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DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗

DOE OSTI2023

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Rao, Nageswara↗

DOE OSTI2021

Phase Demodulation by Frequency Chirping in Coherence Microwave Photonic Interferometry

This paper presents a signal processing method to demodulate the optical interference phase of cascaded individual optical fiber intrinsic Fabry-Perot interferometric (IFPI) sensors in a coherent microwave-photonic interferometry (CMPI) distributed sensing system. The new method utilizes the chirp effect of electro-optic modulator (EOM) to create a quasi-quadrature optical interference phase shift between two adjacent pulses which correspond to two adjacent reflection points in the time domain. The phase shift can be controlled by adjusting the bias voltage that is applied to the EOM. The interference phase is calculated by elliptically fitting the phase shift. The interference phase change is proportional to the optical path difference (OPD) change of the interferometer, and the sign can be used to differentiate the increase or decrease of the OPD. The method is demonstrated for distributed strain sensing, showing good linearity, high resolution and large dynamic range.

42 ENGINEERING↗

DOE OSTI2015

Validation of HELIOS for ATR Core Follow Analyses

This work summarizes the validation analyses for the HELIOS code to support core design and safety assurance calculations of the Advanced Test Reactor (ATR). Past and current core safety assurance is performed by the PDQ-7 diffusion code; a state of the art reactor physics simulation tool from the nuclear industry’s earlier days. Over the past twenty years, improvements in computational speed have enabled the use of modern neutron transport methodologies to replace the role of diffusion theory for simulation of complex systems, such as the ATR. More exact methodologies have enabled a paradigm-shift away from highly tuned codes that force compliance with a bounding safety envelope, and towards codes regularly validated against routine measurements. To validate HELIOS, the 16 ATR operational cycles from late-2009 to present were modeled. The computed power distribution was compared against data collected by the ATR’s on-line power surveillance system. It was found that the ATR’s lobe-powers could be determined with ±10% accuracy. Also, the ATR’s cold startup shim configuration for each of these 16 cycles was estimated and compared against the reported critical position from the reactor log-book. HELIOS successfully predicted criticality within the tolerance set by the ATR startup procedure for 13 out of the 16 cycles. This is compared to 12 times for PDQ (without empirical adjustment). These findings, as well as other insights discussed in this report, suggest that HELIOS is highly suited for replacing PDQ for core safety assurance of the ATR. Furthermore, a modern verification and validation framework has been established that allows reactor and fuel performance data to be computed with a known degree of accuracy and stated uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Exploiting CRIC to streamline the configuration management of GlideinWMS factories for CMS support

GlideinWMS is a workload management and provisioning system that allows sharing computing resources distributed over independent sites. Based on the requests made by GlideinWMS frontends, a dynamically sized pool of resources is created by GlideinWMS pilot factories via pilot job submission to resource sites’ CEs. More than 400 CEs are currently serving more than ten virtual organizations through GlideinWMS, with CMS being the biggest user with 230 CEs. The complex configurations of the parameters defining resource requests, as submitted to those CEs, have been historically managed by manually editing a set of different XML files. New possibilities arise with CMS adopting the CRIC, an information system that collects, aggregates, stores, and exposes, among other things, computing resource data coming from various data providers. The paper will describe the challenges faced when CMS started to use CRIC to automatically generate the GlideinWMS factory configurations. The architecture of the prototype, and the ancillary tools developed to ease this transition, will be discussed. Finally, future plans and milestones will be outlined.

Dost, Jeffrey↗

DOE OSTI2019

Distributed approximate Newton algorithms and weight design for constrained optimization

Not Available

Anderson, Tor↗

DOE OSTI2017

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Fusion neutron generation computations in a stellarator-mirror hybrid with neutral beam injection

In the paper [Moiseenko V.E., Noack K., Agren O. 'Stellarator-mirror based fusion driven fission reactor' J Fusion Energy 29 (2010) 65.], a version of a fusion driven system (FDS), i.e. a sub-critical fast fission assembly with a fusion plasma neutron source, is proposed. The plasma part of the reactor is based on a stellarator with a small mirror part. Hot ions with high perpendicular energy are assumed to be trapped in the magnetic mirror part. The stellarator part which connects to the mirror part and provides confinement for the bulk (deuterium) plasma. In the magnetic well of the mirror part, fusion reactions occur from collisions between a of hot ion component (tritium) with cold background plasma ions. RF heating is one option to heat the tritium. A more conventional method to sustain the hot ions is neutral beam injection (NBI), which is here studied numerically for the above-mentioned hybrid scheme. For these studies, a new kinetic code, KNBIM, has been developed. The code takes into account Coulomb collisions between the hot ions and the background plasma. The geometry of the confining magnetic field is arbitrary for the code. It is accounted for via a numerical bounce averaging procedure. Along with the kinetic calculations the neutron generation intensity and its spatial distribution are computed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

CT dose equilibration and energy absorption in polyethylene cylinders with diameters from 6 to 55 cm

Purpose: ICRU Report No. 87 Committee and AAPM Task Group 200 designed a three-sectional polyethylene phantom of 30 cm in diameter and 60 cm in length for evaluating the midpoint dose D{sub L}(0) and its rise-to-the-equilibrium curve H(L) = D{sub L}(0)/D{sub eq} from computed tomography (CT) scanning, where D{sub eq} is the equilibrium dose. To aid the use of the phantom in radiation dose assessment and to gain an understanding of dose equilibration and energy absorption in polyethylene, the authors evaluated the short (20 cm) to long (60 cm) phantom dose ratio with a polyethylene diameter of 30 cm, assessed H(L) in polyethylene cylinders of 6–55 cm in diameters, and examined energy absorption in these cylinders. Methods: A GEANT4-based Monte Carlo program was used to simulate the single axial scans of polyethylene cylinders (diameters 6–55 cm and length 90 cm, as well as diameter 30 cm and lengths 20 and 60 cm) on a clinical CT scanner (Somatom Definition dual source CT, Siemens Healthcare). Axial dose distributions were computed on the phantom central and peripheral axes. An average dose over the central 23 or 100 mm region was evaluated for modeling dose measurement using a 0.6 cm{sup 3} thimble chamber or a 10 cm long pencil ion chamber, respectively. The short (20 cm) to long (90 cm) phantom dose ratios were calculated for the 30 cm diameter polyethylene phantoms scanned at four tube voltages (80–140 kV) and a range of beam apertures (1–25 cm). H(L) was evaluated using the dose integrals computed with the 90 cm long phantoms. The resultant H(L) data were subsequently used to compute the fraction of the total energy absorbed inside or outside the scan range (E{sub in}/E or E{sub out}/E) on the phantom central and peripheral axes, where E = LD{sub eq} was the total energy absorbed along the z axis. Results: The midpoint dose in the 60 cm long polyethylene phantom was equal to that in the 90 cm long polyethylene phantom. The short-to-long phantom dose ratios changed with beam aperture and phantom axis but were insensitive to tube voltage. H(L) was insensitive to tube voltage and CT scanner model. As phantom diameter increased from 6 to 55 cm, E{sub in}/E generally decreased but asymptotically approached constant levels on the peripheral axes of large phantoms. The curve of E{sub in}/E versus scan length was almost identical to that of H(L). Similarly, E{sub out}/E increased with scan length and asymptotically approached the equilibrium for large scan lengths. E{sub out}/D{sub eq} was much less than the equilibrium length L{sub eq} where H(L) = 0.98, even with scan lengths much larger than L{sub eq}. Conclusions: The polyethylene phantom designed by ICRU Report No. 87 Committee and AAPM Task Group 200 is adequately long for assessing the midpoint dose and its equilibration in CT scanning. The short-to-long phantom dose ratios and the H(L) data provided in this paper allow easy evaluations of the midpoint dose, longitudinal dose distribution, and energy absorption in polyethylene phantoms. The results of dose equilibration and energy absorption presented herein may be insightful for the clinical CT scans with various subject sizes and scan lengths.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Iteration-based Linearized Distribution-level Locational Marginal Price for Three-phase Unbalanced Distribution Systems

Distributed energy resources (DERs) are rocking the utilities’ business landscape. It calls for competitive market environments that incentivize DERs to form maximum operating efficiency. Among proposed pricing schemes, distribution-level locational marginal price (DLMP) is effective in signaling the marginal generation cost differences driven by energy losses and network constraints. It can be derived from a distribution-level optimal power flow (OPF) framework, as it essentially presents the sensitivity of optimized generation cost towards incremental loads. However, due to the high resistance-to-inductance ratio and unbalanced characteristics of distribution networks, computational affordable DLMPs are highly challenged. This article provides a linear-approximated DLMP that can be solved efficiently and generalized to account for reactive power flow, three-phase unbalanced loads and meshed network structure. The successive linear programming technique is introduced to enhance the model accuracy. Case studies on an IEEE 123-Bus system validate its accuracy against a nonlinear benchmark and capability in offering proper incentives.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

Abstract With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

SU-E-T-94: An Advanced Rotating Gamma Ray System for Stereotactic Radiosurgery

Purpose: Co-60 beams have unique dosimetric properties that are ideally suited for cranial treatments. Co-60 sources with cone-shaped collimators provide conformal dose distributions allowing for ablative treatments with rapid dose falloff to spare nearby critical structures. This work investigates a novel, image-guided, rotational Gamma ray system that provides both superior dose conformity/gradient and accurate stereotaxy for stereotactic radiosurgery (SRS). Methods: The SupeRay system (Cyber Medical Corp., China) consists of a rotating source chamber containing 30 gamma sources focusing at the isocenter with 4 collimators measuring 3, 4, 8 and 16mm in diameter. A novel switch design enables the 30 Gamma sources to be turned off at any arbitrarily selected 60° interval in order to avoid critical structures. The 3D treatment couch provides automatic treatment positioning between individual shots and the kV imaging system provides orthogonal images with a spatial resolution of 0.24mm to facilitate target localization. Monte Carlo simulations were used to compute dose distributions and compare with measurements and other Gamma ray SRS systems. Results: Monte Carlo results confirmed the SupeRay design parameters including output factors and 3D dose distributions. Its beam penumbra/dose gradient is similar to or slightly better than that of the Elekta Gamma Knife. The penumbra in the (x,y,z) direction was (7.38mm,7.38mm,3.86mm) for the 16mm collimator, (4.83mm,4.83mm,3.12mm) for the 8mm collimator, and (3.03mm,3.03mm,2.38mm) for the 4mm collimator, respectively, on the SupeRay system while it was (9.5mm,10.0mm,2.9mm), (4.3mm,4.3mm,2.9mm) and (3.2mm,3.2mm,1.9mm) for the same collimator sizes, respectively, on the Perfexion system. The kV imaging system together with a non-invasive relocatable frame provides accurate target localization (<0.5mm) for cases requiring multiple treatment fractions. Conclusion: Because of the unique dosimetric properties of Co-60 sources and its accurate stereotaxy/dose delivery, SupeRay is ideally suited for single and multiple-session ablative treatments of cranial lesions. This work was partially supported by Cyber Medical Corp.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Large-Scale Optimization for Bayesian Inference in Complex Systems

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT₋Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas₋Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

GridAPPS-D Conceptual Design v1.0

The purpose of this document is to provide a conceptual design of the distribution system application development platform being developed for the U.S. Department of Energy’s Advanced Distribution Management System (ADMS) Program by the Grid Modernization Laboratory Consortium project GM0063. The platform will be referred to as GridAPPS-D. This document provides a high level, conceptual view of the platform and provides related background and contextual information. This document is intended to both educate readers about the technical work of the project and to serve as a point of reference for the project team. The document will be updated as the project progresses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Spatial fractionation of the dose using neon and heavier ions: A Monte Carlo study

Purpose: This work explores a new radiation therapy approach which might trigger a renewed use of neon and heavier ions to treat cancers. These ions were shown to be extremely efficient in radioresistant tumor killing. Unfortunately, the efficient region also extends into the normal tissue in front of the tumor. The strategy the authors propose is to profit from the well-established sparing effect of thin spatially fractionated beams, so that the impact on normal tissues might be minimized while a high tumor control is achieved. The main goal of this work is to provide a proof of concept of this new approach. With that aim, a dosimetric study was carried out as a first step to evaluate the interest of further explorations of this avenue. Methods: The GATE/GEANT4 v.6.1 Monte Carlo simulation platform was employed to simulate arrays of rectangular minibeams (700 μm × 2 cm) of four ions (Ne, Si, Ar, and Fe). The irradiations were performed with a 2 cm-long spread-out Bragg peak centered at 7 cm-depth. Dose distributions in a water phantom were scored considering two minibeams center-to-center distances: 1400 and 3500 μm. Peak and valley doses, peak-to-valley dose ratios (PVDRs), beam penumbras, and relative contribution of nuclear fragments and electromagnetic processes were assessed as figures of merit. In addition, the type and proportion of the secondary nuclear fragments were evaluated in both peak and valley regions. Results: Extremely high PVDR values (>100) and low valley doses were obtained. The higher the atomic number (Z) of the primary ion is, the lower the valleys and the narrower the penumbras. Although the yield of secondary nuclear products increases with Z, the actual dose being deposited by the secondary nuclear fragments in the valleys starts to be the dominant contribution at deeper points, helping in the sparing of proximal normal tissues. Additionally, a wider center-to-center distance leads to a minimized contribution of heavier secondary fragments in valleys. Conclusions: The computed dose distributions suggest that a spatial fractionation of the dose combined to the use of submillimetric field sizes might allow profiting from the high efficiency of neon and heavier ions for the treatment of radioresistant tumors, while preserving normal tissues. The authors’ results support the further exploration of this avenue. Next steps include the realization of biological experiment to confirm the shifting of normal tissue complication probability curves.

60 APPLIED LIFE SCIENCES↗