CPU Hours Research Articles

Mesh adaptation and dynamic positioning for the efficient simulation of lifting hydrofoil flows

Accurate simulations of the flow around lifting hydrofoils are challenging, since they need to capture the flow near the foil surface precisely, represent the free surface, and take into account body motion and deformation. Therefore, these simulations are often computationally expensive.This paper studies numerical methods to limit the costs of hydrofoil simulation. Mesh adaptation is used to efficiently capture the free-surface flow, to resolve flow details around the foil surface, and to ensure the accuracy of mesh motion techniques, like overset meshing. For maximum precision of the boundary-layer flow, adaptation is started from dedicated body-aligned meshes.Hydrofoil flexibility is taken into account through a linear eigenmode-based reduced-order model of the structural response. This approach removes the need to couple directly the fluid and structure solvers and reduces the computational overhead for fluid–structure simulation. Equilibrium positions for flexible and rigid motion are determined with a fixed-point iteration based on quasi-Newton and approximate models for the forces respectively, which eliminate the need for costly time-accurate simulation.Test cases demonstrate that these methods work together, providing accurate simulations of realistic hydrofoils with reasonable computational costs. Mesh adaptation allows to target a specific numerical uncertainty through the refinement threshold parameter. Together with the body-aligned ‘sock’ meshes, adaptive refinement produces the same accuracy as non-adapted meshes for up to 10 times less CPU hours. Both fluid–structure interaction methods lead to computations which have the same convergence speeds as for non-moving bodies. Thus, foil flexibility can be simulated for a computational overhead below 40% and it leads to significantly better agreement with experiments.

Detecting transposable elements in long-read genomes using sTELLeR.

Repeat elements, such as transposable elements (TE), are highly repetitive DNA sequences that compose around 50% of the genome. TEs such as Alu, SVA, HERV, and L1 elements can cause disease through disrupting genes, causing frameshift mutations or altering splicing patters. These are elements challenging to characterize using short-read genome sequencing, due to its read length and TEs repetitive nature. Long-read genome sequencing (lrGS) enables bridging of TEs, allowing increased resolution across repetitive DNA sequences. lrGS therefore present an opportunity for improved TE detection and analysis not only from a research perspective but also for future clinical detection. When choosing an lrGS TE caller, parameters such as runtime, CPU hours, sensitivity, precision, and compatibility with inclusion into pipelines are crucial for efficient detection. We therefore developed sTELLeR, (s) Transposable ELement in Long (e) Read, for accurate, fast, and effective TE detection. Particularly, sTELLeR exhibit higher precision and sensitivity for calling of Alu elements than similar tools. The caller is 5-48× as fast and uses <2% of the CPU hours compared to competitive callers. The caller is haplotype aware and output results in a variant call format (VCF) file, enabling compatibility with other variant callers and downstream analysis. sTELLeR is a python-based tool and is available at https://github.com/kristinebilgrav/sTELLeR. Altogether, we show that sTELLeR is a fast, sensitive, and precise caller for detection of TE elements, and can easily be implemented into variant calling workflows.

FunFuzz : Greybox Fuzzing with Function Significance

Greybox fuzzing is dedicated to revealing software bugs by maximizing code coverage. Concentrating on code coverage, greybox fuzzing effectively exposes bugs in real-world programs by continuously executing the program under test (PUT) with the test inputs generated from initial seeds, making it a popular software testing technique. Although powerful, the effectiveness of greybox fuzzing can be restricted in some cases. Ignoring the significant degrees of executed functions, traditional greybox fuzzing usually fails to identify significant seeds that execute more significant functions, and thus may assign similar energy to significant and trivial seeds when conducting power scheduling. As a result, the effectiveness of greybox fuzzing can be degraded due to wasting too much energy on trivial seeds. In this paper, we introduce function significance (FS) to measure the significant degrees of functions. Our key insight is that the influential functions that connect to many other functions are significant to greybox fuzzing as they provide more probabilities to reach previously unexplored code regions. To quantify FS, we conduct influence analysis upon the call graphs extracted from the PUTs to obtain the centrality values of function nodes. With FS as the significance measurement, we further propose FunFuzz , an FS-aware greybox fuzzing technique, to optimize significant seeds and tackle the aforementioned restriction. To this end, FunFuzz dynamically tracks the functions executed by a seed during fuzzing, and computes the significance score for the seed by accumulating the FS values of the functions executed by it. Based on the computed FS values, FunFuzz then takes an estimation-based power scheduling to assign more (or less) energy to seeds that achieve over-estimated (or under-estimated) significance scores. Specifically, the seed energy is adjusted by multiplying with a scale factor computed regarding the ratio of the actual significance score achieved by executing the seed and the estimated significance score predicted by a linear model constructed on-the-fly. To evaluate FunFuzz , we prototype it on top of AFL++ and conduct experiments with 15 programs, of which 10 are from common real-world projects and five are from Magma, and compare it to seven popular fuzzers. The experimental results obtained through fuzzing exceeding 40,800 CPU hours show that: (1) In terms of covering code, FunFuzz outperforms AFL++ by achieving 0.1% \(\sim\) 18.4% more region coverage on 13 out of 15 targets. (2) In terms of finding bugs, FunFuzz unveils 114 unique crashes and 25 Magma bugs (which are derived from CVEs) in 20 trials of 24-hour fuzzing, which are the most compared to the competitor fuzzers and include 32 crashes and 1 Magma bug that the other fuzzers fail to discover. Besides the experiments focusing on code coverage and bug finding, we evaluate the key components of FunFuzz , namely the FS-centered estimation-based power scheduling and the lazy FS computation mechanism. The extensive evaluation not only suggests FunFuzz ’s superiority in code coverage and bug finding, but also demonstrates the effectiveness of the two components.

First-principles quantum Monte Carlo study of charge-carrier mobility in organic molecular semiconductors

We present a first-principles numerical study of charge transport in a realistic two-dimensional tight-binding model of organic molecular semiconductors. We use the hybrid Monte Carlo (HMC) algorithm to simulate the full quantum dynamics of phonons and either single or multiple charge carriers without any tunable parameters. We introduce a number of algorithmic improvements, including efficient Metropolis updates for phonon fields based on analytical insights, which lead to negligible autocorrelation times and allow sub-per-mille precisions to be reached at a low computational cost of O(1) CPU hours. Our simulations produce charge-mobility estimates that are in good agreement with experiments and that also justify the phenomenological transient localization approach. Published by the American Physical Society 2024

Development of a neural network model for peeling–ballooning stability analysis in the KSTAR tokamak pedestals

The neural network model, MISHKA-NN is developed to mitigate the computational burden associated with the linear ideal magnetohydrodynamic (MHD) stability analysis of the pedestal based on the peeling–ballooning (P–B) model. By utilizing both 1D plasma profiles (current density, pressure gradient, and safety factor) and 0D parameters (plasma geometry, total current, and toroidal mode number), the model predicts linear growth rate of edge-localized ideal MHD instability in a given equilibrium state. By enabling the prediction of each instability within a second, the model reduces the time required for plotting a pedestal P–B stability diagram ( j−α diagram) from approximately 100 CPU hours to a few CPU minutes. Notably, even with the utilization of parametric pressure and current profiles and plasma boundary shapes for the training dataset, the model shows a satisfactory level of performance in benchmarking the j−α diagram for the reconstructed equilibrium from a KSTAR tokamak experiment. We anticipate the model to serve as a versatile alternative to 2D linear MHD stability codes, alleviating numerical costs.

M3DIS - A grid of 3D radiation-hydrodynamics stellar atmosphere models for stellar surveys. I. Procedure, validation, and the Sun

Large-scale stellar surveys, such as SDSS-V, 4MOST, WEAVE, and PLATO, require accurate atmospheric models and synthetic spectra of stars for accurate analyses of fundamental stellar parameters and chemical abundances. The primary goal of our work is to develop a new approach to solve radiation-hydrodynamics (RHD) and generate model stellar spectra in a self-consistent and highly efficient framework. We build upon the Copenhagen legacy RHD code, the MULTI3D non-local thermodynamic equilibrium (NLTE) code, and the DISPATCH high-performance framework. The new approach allows us to calculate 3D RHD models of stellar atmospheres on timescales of a few thousand CPU hours and to perform subsequent spectrum synthesis in local thermodynamic equilibrium (LTE) or NLTE for the desired physical conditions within the parameter space of FGK-type stars. We compare the 3D RHD solar model with other available models and validate its performance against solar observations, including the centre-to-limb variation of intensities and key solar diagnostic lines of H and Fe. We show that the performance of the new code allows to overcome the main bottleneck in 3D NLTE spectroscopy and enables calculations of multi-dimensional grids of synthetic stellar observables for comparison with modern astronomical observations.

A stream-aligned mixed polyhedral meshing strategy for integrated surface-subsurface hydrological models

Mesh refinement near river corridors is commonly deployed to resolve topographic details important for accurately representing riverine flow dynamics in watershed-scale spatially distributed integrated hydrology models. However, this increased accuracy comes with higher computational costs due to larger mesh and smaller time steps resulting from small mesh cells. We present a novel stream-aligned mixed-polygonal meshing approach that resolves river-corridor topography efficiently by placing long quadrilateral cells along the mapped center lines of the rivers and standard Triangulated Irregular Network (TIN) mesh in the rest of the domain. Our integrated hydrology simulations using mixed-polyhedral mesh generated from the mixed-polygonal approach closely match those using a highly refined extruded-TIN mesh but with a 96.43% reduction in the number of mesh cells and a 99.75% reduction in CPU hours. Conversely, coarser extruded-TIN meshes, due to poor water conveyance relative to the refined mesh simulations, yielded lower peak flows, longer recession curves, and higher inundation fractions.The creation of this mixed-polyhedral mesh is implemented within the Watershed Workflow tool, a Python-based workflow library for watershed simulation. We also implemented a layered process of hydrologic conditioning specific to the cells in the river corridor, which are explicitly meshed within the mixed-polyhedral mesh. The hydrologic conditioning enforces monotonic topographic gradients, removes spurious obstructions, and allows for burning-in depths of the river cells based on ancillary information. This approach paves the way for efficiently modeling narrow engineered channels and specifying river-specific hydrodynamic details and biogeochemical processes.

Sesame: A power spectrum emulator pipeline for beyond-ΛCDM models

The mysterious nature of the dark sector of the Λ-cold-dark-matter (ΛCDM) model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. Here, we present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond ΛCDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, B(k, z) = Pbeyond − ΛCDM(k, z)/PΛCDM(k, z), and then rely on existing high-quality emulators made for ΛCDM to isolate Pbeyond − ΛCDM(k, z). Since both the ΛCDM and beyond-ΛCDM models are simulated in our approach, some of the lack of power on small scales due to the low force-resolution in the simulations is factored out, allowing us to extend the emulator to k ∼ 3 − 5 h Mpc−1 and still maintain good accuracy. In addition, errors from the simulation and emulation process can easily be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied f(R) model with massive neutrinos, using approximately 3000 CPU hours of computation time. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice.

DiPri : Distance-based Seed Prioritization for Greybox Fuzzing

Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique. In this paper, we propose a novel distance-based seed prioritization approach named DiPri to facilitate greybox fuzzing. Specifically, DiPri evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of DiPri , we prototype DiPri on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that DiPri can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate DiPri into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of DiPri . The results with the Java benchmark demonstrate that DiPri is pretty scalable and can help the host fuzzer find bugs more consistently.

Three-Dimensional Full-Core BEAVRS Using OpenMOC with Transport Equivalence

Using an optimized implementation of the three-dimensional (3D) method of characteristics for neutron transport, along with a novel equivalence method for transport calculations that was designed to correct self-shielding errors from neglecting the angular dependence of resonant group absorption, a 3D full-core light water reactor hybrid stochastic-deterministic eigenvalue calculation was achieved. This paper presents the optimizations developed and compares the transport solutions obtained. For the statepoint, run times near 10 000 CPU hours are achieved—improving on previous works by an order of magnitude—with near 1% error on pin fission to 238U capture ratios and a few dozen pcms on the eigenvalue.

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters.

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

Zero Metallicity with Zero CPU Hours: Masses of the First Stars on the Laptop

We develop an analytic model for the mass of the first stars forming in the centers of primordial gas clouds as a function of host halo mass, redshift, and degree of rotation. The model is based on the estimation of key timescales determining the following three processes: the collapse of the gas cloud, the accretion onto the protostellar core, and the radiative feedback of the protostellar core. The final stellar mass is determined by the total mass accreted until the radiative feedback halts the accretion. The analytic estimation, motivated by the result of the full numerical simulations, leads to algebraic expressions allowing an extremely fast execution. Despite its simplicity, the model reproduces the stellar mass scale and its parameter dependencies observed in state-of-the-art cosmological zoom-in simulations. This work clarifies the basic physical principles undergirding such numerical treatments and provides a path to efficiently calibrating numerical predictions against eventual observations of the first stars.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

A Graphical Multi-Fidelity Gaussian Process Model, with Application to Emulation of Heavy-Ion Collisions

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss.

High-fidelity modelling of a six-turbine tidal array in the Shetlands

Micro-siting tidal stream turbines in a confined seabed area requires a extensive understanding of the flow dynamics over the water column at turbine deployment locations so that operating conditions are assessed, wake effects can be estimated to infer the energy yield [1], or bathymetry effects can be quantified. Tidal currents have the advantage of being highly predictable and mostly bidirectional but the uneven bathymetry found at most of sites introduces a high variability to the flow conditions within relatively short distances. Considering future tidal turbine arrays will comprise dozens of devices, deploying ADCPs at each turbine position would be very expensive or very time consuming, outlining the need for accurate modelling tools to be used as digital twins in micro-siting. Shallow-water models are widely adopted in preliminary design of tidal arrays but fail to capture the three dimensional nature of the flow and predict deflected wakes whose streamwise length is also over-predicted [2]. Thus, eddy-resolving method are required to fully capture the turbulence from the free-stream flow, induced by turbines and from bathymetry. \\&#x0D; This study provides a real-project application of the state-of-the-art large-eddy simulation (LES) code DOFAS (Digital Offshore Farms Simulator) [3] to the six 100kW-turbine array deployed by NOVA Innovation Ltd. in the Shetlands, UK [4]. The bathymetry data has been obtained from EMODnet database with the velocity profiles set at the inlet condition of both ebb and flood tides imported from Macleod et al. (2019) [4]. The deployment site is characterised by steep slopes with a maximum depth of 45 m at the cross-section where turbines are located. The bathymetry has a downwards slope when the flow goes in the ebb tide direction whilst upstream during flood tide. The tidal rose indicated a slight deviation of about 20$^{\circ}$ between ebb and flood directions. The turbines have a diameter ($D$) of 9 m attached to a 10 m long hub whose diameter is 1 m. Three array configurations have been studied: (i) single row of three turbines, (ii) two rows of turbines spaced 8$D$, and (iii) two rows of turbines spaced 12$D$. The computational domain extends over 600 m by 300 m in the horizontal plane yielding 540 million cells, requiring 32,500 CPU hours to compute 30 min of physical time.\\&#x0D; Results show that bathymetry effects at this site play a larger role when designing the location of the secondary row of turbines compared to wake effects from upstream turbines. During the ebb tide, the increase in water depth reduces the wake recovery far downstream so that the array with 12$D$ row spacing has a lower performance than the 8$D$ one with approx. 30\% decrease in energy yield. Conversely, the uphill shape of the bathymetry during the flood enables a fast wake recovery so that the downstream row experiences almost no energy yield loss due to wakes.

Mapping structures on the core–mantle boundary using Sdiff postcursors: Part I. Method and Validation

SUMMARY Ultra-low velocity zones (ULVZs) are patches of extremely slow seismic velocities on the core–mantle boundary (CMB). Here, we target them using the postcursors to S core-diffracted phases (Sdiff) caused by ULVZs. We use traveltimes of these postcursors to make probabilistic maps using a reversible-jump Markov chain Monte Carlo inversion setup. For the forward model, we extend 2-D wave front tracking (2DWT) software, previously developed for surface wave multipathing studies, to the CMB. The 2DWT is able to model the full multipathing behaviour of Sdiff postcursors and compute arrival times for a given ULVZ input velocity structure on the order of a few CPU seconds, as opposed to 100s of CPU hours required for 3-D full waveform synthetics. We validate the method using synthetic data sets produced by the 2DWT, as well as 3-D full waveform synthetics, using a parametrisation formed from a collection of ellipses. We also test idealistic data coverage versus a case of more realistic coverage. We show ULVZ size and velocity reduction can typically be well recovered, and our maps show the inherent trade-off between these parameters around the edge of the ULVZ. Our method cannot directly constrain ULVZ height; tests show that it underestimates ULVZ velocity reductions and overestimates ellipticity for thinner ULVZs due to neglecting mantle effects.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics

Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.

Self-Consistent Picture of the Mass Ejection from a One Second Long Binary Neutron Star Merger Leaving a Short-Lived Remnant in a General-Relativistic Neutrino-Radiation Magnetohydrodynamic Simulation.

We perform a general-relativistic neutrino-radiation magnetohydrodynamic simulation of a one second-long binary neutron star merger on the Japanese supercomputer Fugaku using about 85million CPU hours with 20 736 CPUs. We consider an asymmetric binary neutron star merger with masses of 1.2M_{⊙} and 1.5M_{⊙} and a "soft" equation of state SFHo. It results in a short-lived remnant with the lifetime of ≈0.017 s, and subsequent massive torus formation with the mass of ≈0.05M_{⊙} after the remnant collapses to a black hole. For the first time, we find that after the dynamical mass ejection, which drives the fast tail and mildly relativistic components, the postmerger mass ejection from the massive torus takes place due to the magnetorotational instability-driven turbulent viscosity in a single simulation and the two ejecta components are seen in the distributions of the electron fraction and velocity with distinct features.

FCEVAL: An effective and quantitative platform for evaluating fuzzer combinations fairly and easily

Multiple base fuzzers collaborate as a fuzzer combination. Fuzzer combinations have been proven to perform more robustly and efficiently when fuzzing complicated real-world programs. The efficiency of finding bugs with limited computational resources would greatly benefit from the fuzzer combinations chosen by an effective and quantitative performance evaluation. However, evaluating fuzzer combinations remains challenging due to the lack of infrastructure for collaborative fuzzing, high enough collaboration efficiency of base fuzzers, unified benchmarks, comprehensive metrics, and unified analysis methods of coverage and bugs. This prevents us from selecting efficient fuzzer combinations and thus impairs vulnerability mining on real-world targets. In this paper, we design and implement FCEVAL, the first open-source platform for evaluating fuzzer combinations. In detail, we propose a new test case-sharing policy for increasing fuzzing potential so that we can provide a more efficient running environment for fuzzer combinations and thus improve evaluation effectiveness. Also, we select a unified set of diverse benchmarks and comprehensive metrics while adopting unified independent methods of real-time coverage statistics and multiple-sanitizers-based bug analysis for evaluation fairness and quantification. In addition, we design tools and guidelines covering the whole evaluation process for usability. With the above methodologies, we first construct an infrastructure special for collaborative fuzzing as the base of FCEVAL. After comparing two test case-sharing policies on the infrastructure and choosing the promising one as a substantial part of FCEVAL, we leverage FCEVAL to evaluate fuzzer combinations for more than 40,000 CPU hours and come up with five important conclusions, including (a) an efficient test case-sharing policy improving fuzzing potential and thus evaluation effectiveness, (b) comprehensive metrics being essential, (c) 24-hour duration and 20 repetitions for evaluation being substantial, (d) independent analysis methods of code coverage and bugs deserving of extensive adoption, and (e) FCEVAL being able to evaluate fuzzer combinations effectively, fairly, comprehensively, and easily. Meanwhile, we suggest how to improve collaborative fuzzing. In addition, source codes and test data are publicly available.