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HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark
We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.
Unveiling the Universe with emerging cosmological probes
The detection of the accelerated expansion of the Universe has been one of the major breakthroughs in modern cosmology. Several cosmological probes (Cosmic Microwave Background, Supernovae Type Ia, Baryon Acoustic Oscillations) have been studied in depth to better understand the nature of the mechanism driving this acceleration, and they are being currently pushed to their limits, obtaining remarkable constraints that allowed us to shape the standard cosmological model. In parallel to that, however, the percent precision achieved has recently revealed apparent tensions between measurements obtained from different methods. These are either indicating some unaccounted systematic effects, or are pointing toward new physics. Following the development of CMB, SNe, and BAO cosmology, it is critical to extend our selection of cosmological probes. Novel probes can be exploited to validate results, control or mitigate systematic effects, and, most importantly, to increase the accuracy and robustness of our results. This review is meant to provide a state-of-art benchmark of the latest advances in emerging “beyond-standard” cosmological probes. We present how several different methods can become a key resource for observational cosmology. In particular, we review cosmic chronometers, quasars, gamma-ray bursts, standard sirens, lensing time-delay with galaxies and clusters, cosmic voids, neutral hydrogen intensity mapping, surface brightness fluctuations, stellar ages of the oldest objects, secular redshift drift, and clustering of standard candles. The review describes the method, systematics, and results of each probe in a homogeneous way, giving the reader a clear picture of the available innovative methods that have been introduced in recent years and how to apply them. The review also discusses the potential synergies and complementarities between the various probes, exploring how they will contribute to the future of modern cosmology.
Beam test performance studies of CMS Phase-2 Outer Tracker module prototypes
A new tracking detector will be installed as part of thePhase-2 upgrade of the CMS detector for the high-luminosity LHC era.This tracking detector includes the Inner Tracker, equipped withsilicon pixel sensor modules, and the Outer Tracker, consisting ofmodules with two parallel stacked silicon sensors. The Outer Trackerfront-end ASICs will be able to correlate hits from chargedparticles in these two sensors to perform on-module discriminationof transverse momenta (p$_{T}$). The p$_{T}$information is generated at a frequency of 40 MHz and will be usedin the Level-1 trigger decision of CMS. Prototypes of theso-called 2S modules were tested at the Test Beam Facility at DESYHamburg between 2019 and 2020. These modules use the finalfront-end ASIC, the CMS Binary Chip (CBC), and for the firsttime the Concentrator Integrated Circuit (CIC), optical readoutand on-module power conversion. In total, seven modules were tested,one of which was assembled with sensors irradiated with protons. Animportant aspect was to show that it is possible to read out modulessynchronously. A cluster hit efficiency of about 99.75 % wasachieved for all modules. The CBC p$_{T}$ discriminationmechanism has been verified to work together with the CIC andoptical readout. The measured module performance meets therequirements for operation in the upgraded CMS tracking detector.
On the potentially transformative role of auxiliary-field quantum Monte Carlo in quantum chemistry: A highly accurate method for transition metals and beyond
Approximate solutions to the ab initio electronic structure problem have been a focus of theoretical and computational chemistry research for much of the past century, with the goal of predicting relevant energy differences to within “chemical accuracy” (1 kcal/mol). For small organic molecules, or in general, for weakly correlated main group chemistry, a hierarchy of single-reference wave function methods has been rigorously established, spanning perturbation theory and the coupled cluster (CC) formalism. For these systems, CC with singles, doubles, and perturbative triples is known to achieve chemical accuracy, albeit at O(N7) computational cost. In addition, a hierarchy of density functional approximations of increasing formal sophistication, known as Jacob’s ladder, has been shown to systematically reduce average errors over large datasets representing weakly correlated chemistry. However, the accuracy of such computational models is less clear in the increasingly important frontiers of chemical space including transition metals and f-block compounds, in which strong correlation can play an important role in reactivity. A stochastic method, phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), has been shown to be capable of producing chemically accurate predictions even for challenging molecular systems beyond the main group, with relatively low O(N3 − N4) cost and near-perfect parallel efficiency. Herein, we present our perspectives on the past, present, and future of the ph-AFQMC method. We focus on its potential in transition metal quantum chemistry to be a highly accurate, systematically improvable method that can reliably probe strongly correlated systems in biology and chemical catalysis and provide reference thermochemical values (for future development of density functionals or interatomic potentials) when experiments are either noisy or absent. Finally, we discuss the present limitations of the method and where we expect near-term development to be most fruitful.
Multipoint Observations of Dispersive Scale Alfvénic Field‐Line Resonances Associated With Substorm Auroral Beads
We present a case study of the field-aligned current (FAC) systems that transpire within the high-altitude auroral acceleration region of an “auroral bead” initiated double oval substorm observed on 23 February 2001 by the Cluster fleet. Conjunctive Cluster measurements and auroral images from IMAGE reveal that auroral bead current system formation and evolution is a multi-scale, injection-mediated process. The FACs at large scales vary on substorm evolution time scales (~minutes) in response to the injection and evolution of hotter denser magnetospheric plasma. Embedded within the large-scale FACs are intense short-scale (≲ few 10s of km) currents comprising dispersive scale Alfvén wave (DAW) fluctuations. The DAWs are a complex mixture of ingoing and reflected components that regularly interfere to form a broad spectrum of kinetic (dispersive) scale Alfvénic field-line resonances (KFLRs). The Alfvénic currents appear as a nested series of upward and downward FAC densities with amplitudes reaching a few 100 nA/m 2 . Energized field-aligned or counterstreaming electrons near keV energies and below are observed with parallel skews that vary in concert with variations in the DAW current sense. Positive correlations between DAW electric field energy densities and the energies of energized H + , He + , and O + outflow are observed, indicative of ion energization within the DAW fields. Due to their L-shell location (L~5.8–7.0) and associations with injections, the KFLRs are interpreted as the high-altitude auroral zone analog of KFLRs observed in the equatorial inner magnetosphere.
3-D Semantic Information Inference from Airborne Video. Final Report
We developed a full 3D modeling pipeline focusing on an important application scenario: city-scale 3D reconstruction from aerial imagery. We propose an approach to solve camera pose estimation and dense reconstruction from Wide Area Aerial Surveillance (WAAS) videos captured by an airborne platform. Our approach solves them in an online fashion: it incrementally updates a sparse 3D map and estimates the camera pose as each new frame arrives; depth maps of selected key frames are computed using a variational method and integrated to produce a full 3D model via volumetric reconstruction. In practice, aerial imagery is usually captured using a multicamera system. We propose an approach for camera pose estimation of multi-camera aerial imagery which is parallelized on multiple GPUs for efficiency. The approach is also extended for progressive 3D model acquisition with a hand-held camera. We also developed an offline approach since online approach is not a necessity and accuracy has higher priority over efficiency in many scenarios. We presented MeshRecon, a mesh-based offline system composed of three modules: a dense point cloud is generated using multi-resolution plane sweep method; an initial mesh model is extracted from the point cloud via global optimization considering visibility information of all images; the mesh model is then iteratively refined to capture structural details by optimizing the photometric consistency and spatial regularization. The major processes are parallelized on GPU for efficiency. For the aerial imagery case, we evaluate our system on several real-world multicamera aerial imagery datasets, each covering an urban scenario of several square kilometers. Quantitative result shows that the reconstructed geometric 3D model is highly accurate with error smaller than 1 meter over the entire city. Besides aerial imagery, we also evaluate its performance on general geometric 3D model acquisition of real-world objects. Result shows that the system is robust and flexible for various types of objects at different scales in both indoor and outdoor environments. Based on city 3D models reconstructed at different times, we present a system for city-scale geometry change detection by performing comparisons at the 3D geometry level. Our system is able to detect geometry changes at different scales, ranging from a building cluster to small-scale vegetation changes, with high accuracy.
Fluctuation Dynamo in a Collisionless, Weakly Magnetized Plasma
Results from a numerical study of fluctuation dynamo in a collisionless, weakly magnetized plasma are presented. The key difference between this dynamo and its magnetohydrodynamic (MHD) counterpart is the adiabatic production of magnetic-field-aligned pressure anisotropy by the amplification of a weak seed field. This, in turn, drives kinetic instabilities on the ion-Larmor scale—namely, firehose and mirror—which sever the adiabatic link between the thermal and magnetic pressures, thereby allowing the dynamo to proceed. After an initial phase of rapid growth driven by these instabilities, the magnetic energy grows exponentially and exhibits a ${k}^{3/2}$ spectrum that peaks near the resistive scale, similar to the large-magnetic-Prandtl-number ($\mathrm{Pm}\gg 1$) MHD dynamo. The magnetic field self-organizes into a folded-sheet topology, with direction reversals at the resistive scale and field lines curved at the parallel scale of the flow. The effective $\mathrm{Pm}$ is determined by whether the ion-Larmor scale is above or below the field-reversing scale: in the former case, particles undergo Bohm-like diffusion; in the latter case, particles scatter primarily off of firehose fluctuations residing at the ends of the magnetic folds, and the viscosity becomes anisotropic. The magnetic field ultimately saturates at dynamical strengths, with its spectral peak migrating toward larger scales. This feature, along with an anti-correlation of magnetic-field strength and field-line curvature and a gradual thinning of magnetic sheets into ribbons, resembles the saturated state of the large-$\mathrm{Pm}$ dynamo, the primary differences manifesting in firehose/mirror-unstable regions. Furthermore, these results have implications for magnetic-field growth in the weakly collisional intracluster medium of galaxy clusters.
The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: N -body mock challenge for the quasar sample
ABSTRACT The growth rate and expansion history of the Universe can be measured from large galaxy redshift surveys using the Alcock–Paczynski effect. We validate the Redshift Space Distortion models used in the final analysis of the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 quasar clustering sample, in configuration and Fourier space, using a series of halo occupation distribution mock catalogues generated using the OuterRim N-body simulation. We test three models on a series of non-blind mocks, in the OuterRim cosmology, and blind mocks, which have been rescaled to new cosmologies, and investigate the effects of redshift smearing and catastrophic redshifts. We find that for the non-blind mocks, the models are able to recover fσ8 to within 3 per cent and α∥ and α⊥ to within 1 per cent. The scatter in the measurements is larger for the blind mocks, due to the assumption of an incorrect fiducial cosmology. From this mock challenge, we find that all three models perform well, with similar systematic errors on fσ8, α∥, and α⊥ at the level of $\sigma _{f\sigma _8}=0.013$, $\sigma _{\alpha _\parallel }=0.012$, and $\sigma _{\alpha _\bot }=0.008$. The systematic error on the combined consensus is $\sigma _{f\sigma _8}=0.011$, $\sigma _{\alpha _\parallel }=0.008$, and $\sigma _{\alpha _\bot }=0.005$, which is used in the final DR16 analysis. For baryon acoustic oscillation fits in configuration and Fourier space, we take conservative systematic errors of $\sigma _{\alpha _\parallel }=0.010$ and $\sigma _{\alpha _\bot }=0.007$.
AI4IO: A suite of AI-based tools for IO-aware scheduling
Traditional workload managers do not have the capacity to consider how IO contention can increase job runtime and even cause entire resource allocations to be wasted. Whether from bursts of IO demand or parallel file systems (PFS) performance degradation, IO contention must be identified and addressed to ensure maximum performance. In this paper, we present AI4IO (AI for IO), a suite of tools using AI methods to prevent and mitigate performance losses due to IO contention. AI4IO enables existing workload managers to become IO-aware. Currently, AI4IO consists of two tools: PRIONN and CanarIO. PRIONN predicts IO contention and empowers schedulers to prevent it. CanarIO mitigates the impact of IO contention when it does occur. We measure the effectiveness of AI4IO when integrated into Flux, a next-generation scheduler, for both small- and large-scale IO-intensive job workloads. Our results show that integrating AI4IO into Flux improves the workload makespan up to 6.4%, which can account for more than 18,000 node-h of saved resources per week on a production cluster in our large-scale workload.
Anomalous thickness-dependent electrical conductivity in van der Waals layered transition metal halide, Nb 3 Cl 8
Understanding the electronic transport properties of layered, van der Waals transition metal halides (TMHs) and chalcogenides is a highly active research topic today. Of particular interest is the evolution of those properties with changing thickness as the 2D limit is approached. Here, we present the electrical conductivity of exfoliated single crystals of the TMH, cluster magnet, Nb 3 Cl 8 , over a wide range of thicknesses both with and without hexagonal boron nitride (hBN) encapsulation. The conductivity is found to increase by more than three orders of magnitude when the thickness is decreased from 280 µm to 5 nm, at 300 K. At low temperatures and below ~50 nm, the conductance becomes thickness independent, implying surface conduction is dominating. Temperature dependent conductivity measurements indicate Nb 3 Cl 8 is an insulator, however, the effective activation energy decreases from a bulk value of 310 meV to 140 meV by 5 nm. X-ray photoelectron spectroscopy (XPS) shows mild surface oxidation in devices without hBN capping, however, no significant difference in transport is observed when compared to the capped devices, implying the thickness dependent transport behavior is intrinsic to the material. A conduction mechanism comprised of a higher conductivity surface channel in parallel with a lower conductivity interlayer channel is discussed.
The evolution of model Rh/Fe 3 O 4 (001) catalysts in hydrogen environments
Single metal atoms dispersed on oxides are a new emerging class of catalysts owing to their unique electronic and chemical properties. Here, in this study, we have prepared a series of model single-atom catalysts possessing well-characterized Rh sites that include Rh adatoms (Rh ad ), mixed surface layers with octahedrally-coordinated Rh (Rh oct ), as well as metallic Rh clusters and nanoparticles (Rh met ) on Fe 3 O 4 (001). Using X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM), we investigated the activity of such model systems towards H2 and their stability in reducing environments. Our results show that the atomically dispersed Rhad and Rhoct species do not activate H 2 , which would result in the formation of surface hydroxyls on Fe 3 O 4 (001). In contrast, the presence of Rh met in H 2 results in the formation of hydroxyls and subsequent etching of the Fe 3 O 4 (001) at higher temperatures (≥ 500 K) due to water formation via the Mars-van Krevelen mechanism. Additionally, such surface etching leads to the release of the Rh oct from the surface lattice and their sintering to Rh met . To bridge the material gap between the surface science models and high surface area catalysts, we perform parallel studies on powder Rh/Fe 3 O 4 catalysts. The XPS characterization shows remarkable similarities between these systems. Further, our surface science studies provide an atomistic picture of the behavior of high surface area catalysts in the H 2 atmosphere.
Intermolecular correlations of liquid and glassy CS 2 studied by synchrotron radiation x-ray diffraction
How is the orientation of molecular liquids ordered on cooling? What are the basic structures of molecular glasses, e.g., close to the crystalline structure or some special structures such as icosahedral cluster? These are long-standing questions in liquid and glass physics. Here, we have constructed a novel cryostat to prepare simple molecular glasses by vapor deposition and performed in situ synchrotron radiation x-ray diffraction experiments. The glassy state of a simple molecule CS 2 , which cannot be vitrified by normal liquid quenching, was successfully prepared with this instrument, and its diffraction data were collected in a wide Q-range of 0.16–25.7 Å -1 with a high-energy diffractometer at BL04B2, SPring-8. The diffraction data of liquid CS 2 were also recorded in a wide temperature range of 160–300 K. These diffraction data were analyzed with molecular dynamics simulations and reverse Monte Carlo modelings to investigate orientational correlation. From the obtained 3D structure models, the orientational correlation between neighboring CS 2 molecules was investigated quantitatively as a function of temperature. At room temperature, the parallel and T-shaped arrangements are preferred for the nearest neighbor correlation. On cooling, these arrangements are developed gradually, and its rate became prominent below the melting temperature (162 K). In the glassy state, the slipped-parallel arrangement is dominant as well as the T-shaped arrangement. Both arrangements appear in the CS 2 crystal, indicating that the structure of glassy CS 2 is close to that of crystalline CS 2 .
ConnectIt: a framework for static and incremental parallel graph connectivity algorithms
Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.
3-D Simulations of earthquakes rupture jumps: 1. Homogeneous pre-stress conditions
SUMMARY Observational and modelling studies indicate that earthquake ruptures can jump between fault sections as large as ∼3 and ∼5 km for compressional and extensional offsets, respectively. Here, we compare characteristics of the rupture jump process on parallel but offset fault sections from traditional 3-D dynamic rupture simulations governed by slip weakening friction using the finite element code, FaultMod, to those from quasi-dynamic simulations governed by rate- and state-dependent friction (rate-state friction) using the code RSQSim. These simulations use spatially uniform initial stresses. For a variety of measures the rupture renucleation position on the offset fault, the rate-state friction and slip weakening friction models produce very similar results. The principal difference is the additional occurrence of delayed rupture jumps that arise from the time- and stress-dependent nucleation that is characteristic of rate-state friction. For immediate rupture jumps, models with slip weakening friction span greater offsets than those with rate-state friction. However the jump distances are nearly identical when delayed rupture jumps are included in the comparisons. We propose that delayed rupture jumps are the likely mechanism for adjacent large-earthquake pairs and clusters. Based on the similarity of renucleation positions with both dynamic and quasi-dynamic models, we conclude that the renucleation positions for rupture initiation on the receiver fault (separated by less than ∼3 km from the source fault) are primarily controlled by static stress changes induced by slip on the initiating fault. However, in light of the slightly greater maximum jump distances (>3 km) seen with the dynamic slip weakening friction model, dynamic stress changes from seismic waves play an increasingly important role as offset distances increase.
Newton trust-region methods with primary variable switching for simulating high temperature multiphase porous media flow
Coupling multiphase flow with energy transport due to high temperature heat sources introduces significant new challenges since boiling and condensation processes can lead to dry-out conditions with subsequent re-wetting. The transition between two-phase and single-phase behavior can require changes to the primary dependent variables adding discontinuities as well as extending constitutive nonlinear relations to extreme physical conditions. Practical simulations of large-scale engineered domains lead to Jacobian systems with a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. Performance assessment of potential nuclear repositories, carbon sequestration sites and geothermal reservoirs can require numerous Monte-Carlo simulations to explore uncertainty in material properties, boundary conditions, and failure scenarios. Due to the numerical challenges, standard NR iteration may not converge over the range of required simulations and require more sophisticated optimization method like trust-region. In this study, we use the open-source simulator PFLOTRAN for the important practical problem of the safety assessment of future nuclear waste repositories in the U.S. DOE geologic disposal safety assessment Framework. The simulator applies the PETSc parallel framework and a backward Euler, finite volume discretization. We demonstrate failure of the conventional NR method and the success of trust-region modifications to Newton’s method for a series of test problems of increasing complexity. Trust-region methods essentially modify the Newton step size and direction under some circumstances where the standard NR iteration can cause the solution to diverge or oscillate. Furthermore, we show how the Newton Trust-Region method can be adapted for Primary Variable Switching (PVS) when the multiphase state changes due to boiling or condensation. The simulations with high-temperature heat sources which led to extreme nonlinear processes with many state changes in the domain did not converge with NR, but they do complete successfully with the trust-region methods modified for PVS. This implementation effectively decreased weeks of simulation time needing manual adjustments to complete a simulation down to a day. Finally, we show the strong scalability of the methods on a single node and multiple nodes in an HPC cluster.
Comparison of Distributive Consensus Algorithms for AnomalyDetection on the Power-Grid
As an increasing number of devices in the power-grid rely on high-precision clocks to measure electrical properties and time stamp data, GPS receivers have become a vulnerability on the power-grid. GPS spoofing attacks, in which a fake signal is transmitted, can cause sensor measurement errors that can misinform protective equipment and grid operators for both the transmission and distribution grid. The technology being developed to secure precision clocks in the transmission grid are not appropriate for the distribution grid. Thus, there is a need to developed anomaly detection algorithms that are suitable for the hardware available on the distribution grid. Traditionally, a distributed detection framework is comprised of spatially distributed nodes which acquire observations about a quantity of interest and sends the data to the fusion center, where a global decision is made. Instead the distribution grid has low-power devices, such as smart inverters and microPMUs, that could be used to perform highly parallelized calculations. Having on node calculations is beneficial since it is more network and computation efficient, as the more calculations can be performed where the data is being captured instead of communicating across a slow network to a centralized fusion center. Instead, we could employ an alternate peer-to-peer type of information exchange on the distribution grid that can still reach a global decision. A decentralized approach to anomaly detection makes efficient use of the computational and network bandwidth that exists, and it also lacks the single point of failure of a centralized approach. Thus, a decentralized local information exchange and/or inference is preferred. Lastly, the unreliability of the distribution grid network connections and potential noise or error in the measurements must be considered. In order to produce an effective detection tool, a Byzantine-fault tolerant and asynchronous algorithm must be developed. Thus, the first phase of our project is to develop a distributed, decentralized, asynchronous, Byzantine-fault tolerant, collaborative autonomy-based anomaly detection algorithm to collectively analyze clusters of data for signs of a malicious attack.
Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster
Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.