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FLASH Computer Science
Rusty Lusk Anthony Chan, Ian Foster, Sam Meder, Rick Stevens, Mike Papka, Randy Hudson, Lori Freitag, Ray Loy, Mark Shepard, Joe Flaherty, Jean-Francois Remacle, Ernesto Gomez
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Outline FLASH Computer Science contributes to
The high-performance computing community and environment in general The ASCI community and environment The FLASH community and environment itself FLASH computer science is carried out in several areas: Scalable Performance and I/O Distributed Computing Components, Numerical Algorithms, and Tools Visualization More details are available at the poster session.
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Scalable Performance and I/O Overview
Performance Visualization Scalable logfiles: SLOG Collaboration with IBM, LLNL The Jumpshot tool for exploring performance behavior Parallel I/O with MPI-IO ROMIO HDF-5 Plans SLOG-2 Enhanced Jumpshot
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Logfile-based Performance Analysis
For detailed analysis of parallel program behavior, timestamped events are collected into a log file during the run. A separate display program (Jumpshot) aids the user in conducting a post mortem analysis of program behavior. Log files can become large, making it impossible to inspect the entire program at once. We have developed an indexed file format (SLOG) that uses a preview to select a time of interest and quickly display an interval. We collaborated with IBM and LLNL to collect SLOG files directly from AIX trace records and display traces from multitheaded programs. Processes Logfile Jumpshot Display
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Removing Barriers From Paramesh
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Processes, Threads, and CPU’s on an SMP
SPPM on four eight-CPU nodes of an IBM SP Process view Thread view CPU view
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SLOG/Jumpshot Future Plans
“Bounding-Box” approach to graphical objects more efficient scalable logfile creation simpler, more scalable algorithms Jumpshot improvements interface to logfiles like SLOG-2 scalablility in numbers of processes (y-coordinate objects in general automatically finding the bottleneck
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Scalable IO in ASCI and FLASH
ROMIO: portable implementation of MPI-IO standard Now installed and operational at all three ASCI labs Collaborated with HDF-5 group at NCSA to help define the parallel interface for HDF-5 FLASH code uses parallel HDF-5, thus MPI-IO indirectly
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Distributed Computing Overview
Globus project: relevant recent developments Data Grid Toolkit: secure, high-speed transfer; replica management Grid Security Infrastructure: significant evolution Toolkit development for portals and “virtual organizations” Interactions with DP labs Globus-based DISCOM DRM operational; Kerberos security SNL Globus-based DRM plan wins DOE security approval Visits and DP attendance at Globus project meetings Distributed computing and FLASH Management, movement, analysis of terascale datasets Interactive access to remote resources: FLASH configuration and job-management GUI, using Globus infrastructure Issues Network engineering issues at labs for high-speed transfers Ongoing interest in alternative security models, supporting e.g. third-party file transfers and interactive access to DP computers
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DRM Application Server DRM Application Server
Globus and ASCI Globus-DISCOM collaboration focused on creation of a secure Tri-lab DISCOM production Grid Security & information services Resource management SNL security plan approved User’s Desktop DRM Application Server PSEs, apps, tools DRM Services SNL ASCI White Globus Services Globus Services User’s Desktop DPCS/LL DRM Application Server PSEs, apps, tools LLNL DRM Services SecureNet ASCI Red Globus Services Globus Services NQS LANL SNL Slide courtesy Judy Beiringer, SNL
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Data Grid We are currently engaged in developing the following capabilities that will contribute to building a persistent Data Grid. GridFTP: A high-performance, secure, robust data transfer mechanism—FTP + extensions Automatic negotiation of TCP buffer/window sizes Parallel data transfer Third party control of data transfer Partial file transfer Security Support for reliable data transfer Standardization proposed with Grid Forum (perhaps IETF later) A mechanism for maintaining a catalog of dataset replicas LDAP-based replica catalog server Application Programmer Interface (API) Library Command-line tool A set of tools for creating and managing replicas of large datasets
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The Data Grid and FLASH We are applying Data Grid technologies to the FLASH project as follows. Use GridFTP tools to transfer terascale datasets from ASCI centers (LLNL) to a Data Grid cluster at Argonne National Laboratory. Provide FLASH scientists with GridFTP clients to access the data at Argonne Develop a GridFTP file driver (with partial file transfer) for HDF5. Encourage the use of GridFTP/HDF5 for data visualization applications. Explore the usefulness of replica sites at other locations and build a replica catalog for replicated FLASH datasets. Recent work has included transferring an initial 200 GB dataset from Lawrence Livermore to Argonne, developing GridFTP libraries and tools, and testing the GridFTP protocol’s performance. We will demonstrate the GridFTP protocol, performance data, and replica catalog at the SC2000 conference in early November.
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Potential FLASH Applications
HDF5-based Data Analysis HDF5 GridFTP Parallel Data Transfer Beta Grid Node (ANL) Viewing App GridFTP LAN-based Parallel Data Transfer Data Visualization HDF5 Visualizer Computation Viz Data GridFTP Parallel Data Transfer GridFTP Striped Data Transfer Desktop Users (Everywhere) ASCI Centers (LANL, LLNL, SNL)
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Components, Numerical Algorithms, and Tools Overview
Experiments with componentization in the context of FLASH-like problems Participation in Common Component Architecture Forum Algorithms Discontinuous Galerkin methods Software Support for Unstructured Computation Tools Data-VISE: Adaptive, Multi-resolution Data Reduction AUTOPACK message-aggregation library
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Components Participation in the Common Component Architecture Forum
M x N redistribution component data and field (mesh) component Experiments with components in FLASH-like settings SUMAA3d (unstructured mesh management package) is being given a component interface. This is serving as a prototype implementation for the CCA mesh component We are carrying out Rayleigh-Taylor simulations using this component to implement a discontinuous Galerkin method. This serves as an exploration of possible alternatives ahead for FLASH, enabled by the component interface.
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Discontinuous Galerkin Methods
Discontinuous Galerkin is a discretization method different from PPM (FLASH is currently based on PPM) fewer guard cells, which simplifies algorithms arbitrary element types incrementally higher-order solutions still experimental in FLASH Recent accomplishments at RPI Developed parallel mesh database (AOMD) that can use any mesh representation, e.g. octrees like FLASH. Developed higher order discontinuous Galerkin methods for general conservation laws: formulations, limiters, error indicators, local time stepping, finite element basis Extending Trellis environment at RPI to include HP adaptivity in DG
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Discontinuous Galerkin Experiments for FLASH
Applied Discontinuous Galerkin methods to Rayleigh-Taylor and other test problems from FLASH Evaluating AOMD as an alternate implementation of the FLASH mesh component Plans: PPM on AOMD as a test of how AOMD can fit into FLASH code HP adaptivity in discontinuous Galerkin method
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Architecture of Data-VISE
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Data Reduction with Data-VISE
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AUTOPACK Tool for aggregating small messages to same destination
Minimal departure from MPI semantics Can control tradeoff between aggregation level and latency Tuning is easy Can improve bandwidth while simplifying programming Being incorporated into PARAMESH
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Visualization Overview
Leverage from larger projects Vector visualization Scalable volume rendering Line integral convolution
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Flash Visualization Leverage
Multiresolution Surface Representation Wavelet based, High resolution at area of interest DOE core budget funded Scalable Distributed Volume Renderer Prototype for Corridor One Visualization Framework Advanced Displays High resolution scalable displays Human Factors studies in usability, compared with immersive displays and desktop Advanced Visualization Technology Center (ASCI VIEWS) funded Scalable Distributed Parallel VTK (with Los Alamos National Laboratory and Kitware Inc.) Extensions to Visualization Toolkit (vtk) Vector Visualization LIC based vector visualization Flash funded Flash HDF Visualization Infrastructure Underlying infrastructure used for all visualization tools using Flash datasets
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Vector Visualization Uses the vector data from both Flash HDF 4 and HDF 5 (on native PARAMESH output) Stitches adjacent, like-sized PARAMESH blocks into rectangular regions called metablocks Uses Argonne-implemented FastLIC on each metablock to produce final image Line Integral Convolution for representation of vector fields in 2D datasets Image is created by smearing the pixels of a noise image along the stream lines of the vector field to show the direction of flow everywhere in the field Colors of the output image are done according to color-mapped scalars (either representing magnitude of vector, or other scalar quantity) Resolutions of noise, data and output are independent
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Density and Velocity Density Velocity
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Scalable Volume Rendering
Prototype design for Corridor One visualization framework Allows for use of different rendering algorithms Modular design, strong separation of components Works with a variety of datasets Regular structured grids Flash HDF 4 and HDF 5 datasets (on native PARAMESH output) Software based rendering, MPI-based communication Portable (tested on both Intel and Alpha based cluster) Scalable (tested on 180 processors of Argonne’s Chiba City) Uses a standard ray-casting algorithm Variable sampling used for multi-resolution Flash datasets Tri-linear interpolation in calculation of sample point
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Scalable Volume Rendering
Uses native Paramesh data structures, can use HDF-4, -5
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The computer science component of the FLASH project is contributing to
Summary The computer science component of the FLASH project is contributing to High performance computing in general The ASCI program at the ASCI laboratories The FLASH software evolution and environment in the areas of Scalability and I/O Distributed computing Components, algorithms, and tools Visualization
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