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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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At least 127 records · Page 7
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DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2022

Lossy compression of statistical data using quantum annealer

Abstract We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3–3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

First test results of the HGCAL concentrator ASICs: ECON-T and ECON-D

With over 6 million channels, the High Granularity Calorimeter for the CMS HL-LHC upgrade presents a unique data transmission challenge. The ECON ASICs provide a critical stage of on-detector data compression and selection for the trigger path (ECON-T) and data acquisition path (ECON-D) of the HGCAL. The ASICs, fabricated in 65 nm CMOS, are radiation tolerant up to 200 Mrad and require low power consumption: < 2.5 mW/sensor-channel per chip. Here, we report on the first functionality and radiation tests for the ECON-D-P1 full-functionality prototype. We present a comparison of single event effect (SEE) cross sections measured for different methods of triple modular redundancy using test results from the ECON-T-P1 full-functionality prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The impact of the fiducial cosmology assumption on BAO distance scale measurements

ABSTRACT Standard analysis pipelines for measurements of Baryon Acoustic Oscillations (BAO) in galaxy surveys make use of a fiducial cosmological model to guide the data compression required to transform from observed redshifts and angles to the measured angular and radial BAO peak positions. In order to remove any dependence on the fiducial cosmology from the results, all models compared to the data should mimic the compression and its dependence on the fiducial model. In practice, approximations are made when testing models: (1) There is assumed to be no residual dependence on the fiducial cosmology after reconstruction, (2) differences in the distance–redshift relationship are assumed to match a linear scaling, and (3) differences in clustering between true and fiducial models are assumed to be removed by the free parameters used to null the non-BAO signal. We test these approximations using the current standard measurement procedure with a set of halo catalogues from the aemulus suite of N-body simulations, which span a range of wCDM cosmological models. We focus on reconstruction of the primordial BAO and locating the BAO. For the range of wCDM cosmologies covered by the aemulus suite, we find no evidence for systematic errors in the measured BAO shift parameters α∥ and α⊥ to $\lt 0.1\%$. However, the measured errors $\sigma _{\alpha _{\parallel }}$ and $\sigma _{\alpha _{\bot }}$ show a notable absolute increase by up to +0.001 and +0.002, respectively, in the case that the fiducial cosmology does not match the truth. These effects on the inferred BAO scale will be important, given the precision of measurements expected from future surveys including DESI, Euclid, and WFIRST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

AEflow (Autoencoder fluid flow compression network) [SWR-22-29]

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

King, Ryan↗
DOE OSTI2024

Kernelized approaches to streaming compression of scientific data

In this paper three algorithms are developed for the streaming compression of scientific data. The algorithms presented are reliant on the theory of vector-valued reproducing kernel Hilbert spaces and operator valued kernel. Further, the scientific data is modeled as a snapshot of time dependent vector field F(x, t) over a manifold M and the recovery of the data is framed as a learning problem. These processes are then appropriately modified and ana lyzed for the streaming scenario in which data is generated without the ability to revisit past entries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ti-6Al-4V to over 1.2 TPa: Shock Hugoniot experiments, ab initio calculations, and a broad-range multiphase equation of state

Titanium alloys are used in a large array of applications. In this work we focus our attention on the most used alloy, Ti-6Al-4V (Ti64), which has excellent mechanical and biocompatibility properties with applications in aerospace, defense, biomedical, and other fields. Here we present high-fidelity experimental shock compression data measured on Sandia’s Z machine. We extend the principal shock Hugoniot for Ti64 to more than threefold compression, up to over 1.2 TPa. We use the data to validate our ab initio molecular dynamics simulations and to develop a highly reliable, multiphase equation of state (EOS) for Ti64, spanning a broad range of temperature and pressures. The first-principles simulations show very good agreement with Z data and with previous three-stage gas gun data from Sandia’s STAR facility. The resulting principal Hugoniot and the broad-range EOS and phase diagram up to 10 TPa and 10 5 K are suitable for use in shock experiments and in hydrodynamic simulations. The high-precision experimental results and high-fidelity simulations demonstrate that the Hugoniot of the Ti64 alloy is stiffer than that of pure Ti and reveal that Ti64 melts on the Hugoniot at a significantly lower pressure and temperature than previously modeled.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Cinema:Snap: Real-time tools for analysis of dynamic diamond anvil cell experiment data

We report we developed tools and a workflow for real-time analysis of data from dynamic diamond anvil cell experiments performed at user light sources. These tools allow users to determine the phases of matter observed during the compression of materials in order to make decisions during an experiment to improve the quality of experimental results and maximize the use of scarce experimental facility time. The tools fill a gap in dynamic compression data analysis tools that are real-time, are flexible to the needs of high-pressure scientists, connect to automated processing of results, can be easily incorporated into workflows with existing tools and data formats, and support remote experimental data analysis workflows. Specific analytics developed include novel automated two-peak analysis for overlapping peaks and multiple phases, coordinated views of pressure and temperature values, full-compression contour plots, and configurable views of integrated x-ray diffraction. We present an experimental use case to show how the tools produce real-time analytics that help the scientists revise parameters for the next compression

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗
DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Shock compression of poly(methyl methacrylate) PMMA in the 1000 GPa regime: Z machine experiments

Hydrocarbon polymers are used in a wide variety of practical applications. In the field of dynamic compression at extreme pressures, these polymers are used at several high energy density (HED) experimental facilities. One of the most common polymers is poly(methyl methacrylate) or PMMA, also called Plexiglass® or Lucite®. Here, we present high-fidelity, hundreds of GPa range experimental shock compression data measured on Sandia’s Z machine. We extend the principal shock Hugoniot for PMMA to more than threefold compression up to 650 GPa and re-shock Hugoniot states up to 1020 GPa in an off-Hugoniot regime, where experimental data are even sparser. These data can be used to put additional constraints on tabular equation of state (EOS) models. The present results provide clear evidence for the need to re-examine the existing tabular EOS models for PMMA above ~120 GPa as well as perhaps revisit EOSs of similar hydrocarbon polymers commonly used in HED experiments investigating dynamic compression, hydrodynamics, or inertial confinement fusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Kinetic and temperature sensitivity properties of soil exoenzymes through the soil profile down to one-meter depth at a temperate coniferous forest (Blodgett, CA)

This dataset contains data on kinetic and temperature sensitivity parameters of the exoenzymes β-glucosidase (BG), leucine/leucyl aminopeptidase (LAP) and acid phosphatase (AP) across a soil profile down to 90 cm depth at Blodgett forest, CA, USA. These data were generated to determine if kinetic and thermal properties of microbial exoenzymes involved in organic matter decomposition varied with soil depth, following variation in soil properties and microbial communities, as part of the study: Alves et al. (2021). Kinetic Properties of Microbial Exoenzymes Vary with Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile. Frontiers in Microbiology 12:3618. https://doi.org/10.3389/fmicb.2021.735282. This research was performed within the framework of the TES Belowground Biogeochemistry SFA project, in particular association with a long-term field warming experiment of the whole soil profile at Blodgett forest. Samples for this work were collected from locations representative of the field experimental plots. Potential enzyme activity rates were measured using laboratory fluorometric assays with soils collected at 0-10, 10-20, 30-40, 50-60, 60-70 and 80-90 cm deep in biological triplicates (i.e., three soil cores collected at different representative locations). Assays with each soil were conducted over a gradient of eight substrate concentrations per enzyme, and incubated at 4, 10, 16, 25, 35 or 50°C. Enzyme Michaelis-Menten kinetics were modeled over the eight substrate concentrations at each temperature, and the temperature sensitivity of the kinetic parameters was modeled over the six temperatures using linear Arrhenius/Q10 and non-linear Macromolecular Rate Theory (MMRT) models. The dataset includes the fully processed enzyme activity rate data used to model Michaelis-Menten kinetics, calculated kinetic and temperature sensitivity parameters, and basic soil and microbial biomass chemistry for each sample. All data is provided for each individual biological replicate, and kinetic and temperature sensitivity parameters are also provided as means of the biological replicates. The dataset also includes all raw measurement data and code used to parse, combine and perform the analyses described by Alves et al. (2021). For file descriptions, see the file-level metadata files: “enzymes_dataProcessed_flmd.xlsx” (processed data); “enzymes_dataRaw_flmd.xlsx” (compressed raw data and metadata); and “enzymes_code_flmd.xlsx” (compressed code). The experimental design, list of parameters measured, soil and microbial biomass chemistry data, and means of biological replicates for kinetic and temperature sensitivity parameters are also provided as human-readable tables in file “enzymeTraits_design_results_tables.xlsx”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extended dynamic mode decomposition for model reduction in fluid dynamics simulations

High computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic mode decomposition (xDMD) algorithm in constructing ROMs of three-dimensional cardiovascular computations. Focusing on the ROMs' accuracy in representation and interpolation, we relate these metrics to the truncation rank of singular value decomposition, which underpins xDMD and other approaches to ROM construction. Our key innovation is to relate the truncation rank to the singular values of the original flow problem. This result establishes a priori guidelines for the xDMD deployment and its likely success as a means of data compression and reconstruction of the system's dynamics from dominant spatiotemporal structures present in the data.

Mechanics↗
DOE OSTI2021

All Optical Neural Networks for Low Power Edge Computing

We developed a simplistic physics-based model of an all-optical neural network that mimics the encoder part of an autoencoder neural network for image compression. Our approach relies on the generation of a MATLAB-based model for both data compression and decompression and utilizes MATLAB's built-in autoencoder networks in combination with simple propagation of optical fields between layers constituting phase elements via Fourier transform. We optimize the phase elements using the particle swarm optimization technique and using our model, we demonstrate a compression ratio of 25% for 2828-pixel input images containing numeric digits from 0 to 9.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Global analysis peak fitting for chemical spectroscopy data

The present invention relates to methods for analyzing a chemical sample. For instance, the methods herein allow for global analysis of spectroscopy data in order to extract useful chemical properties from complicated multidimensional data. Such analysis can optionally employ data compression to further expedite computer-implemented computation. In particular, the methods herein provide global analysis of data matrices explained by both linear and non-linear terms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases

ABSTRACT We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in 1D marginal distributions of shear two-point correlation functions in simulated weak lensing data. We examine the implications in the context of future surveys, in particular LSST, with derivations of how the non-Gaussianity scales with survey area. We show that there is no significant bias in 1D posteriors of Ωm and σ8 due to the non-Gaussian likelihood distributions of shear correlations functions using the mock data (100 deg2). We also present a systematic approach to constructing approximate multivariate likelihoods with 1D parametric functions by assuming independence or more flexible non-parametric multivariate methods after decorrelating the data points using principal component analysis (PCA). While the use of PCA does not modify the non-Gaussianity of the multivariate likelihood, we find empirically that the 1D marginal sampling distributions of the PCA components exhibit less skewness and kurtosis than the original shear correlation functions. Modelling the likelihood with marginal parametric functions based on the assumption of independence between PCA components thus gives a lower limit for the biases. We further demonstrate that the difference in cosmological parameter constraints between the multivariate Gaussian likelihood model and more complex non-Gaussian likelihood models would be even smaller for an LSST-like survey. In addition, the PCA approach automatically serves as a data compression method, enabling the retention of the majority of the cosmological information while reducing the dimensionality of the data vector by a factor of ∼5.

79 ASTRONOMY AND ASTROPHYSICS↗