >>> ta_era5 <xarray.DataArray 't' (time: 60, level: 3, latitude: 241, longitude: 480)> dask.array<open_dataset-..., shape=(60, 3, 241, 480), dtype=float64, chunksize=(60, 3, Coordinates: * longitude (longitude) float32 0.0 0.75 1.5 2.25 3.0 3.75 4.5 5.25 6.0 ... * latitude (latitude) float32 90.0 89.25 88.5 87.75 87.0 86.25 85.5 ... * level (level) int32 250 500 850 * time (time) datetime64[ns] 2017-06-01 2017-06-01T12:00:00 .. -
Development of a Coupling Approach for Multi-Physics Analyses of Fusion Reactors
Development of a coupling approach for multi-physics analyses of fusion reactors Zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) bei der Fakultat¨ fur¨ Maschinenbau des Karlsruher Instituts fur¨ Technologie (KIT) genehmigte DISSERTATION von Yuefeng Qiu Datum der mundlichen¨ Prufung:¨ 12. 05. 2016 Referent: Prof. Dr. Stieglitz Korreferent: Prof. Dr. Moslang¨ This document is licensed under the Creative Commons Attribution – Share Alike 3.0 DE License (CC BY-SA 3.0 DE): http://creativecommons.org/licenses/by-sa/3.0/de/ Abstract Fusion reactors are complex systems which are built of many complex components and sub-systems with irregular geometries. Their design involves many interdependent multi- physics problems which require coupled neutronic, thermal hydraulic (TH) and structural mechanical (SM) analyses. In this work, an integrated system has been developed to achieve coupled multi-physics analyses of complex fusion reactor systems. An advanced Monte Carlo (MC) modeling approach has been first developed for converting complex models to MC models with hybrid constructive solid and unstructured mesh geometries. A Tessellation-Tetrahedralization approach has been proposed for generating accurate and efficient unstructured meshes for describing MC models. For coupled multi-physics analyses, a high-fidelity coupling approach has been developed for the physical conservative data mapping from MC meshes to TH and SM meshes. Interfaces have been implemented for the MC codes MCNP5/6, TRIPOLI-4 and Geant4, the CFD codes CFX and Fluent, and the FE analysis platform ANSYS Workbench. Furthermore, these approaches have been implemented and integrated into the SALOME simulation platform. Therefore, a coupling system has been developed, which covers the entire analysis cycle of CAD design, neutronic, TH and SM analyses. -
MATLAB Tutorial - Input and Output (I/O)
MATLAB Tutorial - Input and Output (I/O) Mathieu Dever NOTE: at any point, typing help functionname in the Command window will give you a description and examples for the specified function 1 Importing data There are many different ways to import data into MATLAB, mostly depending on the format of the datafile. Below are a few tips on how to import the most common data formats into MATLAB. • MATLAB format (*.mat files) This is the easiest to import, as it is already in MATLAB format. The function load(filename) will do the trick. • ASCII format (*.txt, *.csv, etc.) An easy strategy is to use MATLAB's GUI for data import. To do that, right-click on the datafile in the "Current Folder" window in MATLAB and select "Import data ...". The GUI gives you a chance to select the appropriate delimiter (space, tab, comma, etc.), the range of data you want to extract, how to deal with missing values, etc. Once you selected the appropriate options, you can either import the data, or you can generate a script that imports the data. This is very useful in showing the low-level code that takes care of reading the data. You will notice that the textscan is the key function that reads the data into MATLAB. Higher-level functions exist for the different datatype: csvread, xlsread, etc. As a rule of thumb, it is preferable to use lower-level function are they are "simpler" to understand (i.e., no bells and whistles). \Black-box" functions are dangerous! • NetCDF format (*.nc, *.cdf) MATLAB comes with a netcdf library that includes all the functions necessary to read netcdf files.