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Results for “heterogeneous computing”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-T-587: Monte Carlo Versus Ray-Tracing for Treatment Planning Involving CNS Tumors On the MultiPlan System for CyberKnife Radiosurgery

Purpose: MultiPlan, the treatment planning system for the CyberKnife Robotic Radiosurgery system offers two approaches to dose computation, namely Ray-Tracing (RT), the default technique and Monte Carlo (MC), an option. RT is deterministic, however it accounts for primary heterogeneity only. MC on the other hand has an uncertainty associated with the calculation results. The advantage is that in addition, it accounts for heterogeneity effects on the scattered dose. Not all sites will benefit from MC. The goal of this work was to focus on central nervous system (CNS) tumors and compare dosimetrically, treatment plans computed with RT versus MC. Methods: Treatment plans were computed using both RT and MC for sites covering (a) the brain (b) C-spine (c) upper T-spine (d) lower T-spine (e) L-spine and (f) sacrum. RT was first used to compute clinically valid treatment plans. Then the same treatment parameters, monitor units, beam weights, etc., were used in the MC algorithm to compute the dose distribution. The plans were then compared for tumor coverage to illustrate the difference if any. All MC calculations were performed at a 1% uncertainty. Results: Using the RT technique, the tumor coverage for the brain, C-spine (C3–C7), upper T-spine (T4–T6), lower T-spine (T10), Lspine (L2) and sacrum were 96.8%, 93.1%, 97.2%, 87.3%, 91.1%, and 95.3%. The corresponding tumor coverage based on the MC approach was 98.2%, 95.3%, 87.55%, 88.2%, 92.5%, and 95.3%. It should be noted that the acceptable planning target coverage for our clinical practice is >95%. The coverage can be compromised for spine tumors to spare normal tissues such as the spinal cord. Conclusion: For treatment planning involving the CNS, RT and MC appear to be similar for most sites but for the T-spine area where most of the beams traverse lung tissue. In this case, MC is highly recommended.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Towards Use of Mixed Precision in ECP Math Libraries [Exascale Computing Project]

The use of multiple types of precision in mathematical software has the potential to increase its performance on new heterogeneous architectures. The xSDK project focuses both on the investigation and development of multiprecision algorithms as well as their inclusion into xSDK member libraries. This report summarizes current efforts on including and/or using mixed precision capabilities in the math libraries Ginkgo, heFFTe, hypre, MAGMA, PETSc/TAO, SLATE, SuperLU, and Trilinos, including KokkosKernels. It contains both numerical results from libraries that already provide mixed precision capabilities, as well as descriptions of the strategies to incorporate multiprecision into established libraries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Multiscale Investigation on Biofilm Distribution and Its Impact on Macroscopic Biogeochemical Reaction Rates

Biofilms are critical locations for biogeochemical reactions in the subsurface environment. The occurrence and distribution of biofilms at microscale as well as their impacts on macroscopic biogeochemical reaction rates are still poorly understood. This paper investigated the formation and distributions of biofilms in heterogeneous sediments using multiscale models, and evaluated the effects of biofilm heterogeneity on local and macroscopic biogeochemical reaction rates. Sediment pore structures derived from X-ray computed tomography were used to simulate the microscale flow dynamics and biofilm distribution in the sediment column. The response of biofilm formation and distribution to the variations in hydraulic and chemical properties was first examined. One representative biofilm distribution was then utilized to evaluate its effects on macroscopic reaction rates using nitrate reduction as an example. The results revealed that microorganisms primarily grew on the surfaces of grains and aggregates near preferential flow paths where both electron donor and acceptor were readily accessible, leading to the heterogeneous distribution of biofilms in the sediments. Furthermore, the heterogeneous biofilm distribution decreased the macroscopic rate of biogeochemical reactions as compared with those in homogeneous cases. Operationally considering the heterogeneous biofilm distribution in macroscopic reactive transport models such as using dual porosity domain concept can significantly improve the prediction of biogeochemical reaction rates. Overall, this study provided important insights into the biofilm formation and distribution in soils and sediments as well as their impacts on the macroscopic manifestation of reaction rates.

42 ENGINEERING↗
DOE OSTI2021

Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins

Catalytic alkane dehydrogenation is of considerable importance in the synthesis of olefins industrially. However, discovery of highly active, selective, and stable heterogeneous catalysts to replace noble metal Pt remains challenging. By combining descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments, we efficiently evaluated 1,998 bimetallic alloys and successfully identified Ni 3 Mo as one of the most promising catalysts in selective ethane dehydrogenation. This work will open new possibilities of using earth-abundant materials as catalysts for essential heterogeneous catalytic reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Nonlinear structural analysis for fiber-reinforced superalloy turbine blades

A computational capability for predicting the nonlinear thermomechanical structural response of fiber-reinforced superalloy (FRS) turbine blades is described. This capability is embedded in a special purpose computer code (COBSTRAN) developed at the NASA Lewis Research Center. Special features of this computational capability include accounting for: fiber/matrix reaction, nonlinear and anisotropic material behavior, complex stress distribution due to local and global heterogeneity, and residual stresses due to initial fabrication and/or inelastic behavior during subsequent missions. Numerical results are presented from analyses of a hypothetical FRS turbine blade subjected to a fabrication process and subsequent mission cycle. The results demonstrate the capabilities of this computational tool to; predict local stress/strain response and capture trends of local nonlinear and anisotropic material behavior, relate the effects of this local behavior to the global response of a multilayered fiber-composite turbine blade, and trace material history from fabrication through successive missions.

Hopkins, D. A.↗
NASA NTRS1992

Design for interaction between humans and intelligent systems during real-time fault management

Initial results are reported to provide guidance and assistance for designers of intelligent systems and their human interfaces. The objective is to achieve more effective human-computer interaction (HCI) for real time fault management support systems. Studies of the development of intelligent fault management systems within NASA have resulted in a new perspective of the user. If the user is viewed as one of the subsystems in a heterogeneous, distributed system, system design becomes the design of a flexible architecture for accomplishing system tasks with both human and computer agents. HCI requirements and design should be distinguished from user interface (displays and controls) requirements and design. Effective HCI design for multi-agent systems requires explicit identification of activities and information that support coordination and communication between agents. The effects are characterized of HCI design on overall system design and approaches are identified to addressing HCI requirements in system design. The results include definition of (1) guidance based on information level requirements analysis of HCI, (2) high level requirements for a design methodology that integrates the HCI perspective into system design, and (3) requirements for embedding HCI design tools into intelligent system development environments.

Malin, Jane T.↗
DOE OSTI2017

Combining Hadoop with MPI to Solve Metagenomics Problems that are both Data- and Compute-intensive

Metagenomics, the study of all microbial species cohabitants in an environment, usually produces large amount of sequence data varying from several GBs to a few TBs. Analyzing metagenomics data includes both data-intensive and compute-intensive steps, making the entire process hard to scale. Here we aim to optimize a metagenomics application that partitions the shortgun metagenomics sequences based on their species of origin. Our solution combines MapReduce-based BioPig analytic toolkit with MPI to provide scalability in respective to both data and compute. We also made some improvements to the existing BioPig toolkit by using simplified data types and compressed k-mer storage. These optimizations leads up to 193× speedup for the computing-intensive step and 9.6× speedup over the entire pipeline. Our optimized application is also capable of processing datasets that are 16 times larger on the same hardware platform. These conclusions indicate integrating heterogeneous technologies such as Hadoop and MPI is quite efficient to solve large genomics problems that are both data-intensive and compute-intensive.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

2013 Gordon Research Conference, Inorganic reaction mechanisms, Galveston, TX, March 3-8 2013

The 2013 Gordon Conference on Inorganic Reaction Mechanisms will present cutting-edge research on the molecular aspects of inorganic reactions involving elements from throughout the periodic table and state-of-the art techniques that are used in the elucidation of reaction mechanisms. The Conference will feature a wide range of topics, such as homogeneous and heterogeneous catalysis, metallobiochemistry, electron-transfer in energy reactions, polymerization, nitrogen fixation, green chemistry, oxidation, solar conversion, alkane functionalization, organotransition metal chemistry, and computational chemistry. The talks will cover themes of current interest including energy, materials, and bioinorganic chemistry. Sections cover: Electron-Transfer in Energy Reactions; Catalytic Polymerization and Oxidation Chemistry; Kinetics and Spectroscopy of Heterogeneous Catalysts; Metal-Organic Chemistry and its Application in Synthesis; Green Energy Conversion;Organometallic Chemistry and Activation of Small Molecules; Advances in Kinetics Modeling and Green Chemistry; Metals in Biology and Disease; Frontiers in Catalytic Bond Activation and Cleavage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Experimental and Computational Methods for the Deconstruction and Valorization of Lignin

Lignin is among the most abundant natural heteropolymers on Earth. While lignin represents an abundant feedstock material for the production of renewable fuels and value-added chemicals, it is an underutilized value stream in current biomass conversion technologies because there exist no economic and technically feasible routes for lignin depolymerization and upgrading. My work at NREL has focused on elucidating deconstruction pathways of this recalcitrant polymer using homogeneous and heterogeneous catalysts. This entails testing putative catalyst species against a library of model compounds as well as using computational modeling of the substrates and catalysts to determine thermodynamic and kinetic parameters. From these preliminary studies, I hope to develop strategies for the selective conversion of biomass to fuels and chemicals. Synthetic techniques applicable to a model compound library, synthesis and spectroscopic characterization of catalyst species, as well as DFT studies of all of the aforementioned will be presented.

09 BIOMASS FUELS↗
DOE OSTI2025

Enabling Innovative Analysis on Heterogeneous Clusters through HTCdaskgateway

High energy particle (HEP) physics research is going through fundamental changes as we move to collect larger amounts of data from the Large Hadron Collider (LHC). Analysis facilities and distributed computing, through HTCs, have come together to create the next pythonic generation of analysis by utilizing HTCdaskgateway, a Dask gateway extension, allowing users to spawn workers compatible with both their analysis and heterogeneous clusters in line with authentication requirements. This is enabling physicists to engage with scientific python in ways they had not before because of domain specific C++ tools. An example of HTCdaskgateway’s use is Fermilab’s Elastic Analysis Facility.

Chavez, Elise [U. Wisconsin, Madison (main)]↗
DOE OSTI2018

ExaTN

A challenging scientific problem that will benefit from exascale computing is predictive simulation of two- and three-dimensional quantum many-body Hamiltonians in condensed matter physics, together with the related strong entanglement problems in quantum chemistry and quantum computing. Efficient numerical simulations will require new insights into wavefunction compression algorithms that scale on extremely heterogeneous HPC architectures. To address this need, we propose to develop a scalable math library of parallel multi-linear algebraic numerical primitives for hierarchical tensor compression. Our library will subsequently be used for building an efficient, performance-portable HPC framework for simulating strongly correlated quantum systems. In particular, our will will be the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a hierarchical tensor decomposition quantum scheme capable of expressing local expectation values of strongly entangled higher-dimensional quantum systems efficiently. To address the extreme complexity and high computational cost of such schemes, we will automate their parallel implementation by delivering an integrated software framework, ExaTN (Exascale Tensor Networks), that will process hierarchical tensor representations on exascale HPC systems via an asymchronous task-based programming model. As a specific application, our framework will provide scientists with a capability of simulating strongly entangled quantum systems, thus positioning ORNL as a strategic leader in modeling of strongly correlated materials.

McCaskey, AlexanderJ↗
DOE OSTI2017

Heterogeneous Diels–Alder catalysis for biomass-derived aromatic compounds

In this tutorial review, we provide an overview of heterogeneous Diels–Alder catalysis for the production of lignocellulosic biomass-derived aromatic compounds. Diels–Alder reactions afford an extremely selective and efficient route for carbon–carbon cycloadditions to produce intermediates that can readily undergo subsequent dehydration or dehydrogenation reactions for aromatization. As a result, catalysis of Diels–Alder reactions with biomass-derived dienes and dienophiles has seen a growth of interest in recent years; however, significant opportunities remain to (i) tailor heterogeneous catalyst materials for tandem Diels–Alder and aromatization reactions, and (ii) utilize biomass-derived dienes and dienophiles to access both conventional and novel aromatic monomers. As such, this review discusses the mechanistic aspects of Diels–Alder reactions from both an experimental and computational perspective, as well as the synergy of Brønsted–Lewis acid catalysts to facilitate tandem Diels–Alder and aromatization reactions. Heterogeneous catalyst design strategies for Diels–Alder reactions are reviewed for two exemplary solid acid catalysts, zeolites and polyoxometalates, and recent efforts for targeting direct replacement aromatic monomers from biomass are summarized. In conclusion, we point out important research directions for progressing Diels–Alder catalysis to target novel, aromatic monomers with chemical functionality that enables new properties compared to monomers that are readily accessible from petroleum.

09 BIOMASS FUELS↗
DOE OSTI2019

Carbon Storage and Enhanced Oil Recovery in Pennsylvanian Morrow Formation Clastic Reservoirs: Controls on Oil–Brine and Oil–CO2 Relative Permeability from Diagenetic Heterogeneity and Evolving Wettability

The efficiency of carbon utilization and storage within the Pennsylvanian Morrow B sandstone, Farnsworth Unit, Texas, is dependent on three-phase oil, brine, and CO2 flow behavior, as well as spatial distributions of reservoir properties and wettability. We show that end member two-phase flow properties, with binary pairs of oil–brine and oil–CO2, are directly dependent on heterogeneity derived from diagenetic processes, and evolve progressively with exposure to CO2 and changing wettability. Morrow B sandstone lithofacies exhibit a range of diagenetic processes, which produce variations in pore types and structures, quantified at the core plug scale using X-ray micro computed tomography imaging and optical petrography. Permeability and porosity relationships in the reservoir permit the classification of sedimentologic and diagenetic heterogeneity into five distinct hydraulic flow units, with characteristic pore types including: macroporosity with little to no clay filling intergranular pores; microporous authigenic clay-dominated regions in which intergranular porosity is filled with clay; and carbonate–cement dominated regions with little intergranular porosity. Steady-state oil–brine and oil–CO2 co-injection experiments using reservoir-extracted oil and brine show that differences in relative permeability persist between flow unit core plugs with near-constant porosity, attributable to contrasts in and the spatial arrangement of diagenetic pore types. Core plugs “aged” by exposure to reservoir oil over time exhibit wettability closer to suspected in situ reservoir conditions, compared to “cleaned” core plugs. Together with contact angle measurements, these results suggest that reservoir wettability is transient and modified quickly by oil recovery and carbon storage operations. Reservoir simulation results for enhanced oil recovery, using a five-spot pattern and water-alternating-with-gas injection history at Farnsworth, compare models for cumulative oil and water production using both a single relative permeability determined from history matching, and flow unit-dependent relative permeability determined from experiments herein. Both match cumulative oil production of the field to a satisfactory degree but underestimate historical cumulative water production. Differences in modeled versus observed water production are interpreted in terms of evolving wettability, which we argue is due to the increasing presence of fast paths (flow pathways with connected higher permeability) as the reservoir becomes increasingly water-wet. The control of such fast-paths is thus critical for efficient carbon storage and sweep efficiency for CO2-enhanced oil recovery in heterogeneous reservoirs.

Rasmussen, Lindsey↗
DOE OSTI2018

Numerical analysis of experimental studies of methane hydrate dissociation induced by depressurization in a sandy porous medium

Methane Hydrates (MHs) are a promising energy source abundantly available in nature. Understanding the complex processes of MH formation and dissociation is critical for the development of safe and efficient technologies for energy recovery. Many laboratory and numerical studies have investigated these processes using synthesized MH-bearing sediments. A near-universal issue encountered in these studies is the spatial heterogeneous hydrate distribution in the testing apparatus. In the absence of direct observations (e.g. using X-ray computed tomography) coupled with real time production data, the common assumption made in almost all numerical studies is a homogeneous distribution of the various phases. In an earlier study (Yin et al., 2018) that involved the numerical description of a set of experiments on MH-formation in sandy medium using the excess water method, we showed that spatially heterogeneous phase distribution is inevitable and significant. In the present study, we use as a starting point the results and observations at the end of the MH formation and seek to numerically reproduce the laboratory experiments of depressurization-induced dissociation of the spatially-heterogeneous MH distribution. This numerical study faithfully reproduces the geometry of the laboratory apparatus, the initial and boundary conditions of the system, and the parameters of the dissociation stimulus, capturing accurately all stages of the experimental process. Using inverse modelling (history-matching) that minimized deviations between the experimental observations and numerical predictions, we determined the values of all the important flow, thermal, and kinetic parameters that control the system behaviour, which yielded simulation results that were in excellent agreement with the measurements of key monitored variables, i.e. pressure, temperature, cumulative production of gas and water over time. We determined that at the onset of depressurization (when the pressure drop – the driving force of dissociation – is at its maximum), the rate of MH dissociation approaches that of an equilibrium reaction and is limited by the heat transfer from the system surroundings. Furthermore, as the effect of depressurization declines over time, the dissociation reaction becomes kinetically limited despite significant heat inflows from the boundaries, which lead to localized temperature increases in the reactor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Athena: High-Performance Sparse Tensor Contraction Sequence on Heterogeneous Memory

Sparse tensor contraction sequence has been widely employed in many fields, such as chemistry and physics. However, how to efficiently implement the sequence faces multiple challenges, such as redundant computations and memory operations, massive memory consumption, and inefficient utilization of hardware. To address the above challenges, we introduce Athena, a high-performance framework for SpTC sequences. Athena introduces new data structures, leverages emerging Optane-based heterogeneous memory (HM) architecture, and stage parallelism. In particular, Athena introduces shared hash table-represented sparse accumulator to eliminate unnecessary input processing and data migration; Athena uses a novel data-semantic guided dynamic migration solution to make the best use of the Optane-based HM for high performance; Athena also co-runs execution phases with different characteristics to enable high hardware utilization. Evaluating with 12 datasets, we show that Athena brings 327-7362× speedup over the state-of-the-art SpTC algorithm. With the dynamic data placement guided by data semantics, Athena brings performance improvement on Optane-based HM over a state-of-the-art software-based data management solution, a hardware-based data management solution, and PMM-only by 1.58×, 1.82×, and 2.34× respectively.

Liu, Jiawen↗
DOE OSTI2024

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. In conclusion, our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Workflows↗
DOE OSTI2024

SIREN: Scaling Ion-Traps by REquiring iNnovative Heterogenous Integration

The SIREN (Scaling Ion Traps by Requiring iNnovative heterogenous integration) project explores the feasibility of heterogeneous integration (HI) as a transformative approach to scaling ion traps, a critical technology for advancing quantum computers and atomic clocks. Traditional ion trap architectures face significant challenges in scalability due to limitations in optical access, fabrication techniques, and material constraints. SIREN addresses these challenges by leveraging HI, which combines different materials and fabrication processes to create more complex and efficient ion trap structures. HI integrated structures can be manufactured without compromising the process to maintain compatibility to ion traps. This project focuses on integrating a separately fabricated waveguide with a fully functional ion trap. The respective alignment between the pieces needs to be accurate to less than 2 µm to ensure that the light from the waveguide can overlap with the trapping region. The fine alignment must also be maintained through an ultra-high vacuum bake, a critical step in preparing an ion trap experiment. The project's outcomes suggest that heterogeneous integration is a promising pathway for overcoming current scalability barriers, paving the way for the next generation of quantum technologies. SIREN's findings contribute significantly to the field, offering a scalable solution that could accelerate the development of practical quantum computers and highly accurate atomic clocks.

42 ENGINEERING↗
DOE OSTI2019

Predicting CO 2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Numerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large-scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input-output relations of physics-based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC-GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC-GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC-GAN is that it includes a self-training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC-GAN model is designed to learn cross-domain mappings between high-dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC-GAN for predicting the migration of carbon dioxide (CO 2 ) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC-GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO 2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC-GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC-GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics-based models used in both groundwater and subsurface energy exploration applications.

54 ENVIRONMENTAL SCIENCES↗