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At least 505 records · Page 28

A Unified Workflow for Sensitivity-Based Kinetic Analysis in Microkinetic Models

Degrees of rate control (DRC), apparent activation energies, and apparent reaction orders are established local sensitivity diagnostics for interpreting microkinetic models, but applying them routinely to large mechanisms often requires substantial reaction-specific bookkeeping, perturbation design, and postprocessing. Here, in this study, we present a unified derivative-based workflow that evaluates these quantities from a single compiled reaction-network model and target-rate definition. For any user-provided microkinetic model, the workflow compiles the mechanism into stoichiometrically consistent mass-action rate equations, solves the surface dynamics, and uses automatic differentiation to compute sensitivities with respect to rate constants, temperature, and gas partial pressures. By combining their calculations in the same framework, the workflow clearly demonstrates the relationships between different DRCs and the apparent activation energy. Using existing examples of propylene partial oxidation and methane oxidation on Pd(100), we verify expected transient redistribution of rate control, distinguish net Campbell DRCs from one-sided directional sensitivities, and show how apparent activation energy can be reconstructed either from one-sided DRCs or from state-based DRCs while critical mechanistic insights are obtained consistently. In the methane oxidation case, a pathway-subset test further illustrates how a simplified mechanism preserves key kinetic signatures of a full model, showing the potential of our user-friendly tool for model construction beyond kinetic analysis.

36 MATERIALS SCIENCE↗

Performance Understanding and Analysis for Exascale Data Management Workflows (Collaboration)

The general approach of the MONA project is depicted in Figure 1. The figure shows performance monitoring applied to an I/O workflow associated with a scientific simulation, with online measurements captured from this workflow informing methods for workflow reconfiguration and dynamic adjustment. The goal is to maintain suitable levels of Quality of Service for workflow execution, by understanding the underlying causes of workflow performance and behavior. One outcome will be workflow performance models able to characterize realistic workflows. Another outcome will be performance ‘mini-apps’ implementing such workflow behavior. Out of scope for this project are advanced methods for online workflow control.

97 MATHEMATICS AND COMPUTING↗

INTEGRATED WORKFLOW MANAGEMENT FOR PARTICLE ACCELERATOR SIMULATION

Supercomputing systems are used for a wide range of computationally demanding tasks in many fields of science and engineering. They play a key role in numerical simulation, in which mathematical models are computed in order to simulate the behavior of physical systems. Scientists and engineers that use supercomputers for numerical simulation often have their productivity limited by the need to manually organize and manage extremely large amounts of data that are often produced and consumed by the software programs run on these systems. Recognizing these limitations, Kitware Inc. (Clifton Park, NY) and SLAC National Accelerator Laboratory (Menlo Park, CA) are developing an advanced software platform that can reduce the cognitive overhead required by knowledge workers when using supercomputers for numerical simulation. Phase I of the project is complete and includes the development of new capabilities for organizing simulation project files, improvements to the user interface and overall usability, and deployment of a “middle tier” server to sit between user desktop machines and supercomputers to offload much of the data management workload. The project also developed prototype software for executing sequences of numerical simulations, and a prototype for migrating supercomputing software to cloud-based computing systems to provide a potential alternative to supercomputers with different logistical and price-to-performance tradeoffs.

Tourtellott, John↗

STNS01-44 BEE – FY22-1: Enhanced BEE Client [Slide]

BEE provides a portable, modular, HPC-focused workflow engine capable of managing containerized applications at scale. In FY22 BEE is completing enhancements and refinements that will complete the major development work of the workflow system. The first major milestone is the development of a graphical client. The second milestone will be the ability for BEE to automatically restart checkpointed tasks. The final milestone for FY22 will be the ability to launch and manage multiple simultaneous workflows.

97 MATHEMATICS AND COMPUTING↗

VIBES: a workflow for annotating and visualizing viral sequences integrated into bacterial genomes

Abstract Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES’s primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.

59 BASIC BIOLOGICAL SCIENCES↗

Near real-time streaming analysis of big fusion data

Experiments on fusion plasmas produce high-dimensional data time series with ever-increasing magnitude and velocity, but turn-around times for analysis of this data have not kept up. For example, many data analysis tasks are often performed in a manual, ad-hoc manner some time after an experiment. In this article, we introduce the Delta framework that facilitates near real-time streaming analysis of big and fast fusion data. By streaming measurement data from fusion experiments to a high-performance compute center, Delta allows computationally expensive data analysis tasks to be performed in between plasma pulses. This article describes the modular and expandable software architecture of Delta and presents performance benchmarks of individual components as well as of an example workflow. Focusing on a streaming analysis workflow where electron cyclotron emission imaging (ECEi) data is measured at KSTAR on the National Energy Research Scientific Computing Center's (NERSC's) supercomputer we routinely observe data transfer rates of about 4 Gigabit per second. In NERSC, a demanding turbulence analysis workflow effectively utilizes multiple nodes and graphical processing units and executes them in under 5 min. We further discuss how Delta uses modern database systems and container orchestration services to provide web-based real-time data visualization. For the case of ECEi data we demonstrate how data visualizations can be augmented with outputs from machine learning models. Here, by providing session leaders and physics operators, results of higher-order data analysis using live visualizations may make more informed decisions on how to configure the machine for the next shot.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗

Software Framework for Federated Science Instruments

There is an unprecedented promise of enhanced capabilities for federations of leadership computing systems and experimental science facilities by leveraging software technologies for fast and efficient operations. These federations seek to unify different science instruments, both computing and experimental, to effectively support science users and operators to execute complex workflows. The FedScI project addresses the software challenges associated with the formation and operation of federated environments by leveraging recent advances in containerization of software and softwarization of hardware. We propose a software framework to streamline the federation usage by science users and it’s provisioning and operations by facility providers. A distinguishing element of our work is the support for improved interaction between experimental devices, such as beam-line instruments, and more traditional high-performance computing resources, including compute, network, storage systems. We present guiding principles for the software framework and highlight portions of a current prototype implementation. We describe our science use case involving neutron imaging beam-lines (SNAP/BL-3, Imaging/CG-1D) at the Spallation Neutron Source and High Flux Isotope Reactor facilities at Oak Ridge National Laboratory. Additionally, we detail plans for a more direct instrument interaction within a federated environment, which could enable more advanced workflows with feedback loops to shorten the time to science.

Naughton III, Thomas↗

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

36 MATERIALS SCIENCE↗

Integrating quantum computing resources into scientific HPC ecosystems

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era’s inherent external noise issues. Here, this paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Diabatization with Electrostatic Embedding for Studying Photophysics in Organic Molecular Crystals

Highly emissive organic molecular crystals find applications in several areas, such as organic electronics, solar cells, and sensors. Understanding the excited-state mechanisms underlying these applications is essential for optimizing and controlling them effectively. Exciton models coupled with nonadiabatic dynamics, particularly quantum dynamics, provide crucial insights into photochemical and photophysical processes in molecular crystals. Nevertheless, there remains a lack of general tools and automated workflows to facilitate such simulations. In this paper, we present a computational strategy to investigate the photoactivated dynamics of organic molecular crystals, bridging methodologies traditionally used for molecular systems and materials science, with a particular focus on the interplay between local excitations and charge transfer (CT) processes. We have implemented an interface between the fromage and Overdia programs, enabling the construction of vibronic Hamiltonians for molecular crystals within an excited-state ONIOM(QM:QM′) framework, incorporating long-range electrostatics through a RESP-based Ewald summation. Fragment-based diabatization provides a route to quantum dynamics simulations in weak-to-intermediate coupling regimes. The method was applied to the photophysics of dibenzo[g,p]chrysene (DBC) crystals using time-dependent DFT. The fromage/ Overdia interface was employed to compute the couplings of local excitations and CT states for 18 unique DBC dimers in the crystal and to quantify the influence of electrostatic embedding, which was found to be modest (10−20%). Simulations on π-stacked dimers reproduced the small red shift observed experimentally from solution to crystal, attributed to electronic interactions among fixed monomers rather than crystal electrostatics. Quantum dynamics simulations revealed ultrafast population transfer from bright local excitations to CT states. This approach establishes a robust framework linking molecular and solid-state excited-state dynamics, with potential applications for studying excitations, defects, and impurities in molecular crystals.

Crystals↗

The ATLAS experiment software on ARM

With an increased dataset obtained during the Run 3 of the LHC at CERN and the even larger expected increase of the dataset by more than one order of magnitude for the HL-LHC, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM architecture is becoming a competitive and energy efficient alternative. Some surveys indicate its increased presence in HPCs and commercial clouds, and some WLCG sites have expressed their interest. Chip makers are also developing their next generation solutions on ARM architectures, sometimes combining ARM and GPU processors in the same chip. Consequently it is important that the ATLAS software embraces the change and is able to successfully exploit this architecture. We report on the successful porting to ARM of the Athena software framework, which is used by ATLAS for both online and offline computing operations. Furthermore we report on the successful validation of simulation workflows running on ARM resources. For this we have set up an ATLAS Grid site using ARM compatible middleware and containers on Amazon Web Services (AWS) ARM resources. The ARM version of Athena is fully integrated in the regular software build system and distributed in the same way as other software releases. In addition, the workflows have been integrated into the HEPscore benchmark suite which is the planned WLCG wide replacement of the HepSpec06 benchmark used for Grid site pledges. In the overall porting process we have used resources on AWS, Google Cloud Platform (GCP) and CERN. A performance comparison of different architectures and resources will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Regularizing Invertible Neural Networks for Airfoil Design Through Dimension Reduction

This work examines how dimension reduction can improve the performance of invertible neural networks (INN) for airfoil design. Design workflows are typically expensive, relying on many evaluations of high fidelity computational fluid dynamics (CFD) models. Furthermore, the inverse design problem is typically ill-posed. That is, multiple valid solutions exist that satisfy the design criteria. Regularization can reduce this inverse design space and simplify the problem. We study the use of subspace-based input dimension reduction to act as a regularizer for the INN model and improve the recovery of new airfoil shapes with desired performance characteristics. We find that the dimension reduction identifies two dominant modes, relating to airfoil thickness and camber, that optimally determine the airfoil's aerodynamics. We demonstrate the capability of the proposed INN model to generate 100 airfoils that satisfy the specific aerodynamic and structural characteristics.

airfoil design↗

Evolving HPC and Application Design Toward a Coupled Data Assimilation System at NASA Suitable for Emerging Exascale Platforms

The prediction capabilities of global models have continuously evolved from the traditional medium-range global weather prediction application to span scales in support of hourly prediction of convective scale storms to seasonal Earth system prediction. This evolution has increased the demands on the system infrastructure design and workflow to achieve the required performance on modern high-performance computing (HPC) platforms. The planned evolution of the Goddard Earth Observing System (GEOS) modeling and assimilation system will stress the capabilities of conventional HPC overwhelming the available compute cycles at the NASA Center for Climate Simulation (NCCS) at the NASA Goddard Space Flight Center in the coming 5-10 years. This has led to the re-design of key elements of the assimilation and modeling systems to achieve significant gains in performance on anticipated Exacale platforms. The transition of the assimilation system to the Joint Effort for Data assimilation Integration (JEDI) framework has positioned GEOS to exploit new efficient algorithms for data assimilation (DA) in a fully-coupled Earth system context. The suitability of the GEOS model to leverage a domain specific language (DSL) approach and artificial intelligence (AI) is being explored to accelerate computational performance and data exchange efficiency of the coupled Earth system model. The storage and processing of large data volumes produced by these advance systems is being redesigned with a data-centric cloud-based approach. We will highlight the recent efforts in these areas and emphasize the demand for further development and re-design to achieve the science objectives in support of NASA's Earth system modeling and assimilation missions.

Putman, Bill↗

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗

Creating Apptainer Workflows with Docker-Compose-like Utilities

Creating Apptainer Workflows with Docker-Compose-like Utilities In this presentation, I will explore the utilization of a tool called process-compose, inspired by docker-compose, to create Apptainer-based services. This approach allows for easy deployment and management of fully containerized applications on High Performance Computing (HPC) systems without requiring elevated privileges. Benefits to the Ecosystem: By incorporating process-compose and Apptainer, I aim to address several key challenges in the HPC ecosystem: Simplified Workflow Management: Process-compose provides a user-friendly interface for defining and managing complex containerized application services, reducing the setup time and lowering the barrier to entry for new users. Enhanced Portability: Apptainer ensures that containerized applications can run consistently across different HPC environments, promoting greater portability and reducing compatibility issues. Process-compose is also a single binary that does not need to be installed by admin level users. Community Driven Solutions: This approach aligns with the goals of the High Performance Software Foundation (HPSF) to advance community-driven solutions. By sharing our experiences and insights, I hope to foster collaboration and innovation within the HPC community. Increased Productivity: The combination of process-compose and Apptainer streamlines the serve deployment process, allowing researchers and developers to focus more on their scientific work rather than the intricacies of system or service administration. Through this presentation, attendees will gain valuable insights into the practical implementation of containerized workflows on HPC systems, learn about the benefits of using process-compose and Apptainer, and understand how these tools can contribute to a more efficient HPC ecosystem.

97 - MATHEMATICS AND COMPUTING↗

Characterizing Quantum Classifier Utility in Natural Language Processing Workflows

Quantum Natural Language Processing (QNLP) develops natural language processing (NLP) models for deployment on quantum computers. We explore feature and data prototype selection techniques to address challenges posed by encoding high dimensional features. Our study builds quantum circuit classifiers that includes classical feature pre-processing, quantum embedding and quantum model training. The quantum models are built on 4 or 6 qubits and the quantum neural network (QNN) uses the established bricklayer design. We compare the dependence of model performance (in terms of accuracy and F1 scores) on feature length, embedding gates and parameterized unitary design. We compare the performance of quantum machine learning models to classical convolution neural network model (CNN) on binary and multi-class classification tasks using two datasets of synthetic features and labels. The first is the ECP-CANDLE P3B3 dataset a corpus of synthetically generated cancer pathology reports. The second dataset is extracted from well-known benchmark dataset (MADELON) - features are generated with a combination of informative, repeated and uninformative features. Both datasets are used for binary classification and multi-class classification with 3 classes. We observe robust, accurate performance from all models on the binary classification tasks, but multiclass classification is a challenge for the quantum models-there is a notable decrease in accuracy when using 3 classes. Overall the performance is comparable in terms of recall and accuracy between QNNs and CNNs, even with large datasets. These results provide a point of comparison between quantum and classical models on real-world datasets.

Hamilton, Kathleen↗