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DOE OSTI2020

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; 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) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% 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 (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

Methods, apparatus, and products are disclosed for providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: receiving a network packet in a compute node, the network packet specifying a destination compute node; selecting, in dependence upon the destination compute node, at least one of the links for the compute node along which to forward the network packet toward the destination compute node; and forwarding the network packet along the selected link to the adjacent compute node connected to the compute node through the selected link.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

Methods, apparatus, and products are disclosed for providing nearest neighbor point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: identifying each link in the global combining network for each compute node of the operational group; designating one of a plurality of point-to-point class routing identifiers for each link such that no compute node in the operational group is connected to two adjacent compute nodes in the operational group with links designated for the same class routing identifiers; and configuring each compute node of the operational group for point-to-point communications with each adjacent compute node in the global combining network through the link between that compute node and that adjacent compute node using that link's designated class routing identifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer

Methods, apparatus, and products are disclosed for providing full point-to-point communications among compute nodes of an operational group in a global combining network of a parallel computer, each compute node connected to each adjacent compute node in the global combining network through a link, that include: receiving a network packet in a compute node, the network packet specifying a destination compute node; selecting, in dependence upon the destination compute node, at least one of the links for the compute node along which to forward the network packet toward the destination compute node; and forwarding the network packet along the selected link to the adjacent compute node connected to the compute node through the selected link.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.

Accelerated Data Analytics and↗
DOE OSTI2022

State preparation and evolution in quantum computing: a perspective from Hamiltonian moments

Quantum algorithms on the noisy intermediate-scale quantum (NISQ) devices are expected to simulate quan- tum systems that are classically intractable to demonstrate quantum advantages. However, the non-negligible gate error on the NISQ devices impedes the conventional quantum algorithms to be implemented. Practical strategies usually exploit hybrid quantum-classical quantum algorithms to demonstrate potentially useful ap- plications of quantum computing in the NISQ era. Among the numerous hybrid quantum-classical algorithms, recent efforts highlight the development of quantum algorithms based upon quantum computed Hamiltonian moments, ?f|Hˆn|f? (n = 1, 2, · · · ), with respect to quantum state |f?. In this tutorial, we will give a brief review of these quantum algorithms with focuses on the typical ways of computing Hamiltonian moments using quantum hardware and improving the accuracy of the estimated state energies based on the quantum computed moments. Furthermore, we will present a tutorial to show how we can measure and compute the Hamiltonian moments of a four-site Heisenberg model, and compute the energy and magnetization of the model utilizing the imaginary time evolution in the real IBM-Q NISQ hardware environment. Along this line, we will further discuss some practical issues associated with these algorithms. We will conclude this tutorial review by overviewing some possible developments and applications in this direction in the near future.

Aulicino, Joseph C.↗
DOE OSTI2010

Low latency, high bandwidth data communications between compute nodes in a parallel computer

Methods, parallel computers, and computer program products are disclosed for low latency, high bandwidth data communications between compute nodes in a parallel computer. Embodiments include receiving, by an origin direct memory access (`DMA`) engine of an origin compute node, data for transfer to a target compute node; sending, by the origin DMA engine of the origin compute node to a target DMA engine on the target compute node, a request to send (`RTS`) message; transferring, by the origin DMA engine, a predetermined portion of the data to the target compute node using memory FIFO operation; determining, by the origin DMA engine whether an acknowledgement of the RTS message has been received from the target DMA engine; if the an acknowledgement of the RTS message has not been received, transferring, by the origin DMA engine, another predetermined portion of the data to the target compute node using a memory FIFO operation; and if the acknowledgement of the RTS message has been received by the origin DMA engine, transferring, by the origin DMA engine, any remaining portion of the data to the target compute node using a direct put operation.

Archer, Charles J↗
DOE OSTI2012

Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach

Abstract For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only ~2.5 hours (42 compute nodes) a 40x speed-up. Tools developed for this parallel execution are discussed.

58 GEOSCIENCES↗
DOE OSTI2016

High Performance Computing Facility Operational Assessment 2015: Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable systems; creating innovative solutions for high-performance computing (HPC) needs; and managing risks, safety, and security aspects associated with operating one of the most powerful computers in the world. The results can be seen in the cutting-edge science delivered by users and the praise from the research community. Calendar year (CY) 2015 was filled with outstanding operational results and accomplishments: a very high rating from users on overall satisfaction that ties the highest-ever mark set in CY 2014; the greatest number of core-hours delivered to research projects; the largest percentage of capability usage since the OLCF began tracking the metric in 2009; and success in delivering on the allocation of 60, 30, and 10% of core hours offered for the INCITE (Innovative and Novel Computational Impact on Theory and Experiment), ALCC (Advanced Scientific Computing Research Leadership Computing Challenge), and Director’s Discretionary programs, respectively. These accomplishments, coupled with the extremely high utilization rate, represent the fulfillment of the promise of Titan: maximum use by maximum-size simulations. The impact of all of these successes and more is reflected in the accomplishments of OLCF users, with publications this year in notable journals Nature, Nature Materials, Nature Chemistry, Nature Physics, Nature Climate Change, ACS Nano, Journal of the American Chemical Society, and Physical Review Letters, as well as many others. The achievements included in the 2015 OLCF Operational Assessment Report reflect first-ever or largest simulations in their communities; for example Titan enabled engineers in Los Angeles and the surrounding region to design and begin building improved critical infrastructure by enabling the highest-resolution Cybershake map for Southern California to date. The Titan system provides the largest extant heterogeneous architecture for computing and computational science. Usage is high, delivering on the promise of a system well-suited for capability simulations for science. This success is due in part to innovations in tracking and reporting the activity on the compute nodes, and using this information to further enable and optimize applications, extending and balancing workload across the entire node. The OLCF continues to invest in innovative processes, tools, and resources necessary to meet continuing user demand. The facility’s leadership in data analysis and workflows was featured at the Department of Energy (DOE) booth at SC15, for the second year in a row, highlighting work with researchers from the National Library of Medicine coupled with unique computational and data resources serving experimental and observational data across facilities. Effective operations of the OLCF play a key role in the scientific missions and accomplishments of its users. Building on the exemplary year of 2014, as shown by the 2014 Operational Assessment Report (OAR) review committee response in Appendix A, this OAR delineates the policies, procedures, and innovations implemented by the OLCF to continue delivering a multi-petaflop resource for cutting-edge research. This report covers CY 2015, which, unless otherwise specified, denotes January 1, 2015, through December 31, 2015.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

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↗
DOE OSTI2014

Synchronizing compute node time bases in a parallel computer

Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Synchronizing compute node time bases in a parallel computer

Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

We introduce atomate, an open-source Python framework for computational materials science simula-tion, analysis, and design with an emphasis on automation and extensibility. Built on top of open sourcePython packages already in use by the materials community such as pymatgen, FireWorks, and custodian,atomate provides well-tested workflow templates to compute various materials properties such as elec-tronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomatealso enables the computational characterization of materials by providing workflows that calculate X-rayabsorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate isthat it provides both fully functional workflows as well as reusable components that enable one to com-pose complex materials science workflows that use a diverse set of computational tools. Additionally,atomate creates output databases that organize the results from individual calculations and contains abuilder framework that creates summary reports for each computed material based on multiplesimulations.

Mathew, Kiran↗
DOE OSTI2015

Bowtie filters for dedicated breast CT: Theory and computational implementation

Purpose: To design bowtie filters with improved properties for dedicated breast CT to improve image quality and reduce dose to the patient. Methods: The authors present three different bowtie filters designed for a cylindrical 14-cm diameter phantom with a uniform composition of 40/60 breast tissue, which vary in their design objectives and performance improvements. Bowtie design #1 is based on single material spectral matching and produces nearly uniform spectral shape for radiation incident upon the detector. Bowtie design #2 uses the idea of basis material decomposition to produce the same spectral shape and intensity at the detector, using two different materials. Bowtie design #3 eliminates the beam hardening effect in the reconstructed image by adjusting the bowtie filter thickness so that the effective attenuation coefficient for every ray is the same. All three designs are obtained using analytical computational methods and linear attenuation coefficients. Thus, the designs do not take into account the effects of scatter. The authors considered this to be a reasonable approach to the filter design problem since the use of Monte Carlo methods would have been computationally intensive. The filter profiles for a cone-angle of 0° were used for the entire length of each filter because the differences between those profiles and the correct cone-beam profiles for the cone angles in our system are very small, and the constant profiles allowed construction of the filters with the facilities available to us. For evaluation of the filters, we used Monte Carlo simulation techniques and the full cone-beam geometry. Images were generated with and without each bowtie filter to analyze the effect on dose distribution, noise uniformity, and contrast-to-noise ratio (CNR) homogeneity. Line profiles through the reconstructed images generated from the simulated projection images were also used as validation for the filter designs. Results: Examples of the three designs are presented. Initial verification of performance of the designs was done using analytical computations of HVL, intensity, and effective attenuation coefficient behind the phantom as a function of fan-angle with a cone-angle of 0°. The performance of the designs depends only weakly on incident spectrum and tissue composition. For all designs, the dynamic range requirement on the detector was reduced compared to the no-bowtie-filter case. Further verification of the filter designs was achieved through analysis of reconstructed images from simulations. Simulation data also showed that the use of our bowtie filters can reduce peripheral dose to the breast by 61% and provide uniform noise and CNR distributions. The bowtie filter design concepts validated in this work were then used to create a computational realization of a 3D anthropomorphic bowtie filter capable of achieving a constant effective attenuation coefficient behind the entire field-of-view of an anthropomorphic breast phantom. Conclusions: Three different bowtie filter designs that vary in performance improvements were described and evaluated using computational and simulation techniques. Results indicate that the designs are robust against variations in breast diameter, breast composition, and tube voltage, and that the use of these filters can reduce patient dose and improve image quality compared to the no-bowtie-filter case.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Quantum computing without quantum computers: Database search and data processing using classical wave superposition

Quantum computers are proven to be more efficient at solving a specific class of problems compared to traditional digital computers. Superposition of states and quantum entanglement are the two key ingredients that make quantum computing so powerful. However, not all quantum algorithms require quantum entanglement (e.g., search through an unsorted database). Is it possible to utilize classical wave superposition to speed up database searching as much as by using quantum computers? There were several attempts to mimic quantum computers using classical waves. It was concluded that the use of classical wave superposition comes with the cost of an exponential increase in resources. In this work, we consider the feasibility of building classical wave-based devices able to provide fundamental speedup over digital counterparts without the exponential overhead. We present experimental data on database searching through a magnetic database using spin wave superposition. The results demonstrate the same speedup as expected for quantum computers. Also, we present examples of numerical modeling demonstrating classical wave interference for period finding. This approach may not compete with quantum computers with efficiency but outperform classical digital computers. We argue that classical wave-based devices can perform some of the quantum algorithms with the same efficiency as quantum computers as long as quantum entanglement is not required.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow

Computational simulations have become central to the seismic analysis and design of major infrastructure over the past several decades. Most major structures are now “proof tested” virtually through representative simulations of earthquake-induced response. More recently, with the advancement of high-performance computing (HPC) platforms and the associated massively parallel computational ecosystems, simulation is beginning to play a role in increased understanding and prediction of ground motions for earthquake hazard assessments. However, the computational requirements for regional-scale geophysics-based ground motion simulations are extreme, which has restricted the frequency resolution of direct simulations and limited the ability to perform the large number of simulations required to numerically explore the problem parametric space. In this article, recent developments toward an integrated, multidisciplinary earth science-engineering computational framework for the regional-scale simulation of both ground motions and resulting structural response are described with a particular emphasis on advancing simulations to frequencies relevant to engineered systems. This multidisciplinary computational development is being carried out as part of the US Department of Energy (DOE) Exascale Computing Project with the goal of achieving a computational framework poised to exploit emerging DOE exaflop computer platforms scheduled for the 2022–2023 timeframe.

58 GEOSCIENCES↗
DOE OSTI2012

Performing an allreduce operation on a plurality of compute nodes of a parallel computer

Methods, apparatus, and products are disclosed for performing an allreduce operation on a plurality of compute nodes of a parallel computer. Each compute node includes at least two processing cores. Each processing core has contribution data for the allreduce operation. Performing an allreduce operation on a plurality of compute nodes of a parallel computer includes: establishing one or more logical rings among the compute nodes, each logical ring including at least one processing core from each compute node; performing, for each logical ring, a global allreduce operation using the contribution data for the processing cores included in that logical ring, yielding a global allreduce result for each processing core included in that logical ring; and performing, for each compute node, a local allreduce operation using the global allreduce results for each processing core on that compute node.

97 MATHEMATICS AND COMPUTING↗