Results for “Data parallelism” — Page 39

DOE OSTI2023

Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing (RS) imagery has been one of the crucial components in GeoAI workflows for a wide spectrum of humanitarian applications. Derived vector data of built environment is often leveraged for disaster preparedness and response activities. However, factors such as differences in ortho-rectification, atmospheric conditions and human error, results in spatial misalignment between vector data and the timely available RS imagery. Model training for downstream tasks such as object detection, change analysis, etc., is negatively impacted due to such spatial misalignment. Although there has been progress towards automatic alignment of vector data, the lack of scalability remains an open research challenge. This paper proposes to leverage parallel computing to optimize an automatic vector data alignment workflow. It further employs CPU-level multi-core parallelism for improving the performance of the workflow for scalable built environment mapping. We report observations and discuss findings from the preliminary experiments performed on the Summit Supercomputer.

Potnis, Abhishek↗

DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗

DOE OSTI2025

Validation of Modern Nuclear Data Processing in SCALE

The nuclear data (ND) community is continuously developing more accurate, diversified, and comprehensive data for radiation transport modeling to support the nuclear science community. As these community efforts progress, it is crucial that ND processing tools like AMPX (used for SCALE [1] ND) also be developed in parallel to incorporate these new data into transport codes and actually deliver those data to end users. AMPX is a mature, well-tested code that was developed by many people at Oak Ridge National Laboratory (ORNL) over the course of the past few decades. A large portion of the AMPX codebase, however, was outdated, difficult to maintain, and incompatible with modern code development tools. Some of the most important parts of the AMPX code have now been replaced with modern C++ code that can be maintained more cost-effectively and can be tested more rigorously.

AMPX↗

DOE OSTI2022

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Report for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

FFT Computation with Systolic Arrays, A New Architecture

The use of the Cooley-Tukey algorithm for computing the l-d FFT lends itself to a particular matrix factorization which suggests direct implementation by linearly-connected systolic arrays. Here we present a new systolic architecture that embodies this algorithm. This implementation requires a smaller number of processors and a smaller number of memory cells than other recent implementations, as well as having all the advantages of systolic arrays. For the implementation of the decimation-in-frequency case, word-serial data input allows continuous real-time operation without the need of a serial-to-parallel conversion device. No control or data stream switching is necessary. Computer simulation of this architecture was done in the context of a 1024 point DFT with a fixed point processor, and CMOS processor implementation has started.

Boriakoff, Valentin↗

NASA NTRS1994

Hot proton anisotropies and cool proton temperatures in the outer magnetosphere

The hot protons of the outer magnetosphere typically exhibit a temperature anistropy such that T(sub perp)/T(sub parallel) greater than 1, where perpendicular and parallel symbols denote directions relative to the background magnetic field. If this anisotrpy is sufficiently large, the electomagneitc proton cyclotron anistropy instability may be excited. This instability is studied using linear Vlasov theory and one-dimensional hybrid simulations for a homogeneous plasma model representative of conditions in the outer magnetosphere with a hot anisotropic proton component (denoted by subscript h) and a cool, initially isotropic proton component (subscript c). Linear theory yields an instability threshold condition on the hot proton temperature anistropy where as the simulations imply an upper bound on T(sub perp h)/T(sub parallel h); both the threshold and the upper bound have similar scaling with the maximum growth rate gamma (sub m), the parallel beta of the hot component, beta(sub parallel h), and the relative density of the hot component n(sub h)/n(sub e). An anlysis of plasma observations from the Los Alamos magnetospheric plasma analyzer (MPA) in geosynchronous orbits finds that the maximum value of the hot proton temperature anisotropy approximately satisfies the predicted scaling with beta(sub parallel h) and nu(sub h)/n(sub e) and yields the proportionality factor that quantifies this upper bound. The simulations are also used to examine the heating of the cool proton cyclotron instability. The simulations yield a scaling for the dimensionless late-time cool proton average temperature T(sub c)/T(sub parallel h) as (n(sub h)/n(sub e))/beta(sub parallel h exp 0.5). Analysis of MPA data shows that the observed values of T(sub c)/T(sub parallel h) have similar scaling and again yield the proportionality factor which quantifies this relationship.

Gary, S. Peter↗

DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗

NASA NTRS2008

Operational Use of GPS Navigation for Space Shuttle Entry

The STS-118 flight of the Space Shuttle Endeavour was the first shuttle mission flown with three Global Positioning System (GPS) receivers in place of the three legacy Tactical Air Navigation (TACAN) units. This marked the conclusion of a 15 year effort involving procurement, missionization, integration, and flight testing of a GPS receiver and a parallel effort to formulate and implement shuttle computer software changes to support GPS. The use of GPS data from a single receiver in parallel with TACAN during entry was successfully demonstrated by the orbiters Discovery and Atlantis during four shuttle missions in 2006 and 2007. This provided the confidence needed before flying the first all GPS, no TACAN flight with Endeavour. A significant number of lessons were learned concerning the integration of a software intensive navigation unit into a legacy avionics system. These lessons have been taken into consideration during vehicle design by other flight programs, including the vehicle that will replace the Space Shuttle, Orion.

Goodman, John L.↗

DOE OSTI2020

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

NASA NTRS1994

High Performance Programming Using Explicit Shared Memory Model on the Cray T3D

The Cray T3D is the first-phase system in Cray Research Inc.'s (CRI) three-phase massively parallel processing program. In this report we describe the architecture of the T3D, as well as the CRAFT (Cray Research Adaptive Fortran) programming model, and contrast it with PVM, which is also supported on the T3D We present some performance data based on the NAS Parallel Benchmarks to illustrate both architectural and software features of the T3D.

Saini, Subhash↗

NASA NTRS2020

Temperature and Flow Measurements in Incompressible Heated Jets

An experimental study is conducted to perform time-resolved temperature and velocity measurements on a Mach 0.08 jet at total temperatures of 295 K and 353 K. Mean and rms temperature data acquired using two fine wire sensors with diameters 1.3 and 3.8 μm are compared. In order to extend the limited frequency response of the wires, the temperature data are post-processed using a frequency compensation technique available in the literature. Corresponding velocity measurements are performed at cold and heated conditions using single and parallel wire probes. Simultaneously measured temperature and velocity data obtained using the parallel wire probe are used to calculate correlations pertinent to axial turbulent heat flux. In addition to shedding some light on the aerothermal properties of heated turbulent jets, vis-`a-vis their cold counterparts, this study also provides a database for numerical prediction of these flows.

Temperature measurement, turbulence, Jets, Turbule↗

NASA NTRS2019

Real Time Photon-Counting Receiver for High Photon Efficiency Optical Communications

We present a scalable design for a photon-counting ground receiver based on superconducting nanowire single photon detectors (SNSPDs) and field programmable gate array (FPGA) real-time processing for applications to space-to-ground photon starved links, such as the Orion EM-2 Optical Communication Demonstration (O2O), and future deep space or low transmitter power missions. The receiver is designed to receive a serially concatenated pulse position modulation (SCPPM) waveform, which follows the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Red Book standard. The receiver design uses multiple individually fiber coupled, 80% detection efficiency commercial SNSPDs in parallel to scale to a required data rate, and is capable of achieving data rates up to 528 Mbps. For efficient fiber coupling from the telescope to the array of parallel detectors that can be scaled both to telescope aperture size and the number of detectors, we use either a single mode fiber (SMF) photonic lantern or a few-mode fiber (FMF) photonic lantern. In this paper we give an overview of the receiver system design, the characteristics of the photonic lanterns, the performance of the SNSPDs, and system level tests. We show that 40 Mbps can be received using a single SNSPD, and discuss aspects for scaling to higher data rates.

Vyhnalek, Brian E.↗

NASA NTRS2019

Real Time Photon-Counting Receiver for High Photon Efficiency Optical Communications

We present a scalable design for a photon-counting ground receiver based on superconducting nanowire single photon detectors (SNSPDs) and field programmable gate array (FPGA) real-time processing for applications to space-to-ground photon starved links, such as the Orion EM-2 Optical Communication Demonstration (O2O), and future deep space or low transmitter power missions. The receiver is designed to receive a serially concatenated pulse position modulation (SCPPM) waveform, which follows the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Red Book standard. The receiver design uses multiple individually fiber coupled, 80% detection efficiency commercial SNSPDs in parallel to scale to a required data rate, and is capable of achieving data rates up to 528 Mbps. For efficient fiber coupling from the telescope to the array of parallel detectors that can be scaled both to telescope aperture size and the number of detectors, we use either a single mode fiber (SMF) photonic lantern or a few-mode fiber (FMF) photonic lantern. In this paper we give an overview of the receiver system design, the characteristics of the photonic lanterns, the performance of the SNSPDs, and system level tests. We show that 40 Mbps can be received using a single SNSPD, and discuss aspects for scaling to higher data rates.

Vyhnalek, Brian E.↗

NASA NTRS1993

Investigation of wheat coleoptile response to phototropic stimulations

This report provides a summary of the preparations for, and the conduct and post-flight data analysis of, the Spacelab flight investigation FOTRAN, which flew on the IML-1 mission (STS-42) in January, 1992. The investigation was designed to provide data on the responses of wheat seedlings to various blue-light stimuli given while the plants were exposed to orbital microgravity conditions. Before the flight, a number of hypotheses were established which were to be tested by the data from the flight and parallel ground studies. A description of the experiment protocol developed for the mission is provided, and an account of the activities supported during preparations for and support of the flight experiment is given. Details of the methods used to reduce and analyze the data from the flight are outlined.

Heathcote, David G.↗

NASA NTRS1979

The Massively Parallel Processor and its applications

A long-term experimental development program conducted at Goddard Space Flight Center to implement an ultrahigh-speed data processing system known as the Massively Parallel Processor (MPP) is described. The MPP is a single instruction multiple data stream computer designed to perform logical, integer, and floating point arithmetic operations on variable word length data. Information is presented on system architecture, the system configuration, the array unit architecture, individual processing units, and expected operating rates for several image processing applications (including the processing of Landsat data).

Strong, J. P.↗

NASA NTRS2001

Efficiency of parallel direct optimization

Tremendous progress has been made at the level of sequential computation in phylogenetics. However, little attention has been paid to parallel computation. Parallel computing is particularly suited to phylogenetics because of the many ways large computational problems can be broken into parts that can be analyzed concurrently. In this paper, we investigate the scaling factors and efficiency of random addition and tree refinement strategies using the direct optimization software, POY, on a small (10 slave processors) and a large (256 slave processors) cluster of networked PCs running LINUX. These algorithms were tested on several data sets composed of DNA and morphology ranging from 40 to 500 taxa. Various algorithms in POY show fundamentally different properties within and between clusters. All algorithms are efficient on the small cluster for the 40-taxon data set. On the large cluster, multibuilding exhibits excellent parallel efficiency, whereas parallel building is inefficient. These results are independent of data set size. Branch swapping in parallel shows excellent speed-up for 16 slave processors on the large cluster. However, there is no appreciable speed-up for branch swapping with the further addition of slave processors (>16). This result is independent of data set size. Ratcheting in parallel is efficient with the addition of up to 32 processors in the large cluster. This result is independent of data set size. c2001 The Willi Hennig Society.

NASA Discipline Evolutionary Biology↗

NASA NTRS2013

Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation.

PEEMD↗