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DOE OSTI2023

Extending the predictive power of perturbative QCD using the principle of maximum conformality and the Bayesian analysis

In addition to the evaluation of high-order loop contributions, the precision and predictive power of perturbative QCD (pQCD) predictions depends on two important issues: (1) how to achieve a reliable, convergent fixed-order series, and (2) how to reliably estimate the contributions of unknown higher-order terms. The recursive use of renormalization group equation, together with the Principle of Maximum Conformality (PMC), eliminates the renormalization scheme-and-scale ambiguities of the conventional pQCD series. The result is a conformal, scale-invariant series of finite order which also satisfies all of the principles of the renormalization group. In this paper we propose a novel Bayesian-based approach to estimate the size of the unknown higher order contributions based on an optimized analysis of probability distributions. We show that by using the PMC conformal series, in combination with the Bayesian analysis, one can consistently achieve high degree of reliability estimates for the unknown high order terms. Thus the predictive power of pQCD can be greatly improved. We illustrate this procedure for two pQCD observables: R e+e– and R τ , which are each known up to four loops in pQCD. Numerical analyses confirm that by using the scale-independent and more convergent PMC conformal series, one can achieve reliable Bayesian probability estimates for the unknown higher-order contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Control of Networked Traffic Flow Distribution: A Stochastic Distribution System Perspective

At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic modelling and control for a one-way single-lane corridor traffic system based on theory of stochastic distribution control that shapes the PDF of the queue length.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

BootCMatch: A Software Package for Bootstrap AMG Based on GraphWeighted Matching

This article has two main objectives: one is to describe some extensions of an adaptive Algebraic Multigrid (AMG) method of the form previously proposed by the first and third authors, and a second one is to present a new software framework, named BootCMatch, which implements all the components needed to build and apply the described adaptive AMG both as a stand-alone solver and as a preconditioner in a Krylov method. The adaptive AMG presented is meant to handle general symmetric and positive definite (SPD) sparse linear systems, without assuming any a priori information of the problem and its origin; the goal of adaptivity is to achieve a method with a prescribed convergence rate. The presented method exploits a general coarsening process based on aggregation of unknowns, obtained by a maximum weight matching in the adjacency graph of the system matrix. More specifically, a maximum product matching is employed to define an effective smoother subspace (complementary to the coarse space), a process referred to as compatible relaxation, at every level of the recursive two-level hierarchical AMG process. Results on a large variety of test cases and comparisons with related work demonstrate the reliability and efficiency of the method and of the software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Algorithms for Efficient Reproducible Floating Point Summation

We define “reproducibility” as getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should not affect the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociative floating point addition, makes reproducibility challenging even for summation, or operations like the BLAS. We describe a “reproducible accumulator” data structure (the “binned number”) and associated algorithms to reproducibly sum binary floating point numbers, independent of summation order. We use a subset of the IEEE Floating Point Standard 754-2008 and bitwise operations on the standard representations in memory. Our approach requires only one read-only pass over the data, and one reduction in parallel, using a 6-word reproducible accumulator (more words can be used for higher accuracy), enabling standard tiling optimization techniques. Summing n words with a 6-word reproducible accumulator requires approximately 9 n floating point operations (arithmetic, comparison, and absolute value) and approximately 3 n bitwise operations. The final error bound with a 6-word reproducible accumulator and our default settings can be up to 2 29 times smaller than the error bound for conventional (recursive) summation on ill-conditioned double-precision inputs.

Computer Science↗

DOE OSTI2024

A Formalization of Core Why3 in Coq

Intermediate verification languages like Why3 and Boogie have made it much easier to build program verifiers, transforming the process into a logic compilation problem rather than a proof automation one. Why3 in particular implements a rich logic for program specification with polymorphism, algebraic data types, recursive functions and predicates, and inductive predicates; it translates this logic to over a dozen solvers and proof assistants. Accordingly, it serves as a backend for many tools, including Frama-C, EasyCrypt, and GNATProve for Ada SPARK. But how can we be sure that these tools are correct? The alternate foundational approach, taken by tools like VST and CakeML, provides strong guarantees by implementing the entire toolchain in a proof assistant, but these tools are harder to build and cannot directly take advantage of SMT solver automation. As a first step toward enabling automated tools with similar foundational guarantees, we give a formal semantics in Coq for the logic fragment of Why3. We show that our semantics are useful by giving a correct-by-construction natural deduction proof system for this logic, using this proof system to verify parts of Why3's standard library, and proving sound two of Why3's transformations used to convert terms and formulas into the simpler logics supported by the backend solvers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Mota Mapper: A Multi-Objective Topology-Aware Mapper Library

Mota is a library that provides several heuristics for mapping tasks to compute ranks (processes) on a networked computer cluster or supercomputer. It was originally designed for task placement of adaptive mesh refinement codes (AMR). It is multi-objective in the sense that it simultaneously balances (potentially multiple types of) computational load on each rank as well as the communication traffic between the boxes. The heuristics used for mapping include algorithms such as greedy list assignment and space-filling curves, as well as algorithms from graph analysis such as adjacency matrix bandwidth reduction, recursive bisection, and greedy graph placement.

Chan, Cy↗

DOE OSTI2017

ExaSP2

ExaSP2 is a reference implementation of typical linear algebra algorithms and workloads for a quantum molecular dynamics (QMD) electronic structure code. The algorithm is based on a recursive second-order Fermi-Operator expansion method (SP2) and is tailored for density functional based tight-binding calculations of material systems.

Negre, Christian↗

DOE OSTI2019

Ranger-based Iterative Random Forest

Iterative Random Forest (iRF) is an improvement upon the classic Random Forest, using weighted iterations to distill the forests. Ranger is a C++ implementation of random forest or recursive partitioning, particularly suited for high dimensional data. Ranger-Based iRF uses Ranger as a base Random Forest codebase upon which iterative and scalability aspects have been added.

Jacobson, DanielA↗

DOE OSTI2020

Scalable Linked Dynamic Equilibrium (SLiDE)

SLiDE (Scalable Linked Dynamic Equilibrium) replicates the blueNOTE (National Open source Tools for general Equilibrium analysis) datastream from the Wisconsin National Data Consortium. From there, it produces a recursive dynamic computable general equilibrium model of the US states and eventually the global/national economies. The datastream operations have been set up to allow for scalability of both regions and sectors represented. The model has the capability to flexibly define functional forms similar to other general equilibrium model construction software.

Hughes, Caroline↗

DOE OSTI2020

Hydra Network Automatic Configuration (Hydra Autoconfig) v1

Hydra Autoconfig is an algorithm and software program to automatically configure a resilient network of independent ASICs that are capable of being connected as a Hydra network. The Hydra network itself is described in an invention disclosure titled: "Ad-Hoc Networks of Readout ASICs for Reliable Detector Instrumentation" (2020-091). The Hydra Autoconfig software works by first configuring a Hydra node. Then it recursively builds a Hydra network by attempting to link to each available upstream node in succession, keeping links that are verified. It then repeats on each newly generated node until no possible links remain. Once it is so configured, a reliable Hydra network of ASICs remains that can be reconfigured later to respond to damage or malfunction of one or more of the constituent ASICs.

Grace, Carl↗

DOE OSTI2023

Ciel

Compiler optimizations can alter the numerical results of scientific computing applications. When numerical results differ significantly between compilers, optimization levels, and floating-point hardware, these numerical inconsistencies can impact programming productivity. Ciel is a framework that helps programmers identify locations in the source code that are affected by compiler optimizations in CPU and GPU code. Ciel uses a floating-point precision enhancement strategy, guided by a recursive bisection search algorithm with increasing search granularity, to identify the program expressions that induce numerical inconsistencies due to compiler optimizations.

Miao, Wenjun↗

DOE OSTI2022

Acoustic Data Analytics For Predictive Technologies

The software utilizes frequency, amplitude, and phase features from sensor signals (either raw or processed) that are extracted using recursive frequency analysis, enabling (1) understanding of reactor operations under different operating conditions, (2) identifying of quiescent frequency ranges for sensor development, (3) system controls (4) predictive analytics, (5) structural health monitoring, and (6) loose part monitoring.

Smith, JamesA.↗

DOE OSTI2023

GNATFinder

SAND2023-05510O GNATFinder is a software tool for extracting graphical neural activity threads (GNATs) from spiking neural networks. GNATFinder efficiently extracts GNATs from spiking neural network data. A GNAT is a graph that represents the causal relationships between individual spikes in a spiking network using a combination of the network connectivity structure and the timing of individual spikes. The software allows for identifying similar, repeating threads that often reappear in a dataset. Using quadtree data structures to recursively subdivide the time interval over which spikes were produced by the spiking network, GNATFinder allows for efficient pairwise comparisons of spike times to determine the degree of causal relatedness among all pairs of spikes in a dataset. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2024

sf-24-103

pyzag is a library for efficiently training generic models defined with a recursive nonlinear function. We developed it primarily to provide an efficient means to train NEML2 models it can be used to train other, more general types of models.

Messner, Mark [Argonne National Laboratory (ANL), ↗

DOE OSTI2025

Autonomous Anomaly Detection For Continuous Streams

The code implements the Isolation Forest (IFML) algorithm within the digital twin (DT) of the AGN-201 nuclear reactor. The DT captures real-time operational data including control rod positions, reactor power, and temperature. The IFML model isolates anomalies by detecting patterns that deviate from expected operational behavior. The algorithm recursively partitions the data and assigns anomaly scores based on the isolation of rare and different events. By tuning parameters specific to the reactor’s operational data, the IFML identifies deviations such as unauthorized material insertions or reactor reactivity shifts. The system streams data using LabView and integrates with the DeepLynx data warehouse for anomaly processing.

Trevino, Eduardo↗

DOE OSTI2025

LLaMP v0.1.0

Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. LLaMP is a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning and acting (ReAct) agents that can dynamically and recursively interact with Materials Project to ground large language models on high-fidelity materials informatics.

Riebesell, Janosh [Lawrence Berkeley National Labo↗

DOE OSTI2026

Multithreaded copy ('cp')

This is a modification to 'cp' and 'mv' commands to make them multi-threaded. Simple benchmarks showed that multi-threading could reduce the time to copy a large Linux source directory by over 2x. The 'cp' and 'mv' utilities are part of the existing Coreutils (https://www.gnu.org/software/coreutils/) software package that get installed on all Linux distros. Changes: * Add '-j|--parallel ' flags to 'cp' and 'mv'. This allows the utilities to recursively copy regular files in directories in parallel. This does NOT parallelize multiple single file copies to a destination (like 'cp file2 file2 file3 dst/'). Along with this, add in new 'CP_NUM_THREADS' and 'MV_NUM_THREADS' environment variables to set the number of threads. This can be useful when you want to enable parallelism by default in /etc/profile. The maximum number of threads is internally capped to the number of CPUs. * Add a '-j' flag to 'sort' to complement its existing '--parallel' flag. This is only done for consistency with 'cp' and 'mv'. * Add test cases for the new flags. Also, run each 'cp' and 'mv' test both in single-threaded and multithreaded modes for extra coverage.

Hutter, AnthonyJ [Lawrence Livermore National Labo↗

DOE OSTI2024

Non-smooth Bayesian optimization in tuning scientific applications

Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Here, motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.

97 MATHEMATICS AND COMPUTING↗