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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space remains fragmented. There are no formal methods and metrics to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance \& power efficiency characteristics. Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems. In this paper, we develop a structured approach to the design, evaluation and optimization of HPC resilience using the concept of design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the problems caused by various types of faults, errors and failures in HPC systems and the techniques used to deal with these events. Each well-known solution that addresses a specific HPC resilience challenge is described in the form of a pattern. We develop a complete catalog of such resilience design patterns, which may be used by system architects, system software and tools developers, application programmers, as well as users and operators as essential building blocks when designing and deploying resilience solutions. We also develop a design framework that enhances a designer's understanding the opportunities for integrating multiple patterns across layers of the system stack and the important constraints during implementation of the individual patterns. It is also useful for defining mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The resilience patterns and the design framework also enable exploration and evaluation of design alternatives and support optimization of the cost-benefit trade-offs among performance, protection coverage, and power consumption of resilience solutions. Here, the overall goal of this work is to establish a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Finite element analyses of single particle crushing tests incorporating computed tomography imaging and damage mechanics

Fracture of individual grains within a granular assembly affect the strength and deformation behavior of granular materials under large stress states. Interparticle forces, leading to contact stresses and ultimately fracture initiation, are influenced by particle morphology. A numerical method that incorporates both particle fracture and morphology can provide a more accurate model of a granular material’s macro-scale response. This research investigates an approach to modeling fracture initiation and propagation within individual grains, utilizing high resolution X-ray computed tomography (CT) imaging to consider grain morphology and an explicit finite element code optimized for high performance computing which incorporates damage mechanics. Using precisely measured force-displacement curves from single particle crushing tests of manufactured quartz spheres, the method is validated, and the effects of fracture properties on grain fragmentation are determined. This approach is then used to simulate single particle crushing of Ottawa sand, with grain morphology incorporated through discretized CT images. Here the effect of grain orientation on the force and applied displacement at catastrophic splitting is also explored. The proposed discrete particle based finite element framework can be applied to modeling granular assemblies, considering the effect of individual particle shape and fracture on the assembly’s deformation response through well calibrated numerical simulations.

42 ENGINEERING↗
DOE OSTI2023

Quantum Programming Paradigms and Description Languages

Here, this article offers perspective on quantum computing programming languages, as well as their emerging runtimes and algorithmic modalities. With the scientific high-performance computing (HPC) community as a target audience, we describe the current state of the art in the field, and outline programming paradigms for scientific workflows. One take-home message is that there is significant work required to first refine the notion of the quantum processing unit in order to integrate in the HPC environments. Programming for today’s quantum computers is making significant strides toward modern HPC-compatible workflows, but key challenges still face the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Testing Vpin on Big Data Response to Reflecting on the Vpin Dispute

The authors of this note are computer scientists and mathematicians from Lawrence Berkeley National Laboratory. Most of us are specialists in high-performance computing. In 2011, at the suggestion of a CFTC commissioner, we started examining the effectiveness of VPIN as a leading indicator of unusual market events such as the “Flash Crash” of 2010. The preliminary result of that work was published as a conference paper. Our work was the first to show that VPIN could generate strong signals based on trading records of individual stocks. On a number of stocks, VPIN generated strong signals more than an hour before the “Flash Crash.” This lead-time could be valuable for market makers, regulators and traders in general. To further test the effectiveness of VPIN, we recently expanded our tests to a much larger set of test data, including the trading records of the 94 most liquid futures contracts from beginning of 2007 to middle of 2012. We set out to verify the following hypothesis: the volatility of trading is higher than the average in a time window (we call the event horizon) after VPIN value crosses a threshold. Our work carefully examined the parameters controlling the computation of VPIN as well as the event horizon and the VPIN threshold. In this largest-ever study of a leading indicator based on market microstructure, we examined 16,000 combinations of these parameters, and found that the average false positive rate can be reduced to 7%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Final Report on XStack: Software Synthesis for High Productivity ExaScale Computing

The goal of the project was to develop a programming model that would significantly improve productivity in the high-performance computing domain by bringing together three components: a) Automated equivalence checking, b) Sketch-based program synthesis, and c) Autotuning. The report provides an executive summary of the research accomplished through this project. At the end of the report is appended a paper that describes in more detail the key technical accomplishments from this project, and which was published in SC 2014.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

HPC Institutional Computing Project: W15_lesreactiveflow KIVA-hpFE Development: A Robust and Accurate Engine Modeling Software

KIVA-hpFE is a high performance computer software for solving the physics of multi-species and multiphase turbulent reactive flow in complex geometries having immersed moving parts. The code is written in Fortran 90/95 and can be used on any computer platform with any popular complier. The code is in two versions, a serial version and a parallel version utilizing MPICH2 type Message Passing Interface (MPI or Intel MPI) for solving distributed domains. The parallel version is at least 30x faster than the serial version and much faster than our previous generation of parallel engine modeling software, by many factors. The 5th generation algorithm construction is a Galerkin type Finite Element Method (FEM) solving conservative momentum, species, and energy transport equations along with two-equation turbulent model k-ω Reynolds Averaged Navier-Stokes (RANS) model and a Vreman type dynamic Large Eddy Simulation (LES) method. The LES method is capable modeling transitional flow from laminar to fully turbulent; therefore, this LES method does not require special hybrid or blending to walls. The FEM projection method also uses a Petrov-Galerkin (P-G) stabilization along with pressure stabilization. We employ hierarchical basis sets, constructed on the fly with enrichment in areas associated with relatively larger error as determined by error estimation methods. In addition, when not using the hp-adaptive module, the code employs Lagrangian basis or shape functions. The shape functions are constructed for hexahedral, prismatic and tetrahedral elements. The software is designed to solve many types of reactive flow problems, from burners to internal combustion engines and turbines. In addition, the formulation allows for direct integration of solid bodies (conjugate heat transfer), as in heat transfer through housings, parts, cylinders. It can also easily be extended to stress modeling of solids, used in fluid structure interactions problems, solidification, porous media modeling and magneto hydrodynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

AToM (Advanced Tokamak Modeling)

This final report for the AToM project documents the contributions from General Atomics over the period 1 September 2014 to 31 August 2017. This includes significant progress on the development of tokamak integrated modeling frameworks (OMFIT and IPS), and development of new workflows for computing plasma profile (temperature, density, etc) evolution. Significant effort was also carried out in the high-performance computing arena, by adding the COGENT edge-simulation code to the AToM portfolio, and performing optimization of the CGYRO plasma turbulence code. Issues of data management and user outreach are also documented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

OpenSHMEM Implementation of IOR Benchmark

The benchmarking effort within the Computational Research & Development Programs at the Oak Ridge National Laboratory (ORNL) seeks to design and enable High Performance Computing (HPC) benchmarks and test suites. The work described in this report is a part of the effort focusing on the comparison and analysis of OpenSHMEM implementations using the Interleave Or Random (IOR) software for benchmarking parallel file system using POSIX, MPIIO, or HDF5 interfaces. We describe the effort to emulate the MPIIO parallel collective capabilities in the IOR benchmark using OpenSHMEM communication. One development effort was in emulating the MPI derived datatype used in the read/write operations and in setting the file view. Another effort was in implementing an internal cache in OpenSHMEM distributed shared memory to facilitate global collective I/O operations. Experiments comparing collective I/O in MPIIO implementations with the OpenSHMEM implementations were performed on the SGI Turing Cluster and the Cray XK7 Titan supercomputer at the Oak Ridge Leadership Computing Facility (OLCF). The preliminary results suggest that on the Cray XK7 Titan, the MPIIO implementations obtained higher write performance and the OpenSHMEM version obtained slightly higher read performance. On the SGI Turing Cluster, the MPIIO implementations obtained slightly higher performance over the OpenSHMEM implementations on large files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Computing Annual Report 2023

In 2023, advanced computing saw the arrival of Kestrel, the National Renewable Energy Laboratory's (NREL's) newest high-performance computing (HPC) system. Kestrel will accelerate clean energy research at a pace and scale more than five times greater than Eagle, with approximately 44 petaflops of computing power. Kestrel's heterogeneous architecture - which includes both CPU-only and GPU-accelerated nodes - is designed to bring a much greater GPU capacity to EERE workloads compared to Eagle, enabling rapidly advancing applications in artificial intelligence and expanding research in new directions for computing. In Fiscal Year (FY) 2023, 333 projects utilized NREL's HPC system, advancing the U.S. Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy (EERE) mission across 13 funding areas. Cross-disciplinary collaboration among researchers yielded more than 800 technical outputs, including 177 peer-reviewed journal articles in FY 2023. All this great work continues to advance the science of energy efficiency and renewable energy. This report highlights research that utilized HPC resources in FY 2023.

advanced computing↗
DOE OSTI2019

A Quantitative Study of Deep Learning Training on Heterogeneous Supercomputers

Deep learning (DL) has become a key technique for solving complex problems in scientific research and discovery. DL training for science is substantially challenging because it has to deal with massive quantities of multi-dimensional data. High- performance computing (HPC) supercomputers are increasingly being employed for meeting the exponentially growing demand for DL. Multiple GPUs and high-speed interconnect network are needed for supporting DL on HPC systems. However, the excessive use of GPUs without considering effective benefits leads to inefficient resource utilization of these expensive setups. In this paper, we conduct a quantitative analysis to gauge the efficacy of DL workloads on the latest HPC system and identify viability of next-generation DL-optimized heterogeneous supercomputers for enabling researchers to develop more efficient resource management and distributed DL middleware. We evaluate well- known DL models with large-scale datasets using the popular TensorFlow framework, and provide a thorough evaluation in- cluding scalability, accuracy, variability, storage resource, GPU- GPU/GPU-CPU data transfer, and GPU utilization. Our analysis reveals that the latest heterogeneous supercomputing cluster shows varying performance trend as compared to the existing literature for single- and multi-node training. To the best of our knowledge, this is the first work to conduct such a quantitative and comprehensive study of DL training on a supercomputing system with multiple GPUs.

Han, Jingoo↗
DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2015

DGDFT: A massively parallel method for large scale density functional theory calculations

We describe a massively parallel implementation of the recently developed discontinuous Galerkin density functional theory (DGDFT) method, for efficient large-scale Kohn-Sham DFT based electronic structure calculations. The DGDFT method uses adaptive local basis (ALB) functions generated on-the-fly during the self-consistent field iteration to represent the solution to the Kohn-Sham equations. The use of the ALB set provides a systematic way to improve the accuracy of the approximation. By using the pole expansion and selected inversion technique to compute electron density, energy, and atomic forces, we can make the computational complexity of DGDFT scale at most quadratically with respect to the number of electrons for both insulating and metallic systems. We show that for the two-dimensional (2D) phosphorene systems studied here, using 37 basis functions per atom allows us to reach an accuracy level of 1.3 × 10{sup −4} Hartree/atom in terms of the error of energy and 6.2 × 10{sup −4} Hartree/bohr in terms of the error of atomic force, respectively. DGDFT can achieve 80% parallel efficiency on 128,000 high performance computing cores when it is used to study the electronic structure of 2D phosphorene systems with 3500-14 000 atoms. This high parallel efficiency results from a two-level parallelization scheme that we will describe in detail.

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

GPGPU Computing and Visualization on GPUs at LANL

DC is launching a new program to recognize noteworthy achievements made by users using High Performance Computing (HPC) technologies. The program’s main goals are to: • Showcase success stories involving HPC in science and industry • Help other users better understand the benefits of adopting HPC and justify HPC investments, especially for SMBs • Demonstrate the value of HPC to funding bodies • Expand public support for increased HPC investments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

NEAMS Update: Quarterly Report for October - December 2011

The Advanced Modeling and Simulation Office within the DOE Office of Nuclear Energy (NE) has been charged with revolutionizing the design tools used to build nuclear power plants during the next 10 years. To accomplish this, the DOE has brought together the national laboratories, U.S. universities, and the nuclear energy industry to establish the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program. The mission of NEAMS is to modernize computer modeling of nuclear energy systems and improve the fidelity and validity of modeling results using contemporary software environments and high-performance computers. NEAMS will create a set of engineering-level codes aimed at designing and analyzing the performance and safety of nuclear power plants and reactor fuels. The truly predictive nature of these codes will be achieved by modeling the governing phenomena at the spatial and temporal scales that dominate the behavior. These codes will be executed within a simulation environment that orchestrates code integration with respect to spatial meshing, computational resources, and execution to give the user a common 'look and feel' for setting up problems and displaying results. NEAMS is building upon a suite of existing simulation tools, including those developed by the federal Scientific Discovery through Advanced Computing and Advanced Simulation and Computing programs. NEAMS also draws upon existing simulation tools for materials and nuclear systems, although many of these are limited in terms of scale, applicability, and portability (their ability to be integrated into contemporary software and hardware architectures). NEAMS investments have directly and indirectly supported additional NE research and development programs, including those devoted to waste repositories, safeguarded separations systems, and long-term storage of used nuclear fuel. NEAMS is organized into two broad efforts, each comprising four elements. The quarterly highlights October-December 2011 are: (1) Version 1.0 of AMP, the fuel assembly performance code, was tested on the JAGUAR supercomputer and released on November 1, 2011, a detailed discussion of this new simulation tool is given; (2) A coolant sub-channel model and a preliminary UO{sub 2} smeared-cracking model were implemented in BISON, the single-pin fuel code, more information on how these models were developed and benchmarked is given; (3) The Object Kinetic Monte Carlo model was implemented to account for nucleation events in meso-scale simulations and a discussion of the significance of this advance is given; (4) The SHARP neutronics module, PROTEUS, was expanded to be applicable to all types of reactors, and a discussion of the importance of PROTEUS is given; (5) A plan has been finalized for integrating the high-fidelity, three-dimensional reactor code SHARP with both the systems-level code RELAP7 and the fuel assembly code AMP. This is a new initiative; (6) Work began to evaluate the applicability of AMP to the problem of dry storage of used fuel and to define a relevant problem to test the applicability; (7) A code to obtain phonon spectra from the force-constant matrix for a crystalline lattice has been completed. This important bridge between subcontinuum and continuum phenomena is discussed; (8) Benchmarking was begun on the meso-scale, finite-element fuels code MARMOT to validate its new variable splitting algorithm; (9) A very computationally demanding simulation of diffusion-driven nucleation of new microstructural features has been completed. An explanation of the difficulty of this simulation is given; (10) Experiments were conducted with deformed steel to validate a crystal plasticity finite-element code for bodycentered cubic iron; (11) The Capability Transfer Roadmap was completed and published as an internal laboratory technical report; (12) The AMP fuel assembly code input generator was integrated into the NEAMS Integrated Computational Environment (NiCE). More details on the planned NEAMS computing environment is given; and (13) The NEAMS program website (neams.energy.gov) is nearly ready to launch.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗