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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 145 records · Page 8

Charliecloud 101

This workshop will provide participants with background and hands-on experience to use basic containers for HPC applications. We will discuss what containers are, why they matter for HPC, and how they work. We’ll give an overview of Charliecloud, the unprivileged container solution from HPC Division, and walk participants through installing it on their own compute resource. Participants will build toy containers and a real HPC application, and then run them in parallel on an HPC Division cluster. This will be a highly interactive workshop with lots of Q&A.

97 MATHEMATICS AND COMPUTING↗

Decomposition and Algorithmic Approaches for Solving Large-Scale Process Family Design Problems

Our most recent work expands the water desalination case study from 76 variants to 10,897 variants using the equation-oriented model built in Pyomo as part of the PARETO project. Using the discretization formulation presented in Stinchfield (2024a), rather than solving for all 10,897 variants simultaneously, we decompose the formulation into subproblems containing subsets of variants from the process family. We solve the overall problem with Progressive Hedging (PH) deployed in parallel on a distributed HPC cluster using the open-source Python package mpi-sppy (Knueven et al., 2023). This approach allowed us to solve this process family design problem to ~1.5% relative optimality gap in about 5 hours; in comparison, Gurobi reached ~50% relative optimality gap in about 6 hours (Stinchfield et al., 2024b). However, this approach still requires discretization of the common unit module design ranges; additionally, PH acts as a heuristic for MILP’s with gap-closing capabilities. Ideally, we would not have to use ML surrogates or discretization to solve this problem, instead solving the process family design problem with the equation-oriented model directly to achieve the most accurate results. However, recall that we did not consider solving the MINLP directly due to complexity and size. In this work, we aim to decompose and solve this large-scale MINLP using a Structured Nonlinear Global Optimization algorithm presented by Cao and Zavala (2019).

Stinchfield, Georgia↗

The Development of a Finite Volume Method for Modeling Sound in Coastal Ocean Environment

: As the rapid growth of marine renewable energy and off-shore wind energy, there have been concerns that the noises generated from construction and operation of the devices may interfere marine animals’ communication. In this research, a underwater sound model is developed to simulate sound prorogation generated by marine-hydrokinetic energy (MHK) devices or offshore wind (OSW) energy platforms. Finite volume and finite difference methods are developed to solve the 3D Helmholtz equation of sound propagation in the coastal environment. For finite volume method, the grid system consists of triangular grids in horizontal plane and sigma-layers in vertical dimension. A 3D sparse matrix solver with complex coefficients is formed for solving the resulting acoustic pressure field. The Complex Shifted Laplacian Preconditioner (CSLP) method is applied to efficiently solve the matrix system iteratively with MPI parallelization using a high performance cluster. The sound model is then coupled with the Finite Volume Community Ocean Model (FVCOM) for simulating sound propagation generated by human activities in a range-dependent setting, such as offshore wind energy platform constructions and tidal stream turbines. As a proof of concept, initial validation of the finite difference solver is presented for two coastal wedge problems. Validation of finite volume method will be reported separately.

Coastal↗

Coupled Modeling of Hydrodynamics and Sound in Coastal Ocean for Renewable Ocean Energy Development

An underwater sound model was developed to simulate sound propagation from marine and hydrokinetic energy (MHK) devices or offshore wind (OSW) energy platforms. Finite difference methods were developed to solve the 3D Helmholtz equation for sound propagation in the coastal environment. A 3D sparse matrix solver with complex coefficients was formed for solving the resulting acoustic pressure field. The Complex Shifted Laplacian Preconditioner (CSLP) method was applied to solve the matrix system iteratively with MPI parallelization using a high performance cluster. The sound model was then coupled with the Finite Volume Community Ocean Model (FVCOM) for simulating sound propagation generated by human activities, such as construction of OSW turbines or tidal stream turbine operations, in a range-dependent setting. As a proof of concept, initial validation of the solver is presented for two coastal wedge problems. This sound model can be useful for evaluating impacts on marine mammals due to deployment of MHK devices and OSW energy platforms.

Long, Wen↗

The evolution of active galactic nuclei in clusters of galaxies from the Dark Energy Survey

The correlation between active galactic nuclei (AGN) and environment provides important clues to AGN fueling and the relationship of black hole growth to galaxy evolution. Here, we analyze the fraction of galaxies in clusters hosting AGN as a function of redshift and cluster richness for X-ray detected AGN associated with clusters of galaxies in Dark Energy Survey (DES) Science Verification data. The present sample includes 33 AGN with L_X > 10 43 ergs s -1 in non-central, host galaxies with luminosity greater than 0.5 L* from a total sample of 432 clusters in the redshift range of 0.1 0.7. Our result is in good agreement with previous work and parallels the increase in star formation in cluster galaxies over the same redshift range. But, the AGN fraction in clusters is observed to have no significant correlation with cluster mass. Future analyses with DES Year 1 through Year 3 data will be able to clarify whether AGN activity is correlated to cluster mass and will tightly constrain the relationship between cluster AGN populations and redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

Structured building model reduction toward parallel simulation

Building energy model reduction exchanges accuracy for improved simulation speed by reducing the number of dynamical equations. Parallel computing aims to improve simulation times without loss of accuracy but is poorly utilized by contemporary simulators and is inherently limited by inter-processor communication. This paper bridges these disparate techniques to implement efficient parallel building thermal simulation. We begin with a survey of three structured reduction approaches that compares their performance to a leading unstructured method. We then use structured model reduction to find thermal clusters in the building energy model and allocate processing resources. Experimental results demonstrate faster simulation and low error without any interprocessor communication.

97 MATHEMATICS AND COMPUTING↗

Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

We present a new compiler framework for truly heterogeneous 3D stencil computation on GPU clusters. Our framework consists of a simple directive-based programming model and a tightly integrated source-to-source compiler. Annotated with a small number of directives, sequential stencil C codes can be automatically parallelized for large-scale GPU clusters. The most distinctive feature of the compiler is its capability to generate hybrid MPI$+$ CUDA$+$ OpenMP code that uses concurrent CPU$+$ GPU computing to unleash the full potential of powerful GPU clusters. The auto-generated hybrid codes hide the overhead of various data motion by overlapping them with computation. Test results on the Titan supercomputer and the Wilkes cluster show that auto-translated codes can achieve about 90 % of the performance of highly optimized handwritten codes, for both a simple stencil benchmark and a real-world application in cardiac modeling. The user-friendliness and performance of our domain-specific compiler framework allow harnessing the full power of GPU-accelerated supercomputing without painstaking coding effort.

97 MATHEMATICS AND COMPUTING↗

Young Galaxy Candidates in the Hubble Frontier Fields. IV. MACS J1149.5+2223

We search for high-redshift dropout galaxies behind the Hubble Frontier Fields (HFF) galaxy cluster MACS J1149.5+2223, a powerful cosmic lens that has revealed a number of unique objects in its field. Using the deep images from the Hubble and Spitzer space telescopes, we find 11 galaxies at z > 7 in the MACS J1149.5+2223 cluster field, and 11 in its parallel field. The high-redshift nature of the bright z ≃ 9.6 galaxy MACS1149-JD, previously reported by Zheng et al., is further supported by non-detection in the extremely deep optical images from the HFF campaign. With the new photometry, the best photometric redshift solution for MACS1149-JD reduces slightly to z = 9.44 ± 0.12. The young galaxy has an estimated stellar mass of (7±2)×10{sup 8} M{sub ⊙}, and was formed at z=13.2{sub −1.6}{sup +1.9} when the universe was ≈300 Myr old. Data available for the first four HFF clusters have already enabled us to find faint galaxies to an intrinsic magnitude of M{sub UV}≃−15.5, approximately a factor of 10 deeper than the parallel fields.

79 ASTRONOMY AND ASTROPHYSICS↗

Improving Block-level Efficiency with scsi-mq

Current generation solid-state storage devices are exposing a new bottlenecks in the SCSI and block layers of the Linux kernel, where IO throughput is limited by lock contention, inefficient interrupt handling, and poor memory locality. To address these limitations, the Linux kernel block layer underwent a major rewrite with the blk-mq project to move from a single request queue to a multi-queue model. The Linux SCSI subsystem rework to make use of this new model, known as scsi-mq, has been merged into the Linux kernel and work is underway for dm-multipath support in the upcoming Linux 4.0 kernel. These pieces were necessary to make use of the multi-queue block layer in a Lustre parallel filesystem with high availability requirements. We undertook adding support of the 3.18 kernel to Lustre with scsi-mq and dm-multipath patches to evaluate the potential of these efficiency improvements. In this paper we evaluate the block-level performance of scsi-mq with backing storage hardware representative of a HPC-targerted Lustre filesystem. Our findings show that SCSI write request latency is reduced by as much as 13.6%. Additionally, when profiling the CPU usage of our prototype Lustre filesystem, we found that CPU idle time increased by a factor of 7 with Linux 3.18 and blk-mq as compared to a standard 2.6.32 Linux kernel. Our findings demonstrate increased efficiency of the multi-queue block layer even with disk-based caching storage arrays used in existing parallel filesystems.

Cluster Computing↗

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decomposition corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re-balancing scheme based on probabilistic mass transport methods.

97 MATHEMATICS AND COMPUTING↗

A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark

We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a Cray XC40 system, and an experimental Cray cluster. We implemented this factorization both as a parallelized C implementation with hand-tuned optimizations and in Scala using the Apache Spark high-level cluster computing framework. We obtained consistent performance across the three platforms: using Spark we were able to process the 1TB size dataset in under 30 minutes with 960 cores on all systems, with the fastest times obtained on the experimental Cray cluster. In comparison, the C implementation was 21X faster on the Amazon EC2 system, due to careful cache optimizations, bandwidth-friendly access of matrices and vector computation using SIMD units. We report these results and their implications on the hardware and software issues arising in supporting data-centric workloads in parallel and distributed environments.

Gittens, Alex↗

miniVite: A Graph Analytics Benchmarking Tool for Massively Parallel Systems

Benchmarking of high performance computing sys-tems can help provide critical insights for ef?cient design of computing systems and software applications. Although a large number of tools for benchmarking exist, there is a lack of rep-resentative benchmarks for the class of irregular computations as exempli?ed by graph analytics. In this paper, we propose miniVite as a representative graph analytics benchmark tool to test a variety of distributed-memory systems. Graph clustering, popularly known as community detection, is a prototypical graph operation used in numerous scienti?c computing and analytics applications. The goal of clustering is to partition a graph into clusters (or communities) such that each cluster consists of vertices that are densely connected within the cluster and sparsely connected to the rest of the graph. Modu-larity optimization is a popular technique for identifying clusters in a graph. Ef?cient parallelization of modularity optimization-based algorithms is challenging. One successful approach was conceived in Vite, a distributed-memory implementation of the Louvain algorithm that incorporates several heuristics. We introduce miniVite as a representative but simpli?ed variant of Vite, to serve as a prototypical graph analytics benchmarking tool. Unlike other graph-based methods such as breadth-?rst search and betweenness centrality, miniVite represents highly complex computational patterns stressing a variety of system features, which can provide crucial insight for co-design of future computing systems.

community detection, parallel, MPI↗

Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)

The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.

97 MATHEMATICS AND COMPUTING↗

Parallel k-means++

A parallelization of the k-means++ seed selection algorithm on three distinct hardware platforms: GPU, multicore CPU, and multithreaded architecture. K-means++ was developed by David Arthur and Sergei Vassilvitskii in 2007 as an extension of the k-means data clustering technique. These algorithms allow people to cluster multidimensional data, by attempting to minimize the mean distance of data points within a cluster. K-means++ improved upon traditional k-means by using a more intelligent approach to selecting the initial seeds for the clustering process. While k-means++ has become a popular alternative to traditional k-means clustering, little work has been done to parallelize this technique. We have developed original C++ code for parallelizing the algorithm on three unique hardware architectures: GPU using NVidia's CUDA/Thrust framework, multicore CPU using OpenMP, and the Cray XMT multithreaded architecture. By parallelizing the process for these platforms, we are able to perform k-means++ clustering much more quickly than it could be done before.

Source record↗

rustpix

rustpix is a high-performance, open-source Rust library with first-class Python bindings (via PyO3) for processing pixel-detector data in neutron imaging. It targets time-stamping detectors such as Timepix3 (TPX3) at ORNL's Spallation Neutron Source (VENUS beamline), where each detected neutron deposits charge across a cluster of pixels within a very high-rate event stream (96M+ hits/sec). rustpix parses TPX3 event data in parallel using memory-mapped I/O, offers four interchangeable clustering algorithms (ABS adjacency-based search, DBSCAN, graph/union-find connected components, and a parallel grid method), and extracts weighted, super-resolved centroids to produce neutron-event lists. A streaming architecture lets it process files larger than available memory. rustpix is distributed as a pip-installable Python package (with NumPy integration), Rust crates, a command-line tool, and an interactive GUI; it writes HDF5, Apache Arrow, and CSV; and it is designed to extend to TPX4 and other detector types. Released as open-source under the MIT License.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗

Simulation of Coupled Processes of Flow, Transport, and Storage of CO 2 in Saline Aquifers

This report is the final scientific one for the award DE- FE0000988 entitled “Simulation of Coupled Processes of Flow, Transport, and Storage of CO 2 in Saline Aquifers.” The work has been divided into six tasks. In task, “Development of a Three-Phase Non-Isothermal CO 2 Flow Module,” we developed a fluid property module for brine-CO 2 mixtures designed to handle all possible phase combinations of aqueous phase, sub-critical liquid and gaseous CO 2 , supercritical CO 2 , and solid salt. The thermodynamic and thermophysical properties of brine-CO 2 mixtures (density, viscosity, and specific enthalpy of fluid phases; partitioning of mass components among the different phases) use the same correlations as an earlier fluid property module that does not distinguish between gaseous and liquid CO 2 -rich phases. We verified the fluid property module using two leakage scenarios, one that involves CO 2 migration up a blind fault and subsequent accumulation in a secondary “parasitic” reservoir at shallower depth, and another investigating leakage of CO 2 from a deep storage reservoir along a vertical fault zone. In task, “Development of a Rock Mechanical Module,” we developed a massively parallel reservoir simulator for modeling THM processes in porous media brine aquifers. We derived, from the fundamental equations describing deformation of porous elastic media, a momentum conservation equation relating mean stress, pressure, and temperature, and incorporated it alongside the mass and energy conservation equations from the TOUGH2 formulation, the starting point for the simulator. In addition, rock properties, namely permeability and porosity, are functions of effective stress and other variables that are obtained from the literature. We verified the simulator formulation and numerical implementation using analytical solutions and example problems from the literature. For the former, we matched a one-dimensional consolidation problem and a two-dimensional simulation of the Mandel-Cryer effect. For the latter, we obtained a good match of temperature and gas saturation profiles, and surface uplift, after injection of hot fluid into a model of a caldera structure. In task, “Incorporation of Geochemical Reactions of Selected Important Species,” we developed a novel mathematical model of THMC processes in porous and fractured saline aquifers, simulating geo-chemical reactions associated with CO 2 sequestration in saline aquifers. Two computational frameworks, sequentially coupled and fully coupled, were used to simulate the reactions and transport. We verified capabilities of the THMC model to treat complex THMC processes during CO 2 sequestration by analytical solutions and we constructed reactive transport models to analyze the THMC process quantitatively. Three of these are 1D reactive transport under chemical equilibrium, a batch reaction model with equilibrium chemical reactions, and a THMC model with CO 2 dissolution. In task “Study of Instability in CO 2 Dissolution-Diffusion-Convection Processes,” We reviewed literature related to the study of density driven convective flows and on the instability of CO 2 dissolution-diffusion-convection processes. We ran simulations that model the density-driven flow instability that would occur during CO 2 sequestration. CO 2 diffused through the top of the system and dissolved in the aqueous phase there, increasing its density. Density fingers formed along the top boundary, and coalesced into a few prominent ones, causing convective flow that forced the fluid to the system bottom. These simulations were in two and three dimensions. We ran additional simulations of convective mixing with density contrast caused by variable dissolved CO 2 concentration in saline water, modeled after laboratory experiments in which supercritical CO2 was circulated in the headspace above a brine saturated packed sand in a pressure vessel. As CO 2 dissolved into the upper part of the saturated sand, liquid phase density increases causing instability and setting off convective mixing. We obtained good agreement with the laboratory experiments, which were characterized by finger development and associated mixing of dissolved CO 2 into the system. We then varied a wide range of parameters and conceptual models in order to analyze the possibility of convective mixing under different conditions, such as various boundary conditions, and chemical reaction conditions. The CO 2 fingers from different simulations showed great differences as time progressed, caused by permeability heterogeneity. The early time diffusive phenomenon was captured by fine grid resolution, and the permeability heterogeneity affected the pattern of the CO 2 fingers. In addition, the fingers from three-dimensional simulations tended to be larger and flatter than the two-dimensional ones. In task “Implementation of Efficient Parallel Computing Technologies,” we made enhancements and modifications to our code in order to substantially increase the grid size that could be run. We installed and ran it on various platforms, including a multi-core PC and a cluster, and verified the numerical implementation and parallel code using an example problem from the literature. This problem, with a grid size of sixty million, utilized the cluster’s entire memory and all of its processors. In task “Implementation of General Fracture Conceptual Models,” we used the MINC approach, a generalization of the double-porosity concept, to model flow through porous and fractured media. In this approach, flow within the matrix is described by subdividing the matrix into nested volumes, with flow occurring between adjacent nested matrix volumes as well as between the fractures and the outer matrix volume. We generalized Hooke’s law to a thermo-multi- poroelastic medium, and derived from the fundamental equations describing deformation of porous and fractured elastic media a momentum conservation equation for thermo-multi- poroelastic media. This equation is a generalization to multi-poroelastic media of the one derived in Task 3.0 for single porosity media. We describe two simulations to provide model verification and application examples. The first, one-dimensional consolidation of a double-porosity medium, is compared to an analytical solution. The second is a match of published results from the literature, a simulation of CO 2 injection into hypothetical aquifer-caprock systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Parallel multiple-chain DRAM MCMC for large-scale geosteering inversion and uncertainty quantification

Geosteering is the proactive control of a wellbore placement based on the downhole measurements, aiming at maximizing economic production from the well. We report the recent development of azimuthal resistivity logging-whiledrilling (LWD) tools have a much larger depth of detection, thus sets higher demands for geosteering inversion as more unknown parameters need to be taken into account in the earth model to be inverted. For complicated nonlinear problems, traditional deterministic inversion methods are more likely to be trapped near local optima. In general, Markov chain Monte Carlo (MCMC) inversion methods are more capable of finding the global optimal solution and providing additional uncertainty analyses by sampling from the target distribution. However, MCMC methods usually have the problem of slow convergence. Even though optimization methods like delayed rejection (DR) and adaptive Metropolis (AM) were proposed to speed up the convergence, using MCMC methods to solve geosteering inversion problems may still incur unacceptable time cost. To reduce the sampling cost of MCMC methods, we use parallel multiple-chain DRAM MCMC methods to solve geosteering inverse problems and estimate the corresponding uncertainty. A clustering method, density-based spatial clustering of applications with noise (DBSCAN), is applied to select the best solution among many results. Lastly, the simulation results show that running many relatively short MCMC chains for one problem can obtain a similar result as running a long single chain. Besides, avoiding communications between multiple Markov chains during the sampling process yields almost linear scalability.

97 MATHEMATICS AND COMPUTING↗

Expressing Parallelism with ROOT

The need for processing the ever-increasing amount of data generated by the LHC experiments in a more efficient way has motivated ROOT to further develop its support for parallelism. Such support is being tackled both for shared-memory and distributed-memory environments. The incarnations of the aforementioned parallelism are multi-threading, multi-processing and cluster-wide executions. In the area of multi-threading, we discuss the new implicit parallelism and related interfaces, as well as the new building blocks to safely operate with ROOT objects in a multi-threaded environment. Regarding multi-processing, we review the new MultiProc framework, comparing it with similar tools (e.g. multiprocessing module in Python). Finally, as an alternative to PROOF for cluster-wide executions, we introduce the efforts on integrating ROOT with state-of-the-art distributed data processing technologies like Spark, both in terms of programming model and runtime design (with EOS as one of the main components). For all the levels of parallelism, we discuss, based on real-life examples and measurements, how our proposals can increase the productivity of scientists.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗