Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Heterogeneity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Heterogeneous Cluster Energetics and Nonlinear Thermodynamic Response in Supercritical Fluids

Microstructural heterogeneities arising from molecular clusters directly affect the nonlinear thermodynamic properties of supercritical fluids. We present a physical model to elucidate the relation between energy exchange and heterogeneous cluster dynamics during the transition from liquidlike to gaslike conditions. By analyzing molecular-dynamics data and employing physical principles, the model considers contributions from three key processes, namely, changing cluster density, cluster separation, and transfer of molecules between clusters. We show that the proposed model is consistent with the energetics at subcritical conditions and can be used to explain the nonlinear behavior of thermodynamic response functions, including the peak in the isobaric heat capacity.

42 ENGINEERING↗
DOE OSTI2024

CHARM-SYCL & IRIS: A Tool Chain for Performance Portability on Extremely Heterogeneous Systems

Performance portability is becoming crucial as high-performance computing systems become increasingly heterogeneous. We have many options for CPUs and accelerators (e.g., GPUs) but also for non-Von Neumann architectures such as field-programmable gate arrays. This paper presents the CHARM-SYCL unified programming environment for multiple accelerator types as a performance-portable programming environment. It uses the IRIS library developed at Oak Ridge National Laboratory as the back end accelerator runtime. IRIS has a high-performance scheduler to distribute tasks across accelerators. This design allows us to run an application from the same source on multiple systems with multiple configurations. We provide three types of portability with CHARM-SYCL: Portable Workflow, Compiler and Runtime Portability, and Application and Performance Portability. We implement a Monte Carlo simulation benchmark code on the CHARM-SYCL execution environment and demonstrate that our programming environment can accommodate extremely heterogeneous systems.

Fujita, Norihisa↗
DOE OSTI2021

Modeling Data Movement Performance on Heterogeneous Architectures

The cost of data movement on parallel systems varies greatly with machine architecture, job partition, and nearby jobs. Performance models that accurately capture the cost of data movement provide a tool for analysis, allowing for communication bottlenecks to be pinpointed. Modern heterogeneous architectures yield increased variance in data movement as there are a number of viable paths for inter-GPU communication. In this paper, we present performance models for the various paths of inter-node communication on modern heterogeneous architectures, including the trade-off between GPUDirect communication and copying to CPUs. Furthermore, we present a novel optimization for inter-node communication based on these models, utilizing all available CPU cores per node. Finally, we show associated performance improvements for MPI collective operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Assessing the Implications of Automated Merging Control in a Mixed and Heterogeneous Traffic Environment

Previous efforts to explore the implications of partial market penetration of connected and automated vehicles (CAVs) show a consensus on the benefits of higher market penetration rates (MPR) of vehicles enabled with connectivity and/or automation. There is, however, a level of uncertainty regarding the effects of lower market penetration rates and the consideration of heterogeneous vehicle fleets. Using VISSIM to perform microscopic traffic simulation and, vehicle simulation models, we assess the impacts of different CAVs market penetration rates on fuel consumption considering a heterogeneous traffic environment. The results show that the fuel efficiency benefits of optimal coordination control are maximized in moderate congested scenarios when the CAVs MPR exceeds 40%.

Rios Torres, Jackeline↗
DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimal Client Sampling in Federated Learning with Client-level Heterogeneous Differential Privacy

Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients’ privacy. However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must uniformly enforce the strictest privacy level across all clients, leading to excessive DP noise and significant degradation in model utility. Existing methods to improve the model utility in such heterogeneous privacy settings often assume a trusted server and are largely heuristic, resulting in suboptimal performance and lacking strong theoretical foundations. Here, in this work, we address these challenges under a practical attack model where both clients and the server are honest-but-curious. We propose GDPFed, which partitions clients into groups based on their privacy budgets and achieves client-level DP within each group to reduce the privacy budget waste and hence improve the model utility. Based on the privacy and convergence analysis of GDPFed, we find that the magnitude of DP noise depends on both model dimensionality and the per-group client sampling ratios. To further improve the performance of GDPFed, we introduce GDPFed+, which integrates model sparsification to eliminate unnecessary noise and optimizes per-group client sampling ratios to minimize convergence error. Extensive empirical evaluations on multiple benchmark datasets demonstrate the effectiveness of GDPFed+, showing substantial performance gains compared with state-of-the-art methods.

Xu, Jiahao [Univ. of Nevada, Reno, NV (United Stat↗
DOE OSTI2022

LaRIS: Targeting Portability and Productivity for LAPACK Codes on Extreme Heterogeneous Systems by Using IRIS

In keeping with the trend of heterogeneity in high-performance computing, hardware manufacturers and vendors are developing new architectures and associated software stacks (e.g., libraries) to harness the best possible performance from commonly used kernels (e.g., linear algebra kernels). However, kernels tuned for one architecture are not portable to others. Moreover, the coexistence of different architectures in a single node makes orchestration difficult. To address these challenges, we introduce LaRIS, a portable framework for LAPACK functionalities. LaRIS ensures a separation between linear algebra algorithms and vendor-library kernels by using the IRIS run time and IRIS-BLAS library. Such abstraction at the algorithm level makes the implementation completely agnostic to the vendor library and architecture. LaRIS uses the IRIS run time to dynamically select the vendor-library kernel and suitable processor architecture at run time. Through LU factorization, we demonstrate that LaRIS can fully utilize different heterogeneous systems by launching and orchestrating different vendor-library kernels without any change in the source code.

Monil, M. A. H.↗
DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Distributed Thermal Response Multi-Source Modeling to Evaluate Heterogeneous Subsurface Properties

A thorough assessment of thermal properties in heterogeneous subsurface is necessary in design of low-temperature borehole heat exchangers (BHEs). For this study, a distributed thermal response test (DTRT), which combines distributed temperature sensing (DTS) with a conventional thermal response test (TRT), was conducted in a U-bend geothermal loop installed in an open borehole at the University of Illinois at Urbana-Champaign to estimate thermal properties by analyzing the thermal response of different geologic materials while applying a constant heat input rate. Fiber-optic cables in the DTRT were deployed both inside the U-bend geothermal loop and in the center of the borehole to improve the accuracy of calculated heat-loss rates and borehole temperature profile measurements. To assess the subsurface thermal conductivity during the heating phase of the DTRT, a single-source model and a multi-source model, both based on the infinite line source method, were developed using the borehole temperature data and temperatures inside and along the outside of the loop, separately. The two models returned similar thermal conductivity values. The multi-source modeling has the advantage of predicting the thermal conductivity of heterogeneous geologic materials from borehole temperature profiles during the DTRT heating phase. Additionally, based on the distributed thermal conductivity measured in the borehole, estimates were made for both radial thermal impacts and the rate of heat loss in the BHE.

58 GEOSCIENCES↗
DOE OSTI2020

Slow slip source characterized by lithological and geometric heterogeneity

Slow slip events (SSEs) accommodate a significant proportion of tectonic plate motion at subduction zones, yet little is known about the faults that actually host them. The shallow depth (<2 km) of well-documented SSEs at the Hikurangi subduction zone offshore New Zealand offers a unique opportunity to link geophysical imaging of the subduction zone with direct access to incoming material that represents the megathrust fault rocks hosting slow slip. Two recent International Ocean Discovery Program Expeditions sampled this incoming material before it is entrained immediately down-dip along the shallow plate interface. Drilling results, tied to regional seismic reflection images, reveal heterogeneous lithologies with highly variable physical properties entering the SSE source region. These observations suggest that SSEs and associated slow earthquake phenomena are promoted by lithological, mechanical, and frictional heterogeneity within the fault zone, enhanced by geometric complexity associated with subduction of rough crust.

58 GEOSCIENCES↗
DOE OSTI2022

Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle—and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Design and analysis of CXL performance models for tightly-coupled heterogeneous computing

Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X.

Cabrera, Anthony↗
DOE OSTI2023

Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training

Deep Learning Recommendation Models (DLRMs) are critical applications in various domains and have evolved as one of the single largest machine learning applications. Trillions of DLRM parameters exceed the on-chip memory capacity of GPUs. Large-scale multi-node systems are required for distributed DLRM inference and training, which suffer from the all-to-all communication bottleneck, mainly limiting the scalability of ever-growing DLRMs. In recent years, SmartNICs have evolved with coupled computation and communication capabilities providing opportunities for a powerful heterogeneous device in the system. However, there isn't such a distributed system that fully leverages the abundant smartNIC resources that resolve the scalability issue of DLRMs. In this work, we proposed a software-hardware co-design of a heterogeneous smartNIC system that resolves the communication bottleneck of distributed DLRMs, mitigates the memory bandwidth pressure, and improves computation efficiency. We provide a set of smartNIC designs of cache systems (including local cache and remote cache) and smartNIC computation kernels which reduce data movement, relieve memory lookup intensity, and improve the GPU's computation efficiency. In addition, we propose a graph algorithm that improves the data locality of queries within batches which optimizes the overall system performance with higher data reuse. Our evaluation shows that our system achieves 2.1x latency speedup for inference and 1.6x throughput speedup for training.

Guo, Anqi↗
DOE OSTI2023

HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems

Quantum circuit simulations are applied in more and more circum- stances as the quantum computing community becomes broader. It helps researchers to evaluate quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilize either CPU or GPU to store and calculate the state vector, which results in resource starvation. Moreover, the maximum number of qubits supported by the simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeneous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benefits the data movement between CPU and GPU.

Zhang, Boyuan↗
DOE OSTI2023

MatRIS: Multi-level Math Library Abstraction for Heterogeneity and Performance Portability using IRIS Runtime

Vendor libraries are tuned for a specific architecture and are not portable to others. Moreover, they lack support for heterogeneity and multi-device orchestration, which is required for efficient use of contemporary HPC and cloud resources. To address these challenges, we introduce MatRIS—a multilevel math library abstraction for scalable and performance-portable sparse/dense BLAS/LAPACK operations using IRIS runtime. The MatRIS-IRIS co-design introduces three levels of abstraction to make the implementation completely architecture agnostic and provide highly productive programming. We demonstrate that MatRIS is portable without any change in source code and can fully utilize multi-device heterogeneous systems by achieving high performance and scalability on Summit, Frontier, and a CADES cloud node equipped with four NVIDIA A100 GPUs and four AMD MI100 GPUs. A detailed performance study is presented in which MatRIS demonstrates multi-device scalability. When compared, MatRIS provides competitive and even better performance than libraries from vendors and other third parties.

Monil, M. A. H.↗
DOE OSTI2024

Orchard: Heterogeneous Parallelism and Fine-grained Fusion for Complex Tree Traversals

Many applications are designed to perform traversals ontree-likedata structures. Fusing and parallelizing these traversals enhance the performance of applications. Fusing multiple traversals improves the locality of the application. The runtime of an application can be significantly reduced by extracting parallelism and utilizing multi-threading. Prior frameworks have tried to fuse and parallelize tree traversals using coarse-grained approaches, leading to missed fine-grained opportunities for improving performance. Other frameworks have successfully supported fine-grained fusion on heterogeneous tree types but fall short regarding parallelization. We introduce a new frameworkOrchardbuilt on top ofGrafter.Orchard’s novelty lies in allowing the programmer to transform tree traversal applications by automatically applyingfine-grainedfusion and extractingheterogeneousparallelism.Orchardallows the programmer to write general tree traversal applications in a simple and elegant embedded Domain-Specific Language (eDSL). We show that the combination of fine-grained fusion and heterogeneous parallelism performs better than each alone when the conditions are met.

Computer Science↗
DOE OSTI2022

Across-Depth Heterogeneity and Irreversibility of Fast-Charge-Driven Lithium Plating

Enabling fast charging in lithium-ion batteries (LIBs) is a key factor for resolving consumers’ “range anxiety” concerns in choosing an electric vehicle over a gasoline-powered one. The best-known issue in the fast charging of current LIBs (based on LiNi x Mn y Co 1–x–y O 2 /graphite) is lithium plating, which is barely reversible and is accompanied by capacity loss over time. Therefore, understanding the reasons for the irreversibility of lithium plating driven by fast charging is critical to enhancing the fast-charging capability of LIBs. Here, a study of the irreversibility and heterogeneity of lithium plating is carried out by using electrochemical analyses, inductively coupled plasma mass spectroscopy, and X-ray photoelectron spectroscopy. Further, the study shows that the majority of the lithium plated during fast charging remains inactive even with very slow discharging rates, implying that most of the plated lithium is irreversibly lost. The study also reveals that chemical compounds such as LiF, Li 2 O and Li 2 (CO 3 ) are formed heterogeneously within the lithium plating and are responsible for some of the irreversibility of the plated lithium.

33 ADVANCED PROPULSION SYSTEMS↗