Novel large scale simulation process to support dot’s cafe modeling system
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Aces4: A Platform for Computational Chemistry Calculations with Extremely Large Block-Sparse Arrays
Aces4 is a parallel programming platform comprising a DSL for Computational Chemistry and its runtime system. It offers a convenient way to express parallelism together with extensive support for extremely large, possibly sparse, distributed arrays. It aids scientists in the creation of performant, scalable, massively parallel programs that can effectively take advantage of leadership class computing systems to address important scientific questions. Aces4 has enabled the development and implementation of new methods in electronic structure theory which are breaking new ground in their ability to perform highly accurate calculations on ever larger molecular systems. In this paper the design of Aces4, which is based on the the Super Instruction Architecture approach, is described. Experimental scaling results for Molecular Cluster Perturbation Theory, a new method enabled by Aces4, and CCSD, a widely used computational chemistry method are given.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2021: Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.
97 MATHEMATICS AND COMPUTING↗
Concurrent Probabilistic Simulation of High Temperature Composite Structural Response
A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.
Abdi, Frank↗
Classification of Wildfires from MODIS Data Using Neural Networks
Wildfires are destructive to both life and property, which necessitates an approach to quickly and autonomously detect these events from orbital observatories. This talk will introduce a neural network based approach for classifying wildfires in MODIS multispectral data, and will show how it could be applied to a constellation of low-cost CubeSats. The approach combines training a deep neural network on the ground using high performance consumer GPUs, with a highly optimized inference system running on a flight-proven embedded processor. Normally neural networks execute on hardware orders of magnitude more powerful than anything found in a space-based computer, therefore the inference system is designed to be performance even on the most modest of platforms. This implementation is able to be significantly more accurate than previous neural network implementations, while also approaching the accuracy of the state-of-the-art MODFIRE data products.
Artificial Intelligence↗
Hybrid Cooling and Water Treatment for Resilient, Water-Self-Sufficient Data Centers
The continued growth of data center infrastructure is intensifying demand for freshwater resources, particularly in water-stressed regions, and is increasingly limiting sustainable capacity expansion. This work investigates a conceptual hybrid system that integrates freeze desalination with ultrasonic-assisted ice separation to enable on-site production of pure water from diverse sources, including seawater, brackish groundwater, and reclaimed industrial or agricultural wastewater. Simultaneously, the system produces low-temperature cooling streams that enhance heat removal in high power density computing environments. A process-level thermodynamic analysis is performed across a range of boundary and operating conditions, including variations in feed concentration, freezing temperature, and mass flow rate. The results are presented as performance curves relating feedwater concentration and target purified water output to the corresponding intake flow requirements, enabling estimation of source water demand per unit of Information Technology Equipment (ITE) energy consumption (kWh) across varying data center scales and operational scenarios. The corresponding electrical energy consumption for integrated cooling and water treatment is also evaluated as a function of system operating parameters and target water production levels. These results provide a basis for evaluating system feasibility across different conditions and for identifying parameter ranges in which integrated water treatment and cooling improve resource efficiency, thermal performance, and operational flexibility in data centers.
Elhefny, Aly [ORNL] (ORCID:0000000284907923)↗
Numerical Methodology for Optimization of Compression-Ignited Engines Considering Combustion Noise Control
It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOx) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes, and in-cylinder swirl. An objective merit function (MF) based on the emissions, ISFC, and combustion noise was constructed to quantify the strength of the engine designs and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOx emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. In conclusion, this computational study is a first of its kind (to the best of the authors’ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.
42 ENGINEERING↗
Computer sciences
The Computer Science Program provides advanced concepts, techniques, system architectures, algorithms, and software for both space and aeronautics information sciences and computer systems. The overall goal is to provide the technical foundation within NASA for the advancement of computing technology in aerospace applications. The research program is improving the state of knowledge of fundamental aerospace computing principles and advancing computing technology in space applications such as software engineering and information extraction from data collected by scientific instruments in space. The program includes the development of special algorithms and techniques to exploit the computing power provided by high performance parallel processors and special purpose architectures. Research is being conducted in the fundamentals of data base logic and improvement techniques for producing reliable computing systems.
Smith, Paul H.↗
Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)
The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.
97 MATHEMATICS AND COMPUTING↗
Radar Scattering and Antenna Modeling on Scalable High Performance Computers
The application of advanced computer architecture and software to a broad range of electromagnetic probelm has allowed more accuracte simulations of electrically larger and more complex components and systems that had been previously avilable.
Radar Scattering Antenna Modeling↗
Extreme-scale Algorithms and Solver Resilience
A widening gap exists between the peak performance of high-performance computers and the performance achieved by complex applications running on these platforms. Over the next decade, extreme-scale systems will present major new challenges to algorithm development that could amplify this mismatch in such a way that it prevents the productive use of future DOE Leadership computers due to the following; Extreme levels of parallelism due to multicore processors; An increase in system fault rates requiring algorithms to be resilient beyond just checkpoint/restart; Complex memory hierarchies and costly data movement in both energy and performance; Heterogeneous system architectures (mixing CPUs, GPUs, etc.); and Conflicting goals of performance, resilience, and power requirements.
97 MATHEMATICS AND COMPUTING↗
ATHENA: Analytical Tool for Heterogeneous Neuromorphic Architectures
The ASC program seeks to use machine learning to improve efficiencies in its stockpile stewardship mission. Moreover, there is a growing market for technologies dedicated to accelerating AI workloads. Many of these emerging architectures promise to provide savings in energy efficiency, area, and latency when compared to traditional CPUs for these types of applications — neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. If designed into a heterogeneous system with other accelerators and conventional compute nodes, these technologies have the potential to augment the capabilities of traditional High Performance Computing (HPC) platforms [5]. This expanded computation space requires not only a new approach to physics simulation, but the ability to evaluate and analyze next-generation architectures specialized for AI/ML workloads in both traditional HPC and embedded ND applications. Developing this capability will enable ASC to understand how this hardware performs in both HPC and ND environments, improve our ability to port our applications, guide the development of computing hardware, and inform vendor interactions, leading them toward solutions that address ASC’s unique requirements.
97 MATHEMATICS AND COMPUTING↗
Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies
Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.
Mathematics and Computing↗
Compact Spare-Row Decoder For Computer Memory
Spare-row memory-address-decoder circuit commanded to address ninth row in computer memory instead of addressing one of eight others it would address normally. Variants used to construct small, highly reliable computers. Spare-row decoder offers advantages of compactness, efficiency, and performance. Requires only 12.5 percent memory overhead. System equipped with spare-row decoder requires less glue logic and exhibits greater through-put. Applications include computers in Hitchhiker Central Unit embedded computer on Cassini spacecraft. Concept of circuit applicable to most flight computer systems.
Katz, Richard B.↗
Object-oriented Approach to High-level Network Monitoring and Management
An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.
Mukkamala, Ravi↗
High Performance, Dependable Multiprocessor
With the ever increasing demand for higher bandwidth and processing capacity of today's space exploration, space science, and defense missions, the ability to efficiently apply commercial-off-the-shelf (COTS) processors for on-board computing is now a critical need. In response to this need, NASA's New Millennium Program office has commissioned the development of Dependable Multiprocessor (DM) technology for use in payload and robotic missions. The Dependable Multiprocessor technology is a COTS-based, power efficient, high performance, highly dependable, fault tolerant cluster computer. To date, Honeywell has successfully demonstrated a TRL4 prototype of the Dependable Multiprocessor [I], and is now working on the development of a TRLS prototype. For the present effort Honeywell has teamed up with the University of Florida's High-performance Computing and Simulation (HCS) Lab, and together the team has demonstrated major elements of the Dependable Multiprocessor TRLS system.
fault tolerant↗
Advancement of Extreme Environment Additively Manufactured Alloys for Next Generation Space Propulsion Applications
The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into propulsion development and flight applications. NASA matured commonly used aerospace alloys from various alloy families (Nickel, Copper, Stainless and Steel, Aluminum, and Titanium-based) through detailed AM process and heat treatment characterization, in addition to mechanical and thermophysical testing. While these alloys are actively used in many propulsion applications, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are liquid rocket engines; advanced propulsion systems; and in-space propulsion with high heat fluxes, high pressure, and/or that use propellants that can degrade alloys (e.g., hydrogen). This paper highlights the characterization and physical properties of the more common AM alloys using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes. Additionally, this paper discusses some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop42, GRCop-84, NASA HR-1, GRX-810, and C-103. The results from these processes demonstrated that AM could enable rapid development and ongoing efforts for optimized alloys using ICME, yielding higher performances. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, detailed microstructure characterization, and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. A background and overview of these novel AM-enabled alloys and AM processing developments including metallurgical and mechanical property studies is presented here. The latest advancement in the parallel component development and hot-fire testing and future developments for these alloys is also discussed.
Additive Manufacturing↗
Advancement of Extreme Environment Additively Manufactured Alloys for Next Generation Space Propulsion Applications
The National Aeronautics and Space Administration (NASA) has been involved in the development and maturation of metal additive manufacturing (AM) for space applications since the late 2000’s. Several efforts focused on the understanding of AM processes through material characterization and testing, standards development, component fabrication, and infusion into propulsion development and flight applications. NASA matured commonly used aerospace alloys from various alloy families (Nickel, Copper, Stainless and Steel, Aluminum, and Titanium-based) through detailed AM process and heat treatment characterization, in addition to mechanical and thermophysical testing. While these alloys are actively used in many propulsion applications, there is a need for ongoing AM optimized alloys using integrated computational materials engineering (ICME) and process development for high performance applications. The applications targeted are liquid rocket engines; advanced propulsion systems; and in-space propulsion with high heat fluxes, high pressure, and/or that use propellants that can degrade alloys (e.g., hydrogen). This paper highlights the characterization and physical properties of the more common AM alloys using laser powder bed fusion (L-PBF) and laser powder directed energy deposition (LP-DED) processes. Additionally, this paper discusses some of the ongoing novel alloy development and maturation using AM for use in these harsh environments, such as GRCop-42, GRCop-84, NASA HR-1, GRX-810, and C-103. The results from these processes demonstrated that AM could enable rapid development and ongoing efforts for optimized alloys using ICME, yielding higher performances. These alloys have undergone modeling, fundamental metallurgical evaluations, heat treatment studies, detailed microstructure characterization, and mechanical testing campaigns. This, combined with direct application-specific component fabrication and hot-fire testing, enabled the increase of the Technology Readiness Level (TRL) through high duty-cycle testing. A background and overview of these novel AM-enabled alloys and AM processing developments including metallurgical and mechanical property studies is presented here. The latest advancement in the parallel component development and hot-fire testing and future developments for these alloys is also discussed.
Additive Manufacturing↗