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DOE OSTI2021

Non-Proprietary Companion to the Q1 CY2021 PathForward Final Assessment (WBS 2.4.1, Milestone PM-HI-1040)

The PathForward element of the Exascale Computing Project (ECP) Hardware and Integration (HI) focus area has met its goals of preparing the US industry for exascale system procurements and generally improving US competitiveness in the worldwide computing market. This report is a companion to the deliverable for the ECP milestone PM-HI-1040, Assess PathForward Impact Against Exascale Hardware Challenges, which gives a summary of the final status of each PathForward project, describes progress achieved against PathForward contract milestones, and includes a final assessment of each vendor’s progress on key exascale challenges. This companion document details the results of the PathForward research to the extent possible without disclosing proprietary information and the impact on products and US exascale systems. In addition, it captures lessons learned in order to inform future projects in general and in high-performance computing in particular.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

PICSAR-QED: a Monte Carlo module to simulate strong-field quantum electrodynamics in particle-in-cell codes for exascale architectures

Abstract Physical scenarios where the electromagnetic fields are so strong that quantum electrodynamics (QED) plays a substantial role are one of the frontiers of contemporary plasma physics research. Investigating those scenarios requires state-of-the-art particle-in-cell (PIC) codes able to run on top high-performance computing (HPC) machines and, at the same time, able to simulate strong-field QED processes. This work presents the PICSAR-QED library, an open-source, portable implementation of a Monte Carlo module designed to provide modern PIC codes with the capability to simulate such processes, and optimized for HPC. Detailed tests and benchmarks are carried out to validate the physical models in PICSAR-QED, to study how numerical parameters affect such models, and to demonstrate its capability to run on different architectures (CPUs and GPUs). Its integration with WarpX, a state-of-the-art PIC code designed to deliver scalable performance on upcoming exascale supercomputers, is also discussed and validated against results from the existing literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Dilute Combustion Control Using Spiking Neural Networks

Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity. Readily available data from in-cylinder pressure was used to train a spiking neural network (SNN) which learns the optimal way to manage fuel injection in order to reduce CCV while maintaining acceptable levels of fuel consumption. SNNs are particularly well suited for powertrain control applications due to their ability to be deployed on FPGA-based neuromorphic hardware which are small, inexpensive, and have a low power demand. The high-performance computing (HPC) resources of Oak Ridge National Laboratory were used to run an evolutionary-based training approach for choosing the best SNN configuration that minimizes the size of the network while achieving the desired goal. The neuromorphic hardware with the optimized SNN deployed was connected to the rapid prototyping engine control system for real-time control implementation and tested on a single cylinder version of a GM LNF 4-cylinder engine. The results show a significant reduction of CCV with a small percentage of additional fuel used to stabilize the charge.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Application Results on Early Exascale Hardware

This Exascale Computing Project (ECP) milestone report summarizes the status of 27 of the 31 ECP Applications Development (AD) subprojects at the end of FY21. In November and December of 2021, a comprehensive assessment of AD projects was conducted by the ECP leadership along with external subject matter experts (SMEs). (NNSA application projects are reviewed separately using the ASC milestone process.) The AD review committee—consisting of the AD lead, AD deputy, Level 3 (L3), and at least one external project SME—was tasked with evaluating each project’s progress relative to ECP project goals specified in the FY21 timeline. Key areas of focus were code maturity and performance on pre-exascale systems, an in-depth analysis of final key performance parameter (KPP) verification contracts, and future R&D priorities in the final year of ECP and beyond. As such, this report contains not only an accurate snapshot of each subproject’s current status but also represents a broad account of successes and challenges in porting large scientific applications to DOE’s next-generation high-performance computing architectures – the Frontier and Aurora systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Report on Simulation-Driven Reliability and Failure Analysis of Large-Scale Storage Systems

High-performance computing (HPC) storage systems provide data availability and reliability using various hardware and software fault tolerance techniques. Usually, reliability and availability are calculated at the subsystem or component level using limited metrics such as, mean time to failure (MTTF) or mean time to data loss (MTTDL). This often means settling on simple and disconnected failure models (such as exponential failure rate) to achieve tractable and close-formed solutions. However, such models have been shown to be insufficient in assessing end-to-end storage system reliability and availability. We propose a generic simulation framework aimed at analyzing the reliability and availability of storage systems at scale, and investigating what-if scenarios. The framework is designed for an end-to-end storage system, accommodating the various components and subsystems, their interconnections, failure patterns and propagation, and performs dependency analysis to capture a wide-range of failure cases. We evaluate the framework against a large-scale storage system that is in production and analyze its failure projections toward and beyond the end of lifecycle. We also examine the potential operational impact by studying how different types of components affect the overall system reliability and availability, and present the preliminary results

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as p2r, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these miniapps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Navigating Exascale Operational Data Analytics: From Inundation to Insight

In this paper, we address the challenges in achieving sustainable data-driven efficiency by providing a detailed exploration of the end-to-end operational data analytics (ODA) framework that evolved through two generations of supercomputer systems at the Oak Ridge Leadership Computing Facility (OLCF). This framework addresses large data streams ingested from heavily instrumented HPC environment that accumulates multi-terabytes per day. We outline the multifaceted data life cycle across HPC procurement, operations, and research & development, identifying key obstacles and design decisions that shape effective strategies in building and supporting data pipelines end-to-end. By sharing key insights and lessons learned from our experience, we offer recommendations for the HPC community on enabling sustainable operational data analytics and beyond. Our contributions aim to bridge the gap between potential and real benefits of operational data, guiding future efforts towards integrated and sustainable operational intelligence in high-performance computing environments.

Shin, Woong↗
DOE OSTI2023

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An empirical study of I/O separation for burst buffers in HPC systems

To meet the exascale I/O requirements for the High-Performance Computing (HPC), a new I/O subsystem, Burst Buffer, based on solid state drives (SSD), has been developed. However, the diverse HPC workloads and the bursty I/O pattern cause severe data fragmentation that requires costly garbage collection (GC) and increases the number of bytes written to the SSD. To address this data fragmentation challenge, a new multi-stream feature has been developed for SSDs. In this work, we develop an I/O Separation scheme called BIOS to leverage this multi-stream feature to group the I/O streams based on the user IDs. We propose a stream-aware scheduling policy based on burst buffer pools in the workload manager, and integrate the BIOS with the workload manager to optimize the I/O separation scheme in burst buffer. We evaluate the proposed framework with a burst buffer I/O traces from Cori Supercomputer including a diverse set of applications. Experimental results show that the BIOS could improve the performance by 1.44x on average and reduce the Write Amplification Factor (WAF) by up to 1.20x. Finally, these demonstrate the potential benefits of the I/O separation scheme for solid state storage systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as \ptor, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these mini-apps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

Atif, Mohammad [Brookhaven] (ORCID:000000026889770↗
DOE OSTI2025

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2016

An asynchronous traversal engine for graph-based rich metadata management

Rich metadata in high-performance computing (HPC) systems contains extended information about users, jobs, data files, and their relationships. Property graphs are a promising data model to represent heterogeneous rich metadata flexibly. Specifically, a property graph can use vertices to represent different entities and edges to record the relationships between vertices with unique annotations. The high-volume HPC use case, with millions of entities and relationships, naturally requires an out-of-core distributed property graph database, which must support live updates (to ingest production information in real time), low-latency point queries (for frequent metadata operations such as permission checking), and large-scale traversals (for provenance data mining). Among these needs, large-scale property graph traversals are particularly challenging for distributed graph storage systems. Most existing graph systems implement a "level synchronous" breadth-first search algorithm that relies on global synchronization in each traversal step. This performs well in many problem domains; but a rich metadata management system is characterized by imbalanced graphs, long traversal lengths, and concurrent workloads, each of which has the potential to introduce or exacerbate stragglers (i.e., abnormally slow steps or servers in a graph traversal) that lead to low overall throughput for synchronous traversal algorithms. Previous research indicated that the straggler problem can be mitigated by using asynchronous traversal algorithms, and many graph-processing frameworks have successfully demonstrated this approach. Such systems require the graph to be loaded into a separate batch-processing framework instead of being iteratively accessed, however. In this work, we investigate a general asynchronous graph traversal engine that can operate atop a rich metadata graph in its native format. We outline a traversal-aware query language and key optimizations (traversal-affiliate caching and execution merging) necessary for efficient performance. We further explore the effect of different graph partitioning strategies on the traversal performance for both synchronous and asynchronous traversal engines. Our experiments show that the asynchronous graph traversal engine is more efficient than its synchronous counterpart in the case of HPC rich metadata processing, where more servers are involved and larger traversals are needed. Furthermore, the asynchronous traversal engine is more adaptive to different graph partitioning strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Toward improving drought monitoring using the remotely sensed soil moisture assimilation: A parallel particle filtering framework

Drought is the costliest hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring skills, this study presents a land data assimilation system by merging the remotely sensed surface soil moisture with the use of recently developed particle Markov chain Monte Carlo (PMCMC) method. To cope with the computational complexity, a modular parallel particle filtering framework (PPFF) is developed which allows a large ensemble size in PMCMC applications. The implementation of the proposed system is demonstrated with the 2012 summer flash drought case study over the Contiguous United States (CONUS). Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves the soil moisture predictions and the drought monitoring skills. Compared with the U.S. Drought Monitoring (USDM), the land data assimilation can better capture the drought onset on May 2012 and the drought severity in June and July 2012. Finally, this study recommends that the proposed land data assimilation system under a high-performance computing (HPC) infrastructure can better facilitate the drought preparation and response actions.

42 ENGINEERING↗
DOE OSTI2017

Thermosyphon Cooler Hybrid System for Water Savings in an Energy-Efficient HPC Data Center: Modeling and Installation: Preprint

The Thermosyphon Cooler Hybrid System (TCHS) integrates the control of a dry heat rejection device, the thermosyphon cooler (TSC), with an open cooling tower. A combination of equipment and controls, this new heat rejection system embraces the 'smart use of water,' using evaporative cooling when it is most advantageous and then saving water and modulating toward increased dry sensible cooling as system operations and ambient weather conditions permit. Innovative fan control strategies ensure the most economical balance between water savings and parasitic fan energy. The unique low-pressure-drop design of the TSC allows water to be cooled directly by the TSC evaporator without risk of bursting tubes in subfreezing ambient conditions. Johnson Controls partnered with the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories to deploy the TSC as a test bed at NREL's high-performance computing (HPC) data center in the first half of 2016. Located in NREL's Energy Systems Integration Facility (ESIF), this HPC data center has achieved an annualized average power usage effectiveness rating of 1.06 or better since 2012. Warm-water liquid cooling is used to capture heat generated by computer systems direct to water; that waste heat is either reused as the primary heat source in the ESIF building or rejected using evaporative cooling. This data center is the single largest source of water and power demand on the NREL campus, using about 7,600 m3 (2.0 million gal) of water during the past year with an hourly average IT load of nearly 1 MW (3.4 million Btu/h) -- so dramatically reducing water use while continuing efficient data center operations is of significant interest. Because Sandia's climate is similar to NREL's, this new heat rejection system being deployed at NREL has gained interest at Sandia. Sandia's data centers utilize an hourly average of 8.5 MW (29 million Btu/h) and are also one of the largest consumers of water on Sandia's site. In addition to describing the installation of the TSC and its integration into the ESIF, this paper focuses on the full heat rejection system simulation program used for hourly analysis of the energy and water consumption of the complete system under varying operating scenarios. A follow-up paper will detail the test results. The evaluation of the TSC's performance at NREL will also determine a path forward at Sandia for possible deployment in a large-scale system not only for data center use but also possibly site wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Energy dataset of Frontier supercomputer for waste heat recovery

The Hewlett Packard Enterprise–Cray EX Frontier is the world’s first and fastest exascale supercomputer, hosted at the Oak Ridge Leadership Computing Facility in Tennessee, United States. Frontier is a significant electricity consumer, drawing 8–30 MW; this massive energy demand produces significant waste heat, requiring extensive cooling measures. Although harnessing this waste heat for campus heating is a sustainability goal at Oak Ridge National Laboratory (ORNL), the 30 °C–38 °C waste heat temperature poses compatibility issues with standard HVAC systems. Heat pump systems, prevalent in residential settings and some industries, can efficiently upgrade low-quality heat to usable energy for buildings. Thus, heat pump technology powered by renewable electricity offers an efficient, cost-effective solution for substantial waste heat recovery. However, a major challenge is the absence of benchmark data on high-performance computing (HPC) heat generation and waste heat profiles. This paper reports power demand and waste heat measurements from an ORNL HPC data centre, aiming to guide future research on optimizing waste heat recovery in large-scale data centres, especially those of HPC calibre.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

HPC4Mfg with Sepion

We employed ab-initio simulations and quantum chemical calculations to develop a computational framework for simulating the microscopic structure and mechanical properties of novel polymer membranes used in lithium sulfur batteries. To meet industry targets, next generation batteries with high specific energy (Wh/kg) are essential. Efforts to commercialize light-weight, energy-dense lithium-sulfur secondary batteries (2510 Wh/kg) have been stalled by ongoing problems with the battery’s separator membrane, which should prevent cross-over of active material from cathode to anode that, if unchecked, limits cycle-life. However, Sepion Technologies’ polymer membranes yield long-lasting lithium-sulfur cells. Advancing to 10 Ah battery prototypes, Sepion faces challenges in membrane manufacturing related to polymer processing and the molecular basis for membrane performance and durability. High performance computing offers critical new insight into these phenomena, which in turn will accelerate product entry into the market.

25 ENERGY STORAGE↗
DOE OSTI2017

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.

Lietz, Justin↗
DOE OSTI2021

Toward the predictive discovery of ambipolarly dopable ultra-wide-band-gap semiconductors: The case of rutile GeO 2

Ultrawide-band-gap (UWBG) semiconductors are promising for fast, compact, and energy-efficient power-electronics devices. Their wider band gaps result in higher breakdown electric fields that enable high-power switching with a lower energy loss. Yet, the leading UWBG semiconductors suffer from intrinsic materials' limitations with regard to their doping asymmetry that impedes their adoption in CMOS technology. Improvements in the ambipolar doping of UWBG materials will enable a wider range of applications in power electronics as well as deep-UV optoelectronics. These advances can be accomplished through theoretical insights on the limitations of current UWBG materials coupled with the computational prediction and experimental demonstration of alternative UWBG semiconductor materials with improved doping and transport properties. As an example, we discuss the case of rutile GeO2 (r-GeO 2 ), a water-insoluble GeO 2 polytype, which is theoretically predicted to combine an ultra-wide gap with ambipolar dopability, high carrier mobilities, and a higher thermal conductivity than β-Ga 2 O 3 . The subsequent realization of single-crystalline r-GeO 2 thin films by molecular beam epitaxy provides the opportunity to realize r-GeO 2 for electronic applications. Future efforts toward the predictive discovery and design of new UWBG semiconductors include advances in first-principles theory and high-performance computing software, as well as the demonstration of controlled doping in high-quality thin films with lower dislocation densities and optimized film properties.

36 MATERIALS SCIENCE↗