Computational design of nanoclusters by property-based genetic algorithms: Tuning the electronic properties of ( TiO 2 ) n clusters
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Bhattacharya, Saswata↗
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Quantum Flow Algorithms for Simulating Many-Body Systems on Quantum Computers
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Kowalski, Karol↗
Mapping renormalized coupled cluster methods to quantum computers through a compact unitary representation of nonunitary operators
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97 MATHEMATICS AND COMPUTING↗
Ground-State Preparation and Energy Estimation on Early Fault-Tolerant Quantum Computers via Quantum Eigenvalue Transformation of Unitary Matrices
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
COMPUTATION OF INVARIANT TORI AND ACCELERATION OF THE KAM ALGORITHM
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Warnock, R.↗
Uncertainty Quantification with MCMC method and BFBT Data
The reliability of predictions of the system codes is closely related to the validation of their physical models. For example, the accuracy of void fraction prediction in a Boiling Water Reactor (BWR) using TRACE code depends on the uncertainties of closure relations developed for the two-phase flow models. This work focus on quantifying the uncertainties of two physical models, interfacial drag model and subcooled boiling model, using Markov Chain Monte Carlo (MCMC) method and BFBT experimental benchmark data. We applied the MCMC and MLE algorithms to BFBT benchmark data and estimated the uncertainty distribution of two model parameters: subcooled boiling heat transfer coefficient and interfacial drag (bubbly/slug rod bundle) coefficient. These algorithms tend to give various estimation results. Despite the differences in these algorithms, we find that the estimation results are consistent. After obtaining the uncertainty estimates, we validated the uncertainty estimates by applying the estimated distributions of model parameters to TRACE prediction. The new predictions succeed in reducing the void fraction prediction error, but it is not possible to eliminate all prediction error. We are only adjusting two model parameters, and there exists uncertainties in all model parameters. Application of the algorithms to other physical models and experimental data will be helpful for future work. (authors)
42 ENGINEERING↗
An evaluation of point-insertion sequences for incremental Delaunay tessellations
Currently, incremental algorithms may be seen as the lowest-cost computational methods to generate Delaunay tessellations in several point distributions. In this work, eight point-insertion sequences in incremental algorithms for generating Delaunay tessellations are evaluated. More specifically, four point-insertion sequences in incremental algorithms for generating Delaunay tessellations are proposed: with orders given by the red–black tree with in-order and level-order traversals, spiral ordering, and H-indexing. These four incremental algorithms with such sequences are compared with four incremental algorithms with point-insertion orders given by the following sequences: the Hilbert and Lebesgue curves, cut-longest-edge kd-tree, and random order. Six 2-D and seven 3-D point distributions are tested, with sets ranging from 25,000 to 8,000,000 points. The results of computational and storage costs of these eight algorithms are analyzed. It follows that the incremental algorithm with a point-insertion sequence in the order given by the cut-longest-edge kd-tree shows the lowest computational and storage costs of the sequences tested.
97 MATHEMATICS AND COMPUTING↗
Quantum multi-programming for Grover’s search
Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP algorithm for Grover's search. Our algorithm decomposes Grover's algorithm by the partial diffusion operator and executes the decomposed circuits in parallel by QMP. We proved that this new algorithm increases the rotation angle of the Grover operator which, as a result, increases the success probability. The new algorithm is implemented on IBM quantum computers and compared with the canonical Grover's algorithm and other variations of Grover's algorithms. So, the empirical tests validate that our new algorithm outperforms other variations of Grover's algorithms as well as the canonical Grover's algorithm.
97 MATHEMATICS AND COMPUTING↗
Multi-Level Optimal Power Flow Solver in Large Distribution Networks: Preprint
Solving optimal power flow (OPF) problem for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution networks of tree topology with deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi-level implementation of the primal-dual gradient algorithm for solving the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verifies that the proposed algorithm can significantly improve computational speed without compromising any optimality.
41 EE - Solar Energy Technologies Office (EE-4S)↗
A scalable algorithm for the optimization of neural network architectures
In this work, we propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.
97 MATHEMATICS AND COMPUTING↗
Agreement Among RTOG Sarcoma Radiation Oncologists in Contouring Suspicious Peritumoral Edema for Preoperative Radiation Therapy of Soft Tissue Sarcoma of the Extremity
Purpose: Peritumoral edema may harbor sarcoma cells. The extent of suspicious edema (SE) included in the treatment volume is subject to clinical judgment, balancing the risk of missing tumor cells with excess toxicity. Our goal was to determine variability in SE delineation by sarcoma radiation oncologists (RO). Methods and Materials: Twelve expert ROs were provided with T1 gadolinium and T2-weighted MR images of 10 patients with high-grade extremity soft-tissue sarcoma. Gross tumor volume, clinical target volume (CTV)3cm (3 cm longitudinal and 1.5 cm radial margin), and CTV2cm (2 cm longitudinal and 1 cm radial margin) were contoured by a single observer. Suspicious peritumoral edema, defined as abnormal signal on T2 images, was independently delineated by all 12 ROs. Contouring agreement was analyzed using the simultaneous truth and performance level estimation (STAPLE) algorithm and kappa statistics. Results: The mean volumes of GTV, CTV2cm, and CTV3cm were, respectively, 130 cm{sup 3} (7-413 cm{sup 3}), 280 cm{sup 3} and 360 cm{sup 3}. The mean consensus volume computed using the STAPLE algorithm at 95% confidence interval was 188 cm{sup 3} (24-565 cm{sup 3}) with a substantial overall agreement corrected for chance (mean kappa = 0.71; range: 0.32-0.87). The minimum, maximum, and mean volume of SE (excluding the GTV) were 4, 182, and 58 cm{sup 3} (representing a median of 29% of the GTV volume). The median volume of SE not included in the CTV2cm and in the CTV3cm was 5 and 0.3 cm{sup 3}, respectively. There were 3 large tumors with >30 cm{sup 3} of SE not included in the CTV3cm volume. Conclusion: Despite the fact that SE would empirically seem to be a more subjective volume, a substantial or near-perfect interobserver agreement was observed in SE delineation in most cases with high-grade soft-tissue sarcomas of the extremity. A median of 97% of the consensus SE is within the CTV2cm (99.8% within the CTV3cm). In a minority of cases, however, significant expansion of the CTVs is required to cover SE.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Linear Solver for Electromagnetic Simulation of General Distribution Feeders
High-fidelity electromagnetic transient (EMT) modeling is required for accurate simulation and analysis of power system dynamics in modern distribution feeders. However, the high-fidelity of EMT models often leads to significant computational challenges, particularly in terms of computational resources and simulation time. This paper investigates the development and application of a detailed EMT model for general distribution feeders, with a focus on improving computational efficiency. A direct linear solver is proposed for a bordered block diagonal (BBD) matrix structure commonly encountered in a EMT model of distribution feeders. The solver integrates the Schur complement method with the block tridiagonal matrix algorithm to enhance the computational performance. The proposed solver is validated using the primary feeder of the IEEE 342-node test system, demonstrating its accuracy and efficiency in EMT simulations. Furthermore, the solver’s performance is benchmarked against MATLAB’s built-in linear solvers, showing significant improvements in computation time while maintaining high fidelity and accuracy in simulation results.
Choi, Jongchan [ORNL] (ORCID:000000025952455X)↗
Mana and thermalization: Probing the feasibility of near-Clifford Hamiltonian simulation
Quantum hydrodynamics is the emergent classical dynamics governing transport of conserved quantities in generic strongly interacting quantum systems. Here, recent matrix product operator methods [1,2] have made simulations of quantum hydrodynamics in 1+1D tractable, but they do not naturally generalize to 2+1D or higher, and they offer limited guidance as to the difficulty of simulations on quantum computers. Near-Clifford simulation algorithms are not limited to one dimension, and future error-corrected quantum computers will likely be bottlenecked by non-Clifford operations. We therefore investigate the non-Clifford resource requirements for simulation of quantum hydrodynamics using mana, a resource theory of non-Clifford operations. For infinite-temperature starting states, we find that the mana of subsystems quickly approaches zero, while for starting states with energy above some threshold the mana approaches a nonzero value. Surprisingly, in each case the finite-time mana is governed by the subsystem entropy, not the thermal state mana; we argue that this is because mana is a sensitive diagnostic of finite-time deviations from canonical typicality.
1-dimensional spin chains↗
Track reconstruction as a service for collider physics
Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms have been adapted to run on massively parallel coprocessors, such as graphics processing units (GPUs), to reduce processing time. Nevertheless, challenges remain in fully harnessing the computational capacity of coprocessors in a scalable and non-disruptive manner. This paper proposes an inference-as-a-service approach for particle tracking in high energy physics experiments. To evaluate the efficacy of this approach, two distinct tracking algorithms are tested: Patatrack, a rule-based algorithm, and Exa.TrkX, a machine learning-based algorithm. The as-a-service implementations show enhanced GPU utilization and can process requests from multiple CPU cores concurrently without increasing per-request latency. The impact of data transfer is minimal and insignificant compared to running on local coprocessors. This approach greatly improves the computational efficiency of charged particle tracking, providing a solution to the computing challenges anticipated in the High-Luminosity LHC era.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs performed on hundreds of thousands of processors generating trillions of nonzeros in the output matrix. Distributed SpGEMM at this extreme scale faces two key challenges: (1) high communication cost and (2) inadequate memory to generate the output. Furthermore, we address these challenges with an integrated communication-avoiding and memory-constrained SpGEMM algorithm that scales to 262,144 cores (more than 1 million hardware threads) and can multiply sparse matrices of any size as long as inputs and a fraction of output fit in the aggregated memory. As we go from 16,384 cores to 262,144 cores on a Cray XC40 supercomputer, the new SpGEMM algorithm runs 10x faster when multiplying large-scale protein-similarity matrices.
97 MATHEMATICS AND COMPUTING↗
Hybrid quantum-classical approach for coupled-cluster Green's function theory
The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of time evolution and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system's eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green's function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to many models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grows as O ( N 5 ) per time step to calculate the impurity Green's function in the time domain, where N is the total number of energy levels in the AIM. Since the number of T gates is analogous to the computational time complexity of a classical simulation, we achieve an order of magnitude improvement over a classical CCGF calculation of the same order, which requires O ( N 6 ) computational resources per time step.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
High Fidelity CFD Simulations Supporting the KP-FHR
Kairos Power, LLC, is developing its version of the Fluoride-cooled High-temperature Reactor, the KP-FHR. The design uses a pebble bed core with fluoride salt as a coolant. The pebbles used in the KP-FHR have a diameter of 4 cm, with a shell fuel region where TRISO particles are embedded. A Pebble bed core design is adopted by several Gen IV reactors, They boast many benefits, such as fuel integrity, highly efficient heat transfer, and passive safety. However, it is challenging to accurately predict temperature and flow inside a pebble bed. Traditional approaches use the porous media model, which regards the pebble bed as a continuous medium, but with different temperature fields representing different levels, such as the fluid temperature, pebble surface temperature, and pebble center temperature. Empirical heat transfer correlations are adopted to calculate the heat transfer coefficient between different phases. However, empirical correlations are usually validated with experimental data, which usually lacks detail inside the pebble bed. The available experimental data is also generally at a high Reynolds number, which falls outside of the conditions of KP-FHR. Explicit computational fluid dynamics (CFD) simulations of randomly packed pebble beds have only become feasible recently. This is thanks to the rapid development of computational power and scalable algorithms. In this work, we used the Spectral Element Method (SEM) CFD code NekRS to simulate the randomly packed pebble bed in a cylindrical container. NekRS, which is the GPU variant of Nek5000, but refactored to utilize the computational power of GPUs using the OCCA library to run on hybrid architecture high performance computing systems. It was initially developed with the libParamunal library, but truncated and tuned for large-scale turbulence simulation. As a result, the SEM reaches higher precision with the same degrees of freedom by using a high-order Lagrange polynomial basis distributed on Gauss-Lobatto-Legendre quadrature inside each element, compared to lower-order methods, such the Finite Volume Method and Finite Element Method. The report is divided into five parts. We start with a general discussion of the pebble bed reactor, along with a specific investigation into the KP-FHR. The second part presents the numerical methodology. In the third part, we study a modular pebble bed with 1741 pebbles in a container of 7 pebble-diameter radius. Beyond LES simulations done by NekRS, we also leveraged the thermal radiation model in OpenFOAM to study heat transfer under no-forced-flow scenarios. Then, in the fourth part we simulated a pebble bed similar to the size of the Hermes Test Reactor. The total number of pebbles is in these simulations is 34,374. The container radius is 14 pebble-diameters. Finally, the report concludes in part five, with a discussion of future work.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Bringing heterogeneity to the CMS software framework
The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗