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Summer Internship Report: ARA2 Benchmarking

Over the past decade, the RISC-V Instruction Set Architecture (ISA) has emerged as a significant player in both academic and industrial processor design due to its open-source nature, modular extension system, and versatility across domains ranging from microcontrollers to high-performance computing (HPC). One of its most important recent advancements is the RISC-V Vector Extension (RVV), which enables explicit data-level parallelism through vector registers and vectorized instructions. Unlike traditional SIMD (Single Instruction, Multiple Data) architectures that fix vector lengths at design time, RVV uses the concept of VLEN (vector register length) as a hardware-independent parameter and allows software to adapt dynamically to the available vector width. This flexible approach ensures portability across implementations while enabling scalable performance. The ARA2 core is a parameterizable RISC-V vector processor developed at the Integrated Systems Lab at ETH Zürich and the University of Bologna. Designed as a tightly-coupled accelerator to a scalar RISC-V core, ARA2 implements the RVV 1.0 specification and offers tunable architectural parameters such as the number of vector lanes, VLEN, and cache sizes.

97 MATHEMATICS AND COMPUTING↗

NCCS High Performance GMRES Mixed Precision

HPG-MxP is a software package that performs a fixed number of multigrid preconditioned (using a Gauss-Seidel smoother) Generalized minimal residual (PGMRES) iterations in order to solve a possibly nonsymmetric large sparse linear system of equations. It is designed to be a benchmark to measure a computer's performance for sparse linear algebra workloads typical in scientific computing while allowing the use of mixed precision methods. The solution is required to have convergence characteristics and accuracy similar to double precision GMRES. It is based on the High Performance Conjugate Gradient Benchmark (HPCG) which restricts all implementations to use only the IEEE double precision format (FP64). The original implementation (https://github.com/hpg-mxp/hpg-mxp) was written by Ichitaro Yamazaki, Jennifer Loe, Christian Glusa, Sivasankaran Rajamanickam, Piotr Luszczek, and Jack Dongarra. Please refer to that repository for documentation on the original implementation. This version is maintained by the National Center for Computational Sciences at Oak Ridge National Laboratory. It is highly scalable and optimized for Oak Ridge Leadership Computing Facility (OLCF) systems, particularly Frontier.

Kashi, Aditya [Oak Ridge National Laboratory (ORNL↗

Parallel processing of filtered queries in attributed semantic graphs

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

97 MATHEMATICS AND COMPUTING↗

Robust and efficient variational fitting of Fock exchange

We propose a new variational fitting approach for Fock exchange that requires only the calculation of analytical three-center electron repulsion integrals. It relies on localized molecular orbitals and Hermite Gaussian auxiliary functions. The working equations along with a detailed description of the implementation are presented. The computational performance of the new algorithm is analyzed by benchmark calculations on systems with different dimensionality. Comparison with standard four-center and three-center electron repulsion integral Hartree-Fock calculations shows an excellent accuracy-performance relation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Vision-based localization for cooperative robot-CNC hybrid manufacturing

Wire and arc additive manufacturing (WAAM) has shown promise in recent years for producing large-scale parts with higher deposition rates than other additive processes. WAAM is often combined with subtractive machining to form a hybrid manufacturing process. This hybrid process can be realized by retrofitting computer numerical control (CNC) machines with deposition heads, adding spindles and deposition heads to robots, or developing part localization methods to transfer parts from an additive cell to a CNC machine. Here, a novel, robot-CNC hybrid configuration is introduced where a maneuverable robot is placed in front of a CNC machine to deposit material within the machine envelop. Furthermore, this method removes the need for part localization and the extensive machine modifications required for retrofitting; however, the problem of robot localization is also added. In this work, the effects of error in vision-based, contactless robot localization on machining parameters in a robot-machine hybrid process were studied. Performance was characterized on an implementation of this system using classical computer vision techniques. In addition, machining simulations were conducted to evaluate the effects of image-induced error on chip thickness, material removal rate, and machining allowance. Initial tests show that computer vision could adequately locate a robot for the hybrid WAAM process within .5 mm.

Hybrid manufacturing↗

A Parametric Study of the Adjoint Flux Calculation for TDKENO

TDKENO is a time-dependent transport code designed to simulate reactor transients. One application is to study experiments performed at the Transient Reactor Test Facility (TREAT) at the Idaho National Laboratory (INL). TDKENO solves the time-dependent, three-dimensional Boltzmann transport equation with explicit representation of delayed neutrons. Instead of directly integrating this equation, the Improved Quasi Static (IQS) method is employed. This involves factoring the neutron flux into two components - a rapidly varying (in time) amplitude equation and a slowly varying shape equation. These are solved separately on different time scales. The shape equation is solved using the Monte Carlo transport code KENO, from Oak Ridge National Laboratory's SCALE code package. Using the Monte Carlo method to solve the shape equation is computationally intensive, but the operation is only performed when needed - namely when the reactor system is undergoing a significant change. The amplitude equation is solved deterministically. This operation contains the time dependence and can be calculated relatively quickly, resulting in computational savings. TDKENO begins by solving the adjoint form of the transport equation for use as a weighting function. It uses this adjoint flux for multiple aspects of the simulation, including the calculation of all of the point kinetics parameters . The flux shape is found by solving a modified form of the transport equation in 'forward' mode. This paper examines the effect the adjoint weighting function has on the solution. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Design of Distributed Cyber-Physical Systems for Connected and Automated Vehicles with Implementing Methodologies

With the development of communication and control technology, intelligent transportation systems have received increasing attention from both industry and academia. Intelligent transportation systems are supported by the Internet of Things, Cyber-Physical System, Artificial Intelligence, Cloud Computing and many other technologies, which supply fundamental information for connected and automated vehicles. Although plenty of studies have provided different formulations for intelligent transportation systems, many of them depend on Master Control Center. However, a centralized control mode requires a huge amount of data transmission and high level of hardware configuration and may cause communication delay and privacy leak. Some distributed architectures have been proposed to overcome the above problems but systematized technologies to collect and exchange information, process large amounts of data, model the dynamics of vehicles, and safely control the connected and automated vehicles are not explored in detail. In this paper, we proposed a novel distributed cyber-physical system for connected and automated vehicles in which every vehicle is modeled as a double-integrator using edge computing to analyze information collected from its nearest neighbors. The vehicles are supposed to travel along a desired trajectory and to maintain a rigid formation geometry. Related methodologies for the proposed system are illustrated and experiments are conducted showing that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

97 MATHEMATICS AND COMPUTING↗

Mitigating component performance variation

Apparatus and methods may provide for characterizing a plurality of similar components of a distributed computing system based on a maximum safe operation level associated with each component and storing characterization data in a database and allocating non-uniform power to each similar component based at least in part on the characterization data in the database to substantially equalize performance of the components.

97 MATHEMATICS AND COMPUTING↗

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Charge order–superfluidity transition in a two-dimensional system of hard-core bosons and emerging domain structures

We have used high-performance parallel computations by NVIDIA graphics cards applying the method of nonlinear conjugate gradients and Monte Carlo method to observe directly the developing ground state configuration of a two-dimensional hard-core boson system with decrease in temperature, and its evolution with deviation from a half-filling. This has allowed us to explore unconventional features of a charge order—superfluidity phase transition, specifically, formation of an irregular domain structure, emergence of a filamentary superfluid structure that condenses within of the charge-ordered phase domain antiphase boundaries, and formation and evolution of various topological structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Hardware Evaluation Outreach: Application development Challenges Now and for the Exascale Era

The intent of this document is to assist the programmer in understanding details of contemporary and Exascale hardware system design and how these designs provide opportunities and place constraints on next-generation simulation software design. We attempt to clarify hardware organization and component details for our most current and Exascale systems to help program developers understand how software needs to change in order to take best advantage of the performance available. Exascale success is specifically defined for ECP as a 50x improvement over baseline in the aggregate "capability volume" on several KPP axes, of which raw floating point performance is only one, but also includes characteristics such as problem size, system memory size, node memory size, power, and efficiency. This multi-axis approach is particularly important to understand in the context of delivered improvements in real applications, since, for instance, the floating point computation may comprise less than 10% of the actual computational work required. Given the Exascale requirements and the constraints these requirements put on the performance expectations of fundamental system components, the programmer will be forced to re-think several application implementation details in order to achieve exaflop performance on these systems. The remainder of this document aims to present more detail on Exascale era system hardware and the specific areas that the programmer should address to extract performance from these systems. We attempt to give the programmer guidance at both a high- and low-level, providing some abstract suggestions on how to refactor codes given the expected system architectures and some low-level recommendations on how to implement these modifications. We also include a section on training resources that are helpful for both programmers that are just beginning to understand code modifications for contemporary and Exascale systems and for those that have done some refactoring and are now trying to extract maximal application performance from these systems.

97 MATHEMATICS AND COMPUTING↗

SU-E-P-36: Evaluation of MLC Positioning Errors in Dynamic IMRT Treatments by Analyzing Dynalog Files

Purpose: To assess the accuracy of MLC positioning in Varian linear accelerator, in dynamic IMRT technique, from the analysis of dynalog files generated by the MLC controller. Methods: In Clinac accelerators (pre-TrueBeam technology), control system has an approximately 50ms delay (one control cycle time). Then, the system compares the measured position to the planned position corresponding to the next control cycle. As it has been confirmed by Varian technical support, this effect causes that measured positions appear in dynalogs one cycle out of phase with respect to the planned positions. Around 9000 dynalogs have been analyzed, coming from the three linear accelerators of our center (one Trilogy and two Clinac 21EX) equipped with a Millennium 120 MLC. In order to compare our results to recent publications, leaf positioning errors (RMS and 95th percentile) are calculated with and without delay effect. Dynalogs have been analyzed using a in-house Matlab software. Results: The RMS errors were 0.341, 0.339 and 0.348mm for each Linac; being the average error 0.343 mm. The 95th percentiles of the error were 0.617, 0.607 and 0.625; with an average of 0.617mm. A recent multi-institution study carried out by Kerns et al. found a mean leaf RMS error of 0.32mm and a 95th percentile error value of 0.64mm.Without delay effect, mean leaf RMS errors obtained were 0.040, 0.042 and 0.038mm for each treatment machine; being the average 0.040mm. The 95th percentile error values obtained were 0.057, 0.058 and 0.054 mm, with an average of 0.056mm. Conclusion: Results obtained for the mean leaf RMS error and the mean 95th percentile were consistent with the multi-institution study. Calculated error statistics with delay effect are significantly larger due to the speed proportional and systematic leaf offset. Consequently it is proposed to correct this effect in dynalogs analysis to determine the MLC performance.

60 APPLIED LIFE SCIENCES↗

Elastic Stochastic Full Waveform Inversion (eSFWI)

This collaboration between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Chevron USA Inc., acting through its Chevron Technical Center division, aimed at developing next-generation computational methods for the Elastic Stochastic Full Waveform Inversion (eSFWI). Seismic imaging is heavily used in the oil and gas industry for identifying and operating subsurface reservoirs. Improved seismic imaging methods can improve productivity, lower costs, and improve operational and environmental safety. This CRADA demonstrated that new high-performance computing (HPC) architectures being rolled out over the next five years can enable unprecedented seismic imaging resolution when using eSFWI techniques to process active seismic data. An open-source computational mini-application was developed, capable of demonstrating near-peak performance for eSFWI algorithms on CPU and GPU enabled HPC platforms. Performance was demonstrated on LLNL HPC systems such as Lassen, as well as on Chevron systems. This project benefited Chevron USA Inc. by demonstrating the potential computational efficiency of their full waveform inversion capabilities used to characterize oil/gas reservoirs, which in turn benefits the public through potential increases in capabilities to perform analysis of leasing sites.

04 OIL SHALES AND TAR SANDS↗

Accelerated radiation transport modeling techniques for pencil beam computed tomography using gamma rays

Monte Carlo radiation transport modeling studies were performed for a compact, and high-resolution gamma-ray computed tomography system designed for imaging irradiated nuclear fuel. The system comprises a 60 Co source – chosen for its highly penetrating 1173 keV and 1332 keV gamma rays – a pair of high-aspect-ratio pencil beam collimators, and an inorganic scintillator detector. Here two acceleration methods are proposed to rapidly model a transmission type gamma-ray tomography system. The first, a variance reduction technique, is based on performing Monte Carlo simulations with a monodirectionally-biased source, sampled from a characteristic sub-volume of the full source volume. The second acceleration method is based on the deterministic calculations using the Beer–Lambert law and detector response characteristics. Comparison of simulations using acceleration approaches with analog simulations of the fully isotropic, full-volume equivalent, show that the Monte Carlo variance reduction technique gives quantitatively accurate predictions for large collimator aspect ratios while the deterministic calculations are semi-quantitative but converge close to the correct result as the collimator aspect ratio increases. As such, these techniques can be used to reduce the computational cost in generating simulated radiographs and tomographs by several orders of magnitude. Experimental validation efforts are currently underway and will be demonstrated in future work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Implementing High Performance Remote Method Invocation in CCA

We report our effort in engineering a high performance remote method invocation (RMI) mechanism for the Common Component Architecture (CCA). This mechanism provides a highly efficient and easy-to-use mechanism for distributed computing in CCA, enabling CCA applications to effectively leverage parallel systems to accelerate computations. This work is built on the previous work of Babel RMI. Babel is a high performance language interoperability tool that is used in CCA for scientific application writers to share, reuse, and compose applications from software components written in different programming languages. Babel provides a transparent and flexible RMI framework for distributed computing. However, the existing Babel RMI implementation is built on top of TCP and does not provide the level of performance required to distribute fine-grained tasks. We observed that the main reason the TCP based RMI does not perform well is because it does not utilize the high performance interconnect hardware on a cluster efficiently. We have implemented a high performance RMI protocol, HPCRMI. HPCRMI achieves low latency by building on top of a low-level portable communication library, Aggregated Remote Message Copy Interface (ARMCI), and minimizing communication for each RMI call. Our design allows a RMI operation to be completed by only two RDMA operations. We also aggressively optimize our system to reduce copying. In this paper, we discuss the design and our experimental evaluation of this protocol. Our experimental results show that our protocol can improve RMI performance by an order of magnitude.

99 GENERAL AND MISCELLANEOUS↗