SEARCH · Engineering Papers

Results for “high performance parallel 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.

SourceResearch connectionFrom yearThrough year

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

At least 1,099 records · Page 61

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota’s iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

The divide-expand-consolidate coupled cluster scheme: Divide-expand-consolidate coupled cluster

The Divide-Expand-Consolidate (DEC) scheme is a linear-scaling and massively parallel framework for high accuracy coupled cluster (CC) calculations on large molecular systems. It is designed as a black-box method, which ensures error control in the correlation energy and molecular properties. DEC is combined with a massively parallel implementation to fully utilize modern many core architectures providing a fast time to solution. The implementation ensures performance portability and will straightforwardly benefit from new hardware developments. The DEC scheme has been applied to several levels of CC theory and extended the range of application of those methods.

Chemistry↗

DOE OSTI2019

Cataloging the visible universe through Bayesian inference in Julia at petascale

A key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as brightness, color, and morphology. This task, known as cataloging, is challenging for several reasons: many astronomical objects are much dimmer than the sky background, labeled data is generally unavailable, overlapping astronomical objects must be resolved collectively, and the datasets are enormous – terabytes now, petabytes soon. In this work, we infer an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188 M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 min. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori’s burst buffer. Julia’s native performance enables Celeste to employ high-level constructs without resorting to hand-written or generated low-level code (C/C++/Fortran) while still achieving petascale performance.

Regier, Jeffrey↗

DOE OSTI2020

AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case

Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process. Here, we propose a general approach to this process based on Bayesian optimization and machine learning that encodes detector requirements. As a case study, we focus on the design of the dual-radiator Ring Imaging Cherenkov (dRICH) detector under development as a potential component of the particle-identification system at the future Electron-Ion Collider (EIC). The EIC is a US-led frontier accelerator project for nuclear physics, which has been proposed to further explore the structure and interactions of nuclear matter at the scale of sea quarks and gluons. We show that the detector design obtained with our automated and highly parallelized framework outperforms the baseline dRICH design within the assumptions of the current model. Our technique can be applied to any detector R&D, provided that realistic simulations are available.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

FitCache: A Transparent Drop-In Framework for Multi-Tier Caching to Accelerate Distributed Deep Learning Workloads

Training in Deep learning (DL) remains highly compute- and data-intensive, with I/O becoming a critical bottleneck as models and datasets scale. Recent studies report that data loading can dominate training time, especially on large-scale HPC systems with shared parallel file systems (PFS). Existing caching approaches either rely on single-tier designs or require intrusive modifications to training pipelines, limiting their portability and effectiveness. In this work, we present FitCache, a transparent drop-in framework for multi-tier caching to accelerate distributed DL training by coordinating fast local memory (e.g., DRAM, Persistent Memory (PMem)) and NVMe as hierarchical caches atop PFS. Our design adapts to hardware diversity, i.e., if NVMe is missing, memory transparently acts as a caching tier, ensuring stable performance. FitCache transparently intercepts I/O requests and issues concurrent fetches across all tiers, returning data from the fastest responder without centralized metadata or static redirection paths. FitCache adapts to dynamic workloads and heterogeneous clusters while maintaining POSIX compatibility. Experiments on Frontier (2048 GPUs) and smaller research clusters show that FitCache reduces training time by up to 40% and per-batch I/O latency by up to 71.6% compared to Lustre Orion PFS, offering a drop-in solution for scalable DL training.

Hu, Guangxing [ORNL] (ORCID:0009000283203614)↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

Zhang, Xingyao↗

DOE OSTI2016

EMAPS: An Efficient Multiscale Approach to Plasma Systems with Non-MHD Scale Effects

Global interactions of energetic ions with magnetoplasmas and neutral gases lie at the core of many space and laboratory plasma phenomena ranging from solar wind entry into and transport within planetary magnetospheres and exospheres to fast-ion driven instabilities in fusion devices to astrophysics-in-lab experiments. The ability of computational models to properly account for physical effects that underlie such interactions, namely ion kinetic, ion cyclotron, Hall, collisional and ionization processes is important for the success and planning of experimental research in plasma physics. Understanding the physics of energetic ions, in particular their nonlinear resonance interactions with Alfvén waves, is central to improving the heating performance of magnetically confined plasmas for future energy generation. Fluid models are not adequate for high-beta plasmas as they cannot fully capture ion kinetic and cyclotron physics (e.g., ion behavior in the presence of magnetic nulls, shock structures, plasma interpenetration, etc.). Recent results from global reconnection simulations show that even in a MHD-like regime there may be significant differences between kinetic and MHD simulations. Therefore, kinetic modeling becomes essential for meeting modern day challenges in plasma physics. The hybrid approximation is an intermediate approximation between the fluid and fully kinetic approximations. It eliminates light waves, removes the electron inertial temporal and spatial scales from the problem and enables full-orbit ion kinetics. As a result, hybrid codes have become effective tools for exploring ion-scale driven phenomena associated with ion beams, shocks, reconnection and turbulence that control the large-scale behavior of laboratory and space magnetoplasmas. A number of numerical issues, however, make three-dimensional (3D) large-scale hybrid simulations of inhomogeneous magnetized plasmas prohibitively expensive or even impossible. To resolve these difficulties we have developed a novel Event-driven Multiscale Asynchronous Parallel Simulation (EMAPS) technology that replaces time stepping with self-adaptive update events. Local calculations are carried out only on an “as needed basis”. EMAPS (i) guarantees accurate and stable processing of physical variables in time accurate simulations, and (ii) eliminates unnecessary computation. Applying EMAPS to the hybrid model has resulted in the development of a unique parallel code, dimension-independent (compile-time-configurable) HYPERS (Hybrid Parallel Event-Resolved Simulator) that scales to hundreds of thousands of parallel processors. HYPERS advances electromagnetic fields and particles asynchronously on time scales determined by local physical laws and mesh properties. To achieve high computational accuracy in complex device geometries, HYPERS employs high-fidelity Cartesian grids with masked conductive cells. The HYPERS model includes multiple ion species, energy and momentum conserving ion-ion collisions, and provides a number of approximations for plasma resistivity and vacuum regions. Both local and periodic boundary conditions are allowed. The HYPERS solver preserves zero divergence of magnetic field. The project has demonstrated HYPERS capabilities on a number of applications of interest to fusion and astrophysical plasma physics applications listed below. 1. Theta-pinch formation of FRCs The formation, spontaneous spin-up, and stability of theta-pinch formed field-reversed configurations have been studied self-consistently in 3D. The end-to-end hybrid simulations reveal poloidal profiles of implosion-driven fast toroidal plasma rotation and demonstrate three discharge regimes as a function of experimental parameters: the decaying stable configuration, the tilt unstable configuration, and the nonlinear evolution of a fast growing tearing mode. 2. FRC collisions with magnetic mirrors Interactions of fast plasma streams and objects with magnetic obstacles (dipoles, mirrors, etc) lie at the core of many space and laboratory plasma phenomena ranging from magnetoshells and solar wind interactions with planetary magnetospheres to compact fusion plasmas. HYPERS simulations are compared with data from the MSX experiment (LANL) that focuses on the physics of magnetized collisionless shocks through the acceleration and subsequent stagnation of FRC plasmoids against a strong magnetic mirrors and flux-conserving boundaries. 3. Exploding magnetoplasmas Results from hybrid simulations of two experiments at the LAPD and Nevada Terawatt Facility are discussed where short-pulse lasers are used to ablate solid targets to produce plasmas that expand across external magnetic fields. The first simulation recreates flutelike density striations observed at the leading edge of a carbon plasma and predicts an early destruction of the magnetic cavity in agreement with experimental evidence. In the second simulation a polyethylene target is ablated into a mixture of protons and carbon ions. A mechanism is demonstrated that allows protons to penetrate the magnetic field in the form of a collimated flow. The results are compared to experimental data and single-fluid MHD simulations. The EMAPS framework has the potential for wide application in many other engineering and scientific fields, such as climate models, biological systems, electronic devices, seismic events, oil reservation simulators that all involve advancing solutions of partial differential equations in time where the rate of activity can be adapted widely over the spatial domain depending on locally space/time phenomena (“events”).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Barrier elision for production parallel programs

Large scientific code bases are often composed of several layers of runtime libraries, implemented in multiple programming languages. In such situation, programmers often choose conservative synchronization patterns leading to suboptimal performance. In this paper, we present context-sensitive dynamic optimizations that elide barriers redundant during the program execution. In our technique, we perform data race detection alongside the program to identify redundant barriers in their calling contexts; after an initial learning, we start eliding all future instances of barriers occurring in the same calling context. We present an automatic on-the-fly optimization and a multi-pass guided optimization. We apply our techniques to NWChem - a 6 million line computational chemistry code written in C/C++/Fortran that uses several runtime libraries such as Global Arrays, ComEx, DMAPP, and MPI. Our technique elides a surprisingly high fraction of barriers (as many as 63%) in production runs. This redundancy elimination translates to application speedups as high as 14% on 2048 cores. Our techniques also provided valuable insight about the application behavior, later used by NWChem developers. Overall, we demonstrate the value of holistic context-sensitive analyses that consider the domain science in conjunction with the associated runtime software stack.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Performance Assessment of Emerging Memories Through FPGA Emulation

Emerging memory technologies offer the prospect of large capacity, high bandwidth, and a range of access latencies ranging from DRAM-like to SSD-like. In this paper, we evaluate the performance of parallel applications on CPUs whose main memories sweep a wide range of latencies within a bandwidth cap. We use an FPGA emulator, the logic in memory emulator (LiME) to accelerate evaluation. The LiME framework uses a multiprocessor system on chip (MPSoC) combining multicore CPU, integrated memory controller, and FPGA fabric, and enables emulation orders of magnitude faster than software simulation. This work highlights the performance impact of higher latency on concurrent applications and identifies conditions under which future high latency memories can effectively be used as main memory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

On Data Transfers Over Wide-Area Dedicated Connections

Dedicated wide-area network connections are employed in big data and high-performance computing scenarios, since the absence of cross-traffic promises to make it easier to analyze and optimize data transfers over them. However, nonlinear transport dynamics and end-system complexity due to multi-core hosts and distributed file systems make these tasks surprisingly challenging. We present an overview of methods to analyze memory and disk file transfers using extensive measurements over 10 Gbps physical and emulated connections with 0–366 ms round trip times (RTTs). For memory transfers, we derive performance profiles of TCP and UDT throughput as a function of RTT, which show concave regions in contrast to entirely convex regions predicted by previous models. These highly desirable concave regions can be expanded by utilizing large buffers and more parallel flows. We also present Poincar´e maps and Lyapunov exponents of TCP and UDT throughputtraces that indicate complex throughput dynamics. For disk file transfers, we show that throughput can be optimized using a combination of parallel I/O and network threads under direct I/O mode. Our initial throughput measurements of Lustre filesystems mounted over long-haul connections using LNet routers show convex profiles indicative of I/O limits.

Rao, Nageswara S.↗

DOE OSTI2017

A Unit Testing Framework for Scientific Legacy Code

Large-scale scientific applications play important roles in supporting research. However, it is often very expensive and time-consuming to make changes to, maintain and evolve the scientific code due to its complexity and poor programming skills of researchers. Therefore, in order to visualize scientific code architecture to optimize software design, understand undocumented source code, and analyze software flow and functionality, we first introduce a unit testing framework (UTF). Then, because such infrastructure's performance is very crucial in practical use since the scientific legacy applications simulate instances in a long period of time, we improve the UTF by applying Message Passing based Parallelization and parallel I/O operations. Furthermore, due to the scientific code has enormous state data and the I/O capacity on the server is limited, we apply in situ data analysis method to encounter fewer resource limitations, and adopt signal processing to greatly reduce data transfer. Last, we demonstrated the correctness and high-efficiency of our framework for legacy Earth model on Titan supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Large-Scale Simulation of Nuclear Reactors: Issues and Perspectives

Numerical simulation has been an intrinsic part of nuclear engineering research since its inception. In recent years a transition is occurring toward predictive, first-principle-based tools such as computational fluid dynamics. Even with the advent of petascale computing, however, such tools still have significant limitations. In the present work some of these issues, and in particular the presence of massive multiscale separation, are discussed, as well as some of the research conducted to mitigate them. Petascale simulations at high fidelity (large eddy simulation/direct numerical simulation) were conducted with the massively parallel spectral element code Nek5000 on a series of representative problems. These simulations shed light on the requirements of several types of simulation: (1) axial flow around fuel rods, with particular attention to wall effects; (2) natural convection in the primary vessel; and (3) flow in a rod bundle in the presence of spacing devices. The focus of the work presented here is on the lessons learned and the requirements to perform these simulations at exascale. Additional physical insight gained from these simulations is also emphasized.

CFD↗

DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Sensitivity Analysis of Passive Heat Removal Capability of FHR Based on Efficient Surrogate Model and GenFlow

Code Scaling, Applicability and Uncertainty Evaluation Methodology (CSAU) is used for quantifying reactor safety margins and works as an alternative method for licensing nuclear power plant. In the framework of CSAU, sensitivity and uncertainty analysis (SA and UA) should be performed. However, for large-scale complicated thermal hydraulics analysis simulated by code (e.g. RELAP5, CFD and subchannel analysis, the sensitivity and uncertainty analysis is really time-consuming. Furthermore, sometimes it is not feasible to perform SA and UA. Specifically, the computation time for Monte Carlo sampling or advanced LHS sampling can be neglected, but the computation time for thermal hydraulics code makes up main part of time during SA and UA process. In order to accelerate the speed of SA and UA, thermal hydraulics code can be improved based on advanced mathematic algorithm or using parallel method. However, the development for new mathematic method needs long time and strict verification and validation process is needed for a new code, which usually takes a couple of years or even longer time to have an approved code that can be used in nuclear industry. An alternative way to improve the speed of SA and UA is using surrogate model. The surrogate model is derived from the input as well as output data of the original code, assuming the code as a black box. The most common method is using response surface method (RSM) as a surrogate for the code, the input parameters are then sampled from their assumed distribution and the corresponding value of the response surface function is calculated. However, a couple of input parameters need hundreds or even thousands of original code calculations. The large number of runs is an obvious disadvantage of this method. Besides, the second disadvantage is an appropriate response surface function must be selected (e.g. quadratic, cubic, quartic, with or without cross product terms, or using a non-polynomial form) to avoid the overfit and underfit problem. This process is time consuming and expensive. Polynomial chaos expansion method is a reasonably fast and attractive alternative, stochastic model. The chaos expansion provides an efficient high-order method of including non-linear effects in stochastic analysis. Usually, the information about the model parameters and their probability distribution is limited and the dependence between them is not known. However, if arbitrary polynomial chaos is used, instead of assuming probability distribution for input parameters which may introduces additional subjectivity into the analysis, the available information can directly and most purely be used in stochastic analysis based on arbitrary data distribution. Thus, it is not necessary to know the exact probability density functions to perform SA and UA based on arbitrary polynomial chaos (aPC). The aPC has been applied for uncertainty quantification in many fields (e.g. in solid mechanics, fluid flows, thermal sciences), and become widely used in nuclear industry recently. Mk1 PB-FHR is a small modular fluoride-salt-cooled, high-temperature reactors (FHRs) that is a concept design by University of California, Berkeley (UCB). It has inherently safe features like natural-circulation-driven systems (e.g. direct reactor auxiliary cooling system (DRACS)) to passively remove decay heat from the reactor core during design basis and beyond design. It is necessary to perform SA and UA, since the licensing process of this new kind of reactor is under way and there are many design parameters that have not been fixed. Current nuclear commercial codes, such as RELAP5, have mainly been developed for light water reactors, which require the use of comprehensive two-phase flow models. This capability is not required to simulate single-phase flow phenomena in FHRs, whose baseline coolant, flibe, remains liquid up to 1430 deg. C at atmospheric pressure. The FHR advanced natural circulation analysis (FANCY) code has been developed specifically for FHR technology modeling and validation work has been performed by using CIET data in UCB. The code has been renamed General Flow (GenFlow) because it has been further developed to have the capability to model complicated pipework system based on object-oriented concept for user friendly purpose. Besides, it can be used to simulate both the force circulation as well as natural circulation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗