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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Workshop on Manufacturing and Integration Challenges for Analog and Neuromorphic Computing

The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office (AMO) held its third virtual workshop on Semiconductor R&D for Energy Efficiency on August 11-13, 2021. This public workshop – titled “Manufacturing and Integration Challenges for Analog and Neuromorphic Computing” – brought together more than 180 leading scientific and technical experts to identify challenges and opportunities to improve manufacturing capabilities for analog and neuromorphic computing architectures to significantly increase energy efficiency of microelectronic devices. The workshop featured perspectives of researchers from national laboratories, universities, government agencies, and industry with expertise in novel and emerging devices, processes, and metrology and characterization. This workshop is intended to inform an AMO research, development, demonstration, and deployment (RDD&D) plan to significantly reduce semiconductor energy use by 2030. It was co-sponsored by the Semiconductor Research Corporation (SRC) and the DOE Office of Science’s Advanced Scientific Computing Research (ASCR).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Position Papers for the 2024 ASCR Workshop on Neuromorphic Computing for Science

Engineering novel neuromorphic computing systems with functionalities, capabilities, and energy efficiency similar to biological brains is one of the most exciting and challenging scientific endeavors of our time. This workshop aims to identify key research needs, challenges, and next steps necessary to develop biologically realistic neuromorphic circuits primitives that capture the functionality of neural systems found in nature. Moreover, simulating neuromorphic computing primitives integrated into networks will be key to under standing their behavior at scale, particularly for those computing architectures where full-scale commercial fabrication is not yet readily accessible. Appropriate neuroscience datasets and metrics will have to be established to vet proposed neuromorphic circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P's multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability of the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. Furthermore, these performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Sub-nanosecond signal propagation in anisotropy-engineered nanomagnetic logic chains

Energy efficient nanomagnetic logic (NML) computing architectures propagate binary information by relying on dipolar field coupling to reorient closely spaced nanoscale magnets. In the past, signal propagation in nanomagnet chains were characterized by static magnetic imaging experiments; however, the mechanisms that determine the final state and their reproducibility over millions of cycles in high-speed operation have yet to be experimentally investigated. Here we present a study of NML operation in a high-speed regime. We perform direct imaging of digital signal propagation in permalloy nanomagnet chains with varying degrees of shape-engineered biaxial anisotropy using full-field magnetic X-ray transmission microscopy and time-resolved photoemission electron microscopy after applying nanosecond magnetic field pulses. Moreover, an intrinsic switching time of 100 ps per magnet is observed. In conclusion these experiments, and accompanying macrospin and micromagnetic simulations, reveal the underlying physics of NML architectures repetitively operated on nanosecond timescales and identify relevant engineering parameters to optimize performance and reliability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Navy Enhanced Sierra Mechanics (NESM): Toolbox for predicting Navy shock and damage

Here, the US Navy is developing a new suite of computational mechanics tools (Navy Enhanced Sierra Mechanics) for the prediction of ship response, damage, and shock environments transmitted to vital systems during threat weapon encounters. NESM includes fully coupled Euler-Lagrange solvers tailored to ship shock/damage predictions. NESM is optimized to support high-performance computing architectures, providing the physics-based ship response/threat weapon damage predictions needed to support the design and assessment of highly survivable ships. NESM is being employed to support current Navy ship design and acquisition programs while being further developed for future Navy fleet needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Cloud-Based Model Calibration Using OpenStudio: Preprint

OpenStudio is a free, open source Software Development Kit (SDK) and application suite for performing building energy modeling and analysis. The OpenStudio Parametric Analysis Tool has been extended to allow cloud-based simulation of multiple OpenStudio models parametrically related to a baseline model. This paper describes the new cloud-based simulation functionality and presents a model cali-bration case study. Calibration is initiated by entering actual monthly utility bill data into the baseline model. Multiple parameters are then varied over multiple iterations to reduce the difference between actual energy consumption and model simulation results, as calculated and visualized by billing period and by fuel type. Simulations are per-formed in parallel using the Amazon Elastic Cloud service. This paper highlights model parameterizations (measures) used for calibration, but the same multi-nodal computing architecture is available for other purposes, for example, recommending combinations of retrofit energy saving measures using the calibrated model as the new baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

2014 HPC Annual Report

Our commitment is to support you through delivery of an IT environment that provides mission value by transforming the way you use, protect, and access information. We approach this through technical innovation, risk management, and relationships with our workforce, Laboratories leadership, and policy makers nationwide. This second edition of our HPC Annual Report continues our commitment to communicate the details and impact of Sandia’s large-scale computing resources that support the programs associated with our diverse mission areas. A key tenet to our approach is to work with our mission partners to understand and anticipate their requirements and formulate an investment strategy that is aligned with those Laboratories priorities. In doing this, our investments include not only expanding the resources available for scientific computing and modeling and simulation, but also acquiring large-scale systems for data analytics, cloud computing, and Emulytics. We are also investigating new computer architectures in our advanced systems test bed to guide future platform designs and prepare for changes in our code development models. Our initial investments in large-scale institutional platforms that are optimized for Informatics and Emulytics work are serving a diverse customer base. We anticipate continued growth and expansion of these resources in the coming years as the use of these analytic techniques expands across our mission space. If your program could benefit from an investment in innovative systems, please work through your Program Management Unit ’s Mission Computing Council representatives to engage our teams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Report of the HPC Correctness Summit, January 25-26, 2017, Washington, DC

Technologies for verification and debugging have made significant strides in the context of general systems software. An investment in such technologies to make them applicable for High Performance Computing (HPC) could lead to substantial improvements in the productivity and sustainability of HPC software development. Such improvements will be essential to fully exploit new exascale computer architectures. Without such investment, there is the possibility of a substantial crisis in our ability to advance the field of HPC, as the complexity of our architectures, algorithms, and applications is moving beyond the ability of our developers. As HPC is of strategic importance to our nation, forming the bedrock of its scientific and technological capabilities, such investment is highly warranted.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The RAJA Portability Layer: Overview and Status

As computer architectures become increasingly complex and diverse, application developers face difficult challenges to achieve high performance while maintaining code portability. The problem is especially acute for large ASC multiphysics codes. Efficient parallel execution often requires tuning algorithms and data access to match processor and memory system constraints. Changing compiler directives and parallel programming model constructs on thousands of individual loops in a large code is disruptive and unwieldy. RAJA is a programming approach that we have been developing at Lawrence Livermore National Laboratory to encapsulate platform-specific concerns, related to both hardware and parallel programming models. The RAJA abstraction layer simplifies porting C/C++ codes to various programming models and architectures by reducing effort and developer disruption. In this report, we motivate and describe key aspects of RAJA. We also present a preliminary assessment of RAJA based on exploration in three ASC hydrodynamics codes at LLNL, which was one part of a three-part ASC Level 2 milestone, completed in September 2014.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Review of Literature Related to Nuclear Data Mini-Apps

his document reviews nuclear data mini-apps prevalent in the published scientific literature. It is intended to identify algorithmic and computer architecture considerations of relevance to optimizing GIDI/MCGIDI for GPU-based architectures. While some of the works considered here focus on CPU-based architectures, the performance-limiting factors and mitigating strategies identified often extend to GPU-based architectures, particularly in the context of latency-bound performance. A brief overview of the cross section (XS) lookup process is given in Section 1 and a detailed analysis of nuclear data mini-apps follows in Section 2. Concluding remarks are offered in Section 3.

42 ENGINEERING↗
DOE OSTI2015

Progress in fast, accurate multi-scale climate simulations

We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

High Performance Computing - Power Application Programming Interface Specification

Achieving practical exascale supercomputing will require massive increases in energy efficiency. The bulk of this improvement will likely be derived from hardware advances such as improved semiconductor device technologies and tighter integration, hopefully resulting in more energy efficient computer architectures. Still, software will have an important role to play. With every generation of new hardware, more power measurement and control capabilities are exposed. Many of these features require software involvement to maximize feature benefits. This trend will allow algorithm designers to add power and energy efficiency to their optimization criteria. Similarly, at the system level, opportunities now exist for energy-aware scheduling to meet external utility constraints such as time of day cost charging and power ramp rate limitations. Finally, future architectures might not be able to operate all components at full capability for a range of reasons including temperature considerations or power delivery limitations. Software will need to make appropriate choices about how to allocate the available power budget given many, sometimes conflicting considerations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ERF: Energy Research and Forecasting

The Energy Research and Forecasting (ERF) code is a new model that simulates the mesoscale and microscale dynamics of the atmosphere using the latest high-performance computing architectures. It employs hierarchical parallelism using an MPI+X model, where X may be OpenMP on multicore CPU-only systems, or CUDA, HIP, or SYCL on GPU-accelerated systems. ERF is built on AMReX (Zhang et al., 2019, 2021), a block-structured adaptive mesh refinement (AMR) software framework that provides the underlying performance-portable software infrastructure for block-structured mesh operations. The "energy" aspect of ERF indicates that the software has been developed with renewable energy applications in mind. In addition to being a numerical weather prediction model, ERF is designed to provide a flexible computational framework for the exploration and investigation of different physics parameterizations and numerical strategies, and to characterize the flow field that impacts the ability of wind turbines to extract wind energy. The ERF development is part of a broader effort led by the US Department of Energy's Wind Energy Technologies Office.

17 WIND ENERGY↗
DOE OSTI2025

AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations

We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.

17 WIND ENERGY↗
DOE OSTI2022

Developing an ELM Ecosystem Dynamics Model on GPU with OpenACC

Porting a complex scientific code, such as the E3SM land model (ELM), onto a new computing architecture is challenging. The paper presents design strategies and technical approaches to develop an ELM ecosystem dynamics model with compiler directives (OpenACC) on NVIDIA GPUs. The code has been refactored with advanced OpenACC features (such as deepcopy and routine directives) to reduce memory consumption and to increase the levels of parallelism through parallel loop reconstruction and new data structures. As a result, the optimized parallel implementation achieved more than a 140-time speedup (50 ms vs 7600 ms), compared to a naive implementation that uses OpenACC routine directive and parallelizes the code across existing loops on a single NVIDIA V100. On a fully loaded computing node with 44 CPUs and 6 GPUs, the code achieved over a 3.0-times speedup, compared to the original code on the CPU. Furthermore, the memory footprint of the optimized parallel implementation is 300 MB, which is around 15% of the 2.15 GB of memory consumed by a naive implementation. This study is the first effort to develop the ELM component on GPUs efficiently to support ultra-high-resolution land simulations at continental scales.

Schwartz, Peter↗
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗