Weak Scale Research Articles

Practical naturalness and its implications for weak scale supersymmetry

Practical naturalness and its implications for weak scale supersymmetry

ESA: An efficient sequence alignment algorithm for biological database search on Sunway TaihuLight

In computational biology, biological database search has been playing a very important role. Since the COVID-19 outbreak, it has provided significant help in identifying common characteristics of viruses and developing vaccines and drugs. Sequence alignment, a method finding similarity, homology and other information between gene/protein sequences, is the usual tool in the database search. With the explosive growth of biological databases, the search process has become extremely time-consuming. However, existing parallel sequence alignment algorithms cannot deliver efficient database search due to low utilization of the resources such as cache memory and performance issues such as load imbalance and high communication overhead. In this paper, we propose an efficient sequence alignment algorithm on Sunway TaihuLight, called ESA, for biological database search. ESA adopts a novel hybrid alignment algorithm combining local and global alignments, which has higher accuracy than other sequence alignment algorithms. Further, ESA has several optimizations including cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. They are implemented in a heterogeneous processor SW26010 adopted in the world’s 6th fastest supercomputer, Sunway TaihuLight. The implementation of ESA is evaluated with the Swiss-Prot database on Sunway TaihuLight and other platforms. Our experimental results show that ESA has a speedup of 34.5 on a single core group (with 65 cores) of Sunway TaihuLight. The strong and weak scalabilities of ESA are tested with 1 to 1024 core groups of Sunway TaihuLight. The results show that ESA has linear weak scalability and very impressive strong scalability. For strong scalability, ESA achieves a speedup of 338.04 with 1024 core groups compared with a single core group. We also show that our proposed optimizations are also applicable to GPU, Intel multicore processors, and heterogeneous computing platforms.

A Multi-GPU Aggregation-Based AMG Preconditioner for Iterative Linear Solvers

We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting Nvidia Graphics Processing Unit (GPU) accelerators. The work extends previous efforts of some of the authors in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BootCMatchG</i> library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the GPU kernels. Strong and weak scalability results of the new version of the library on well-known benchmark test cases are discussed. Comparisons with the Nvidia AmgX solution show a speedup, in the solve phase, up to 2.0x.

Adaptive mesh refinement in locally conservative level set methods for multiphase fluid displacements in porous media

Multiphase flow in porous media often occurs with the formation and coalescence of fluid ganglia. Accurate predictions of such mechanisms in complex pore geometries require simulation models with local mass conservation and with the option to improve resolution in areas of interest. In this work, we incorporate patch-based, structured adaptive mesh refinement capabilities into a method for local volume conservation that describes the behaviour of disconnected fluid ganglia during level set simulations of capillary-controlled displacement in porous media. We validate the model against analytical solutions for three-phase fluid configurations in idealized pores containing gas, oil, and water, by modelling the intermediate-wet oil layers as separate domains with their volumes preserved. Both the pressures and volumes of disconnected ganglia converge to analytical values with increased refinement levels of the adaptive mesh. Favourable results from strong and weak scaling tests emphasize that the number of patches per processor and the total number of patches are important parameters for efficient parallel simulations with adaptive mesh refinement. Simulations of two-phase imbibition and three-phase gas invasion on segmented 3D images of water-wet sandstone show that adaptive mesh refinement has the highest impact on three-phase displacements, especially concerning the behaviour of the conserved, intermediate-wet phase.

A variant of the course of chronic subdural hematoma (clinical case)

Introduction. Doctors of various specialties face difficulties in diagnosing chronic subdural hematomas (CSDH) at different stages, usually due to the lack of an evident causal relationship between neurological manifestations of subdural hematoma and the head injury sustained several weeks or even months prior to the patient's examination. Clinically, CSDH can mimic multiple neurological disorders, and before the patient is referred to a neurosurgeon, they might be inspected by various specialists such as neurologists, physicians, traumatologists and even psychiatrists. As a results, the patient initially does not receive specialized treatment, which directly affects postoperative mortality and disabi lity in patients with this pathology.Aim. In this paper we aim to describe a rare clinical case of atypical course of CSDH. A clinical case. A 54‑year‑old woman, after falling from a ladder about 2 m high, 1.5 months after the injury, developed depression of consciousness and tetraparesis up to 2–3 points according to the Medical research counsil Weakness scale. After examination, the patient was verified to have a chronic hemispheric subdural hematoma of large volume, transverse dislocation of the median structures of the brain by 16 mm. An injury to the cervical spine was ruled out. The patient was operated on, HSDG was removed from 2 milling holes, achieving complete washing of the hematoma and, subsequently, complete restoration of motor functions in the patient – 5 point by Glasgow outcome scale (5‑point Glasgow scale).Discussion. This paper presents the results of treatment and observation of patients with atypical course of CSDH of traumatic origin. The main method of treatment is the surgical removal of chronic subdural hematoma by a minimally invasive method: the imposition of 2 trefination holes with removal by evacuation of CSDH.Conclusion. The problem of early detection of chronic subdural hematomas is extremely relevant. Careful collection of anamnesis, the alertness of doctors of related specialties on this problem, the mandatory use of neuroimaging methods (computer and magnetic resonance imaging) will often help to correctly and timely diagnose, start treatment on time and increase the patient’s chance of recovery, even in cases of atypical course.

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

Critique of: “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From UC San Diego

Bayesian networks (BNs) have become popular in recent years to describe natural phenomena in situations where causal linkages are important to understand. In order to get around the inherent non-tractability of learning BNs, Srivastava et al. propose a markov blanket discovery-based approach to learning in their paper titled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">”A Parallel Framework for Constraint-based Bayesian Network Learning via Markov Blanket Discovery.”</i> We are able to reproduce both the strong and weak scaling experiments from the paper up to 128 cores, and verify communication cost scaling for all three algorithms in the paper. We also introduce methodological improvements to weak scaling that show the paper's findings are unique to the methodology and not the datasets used. Slight variations in performance were observed due to differences in datasets, core count, and job scheduling.

Removal of Active Region Inflows Reveals a Weak Solar Cycle Scale Trend in the Near-surface Meridional Flow

Using time–distance local helioseismology flow maps within 1 Mm of the solar photosphere, we detect inflows toward activity belts that contribute to solar-cycle scale variations in the near-surface meridional flow. These inflows stretch out as far as 30° away from the active region centroids. If active region neighborhoods are excluded, the solar-cycle-scale variation in the background meridional flow diminishes to below 2 m s−1, but still shows systematic variations in the absence of active regions between sunspot cycles 24 and 25. We therefore propose that the near-surface meridional flow is a three-component flow made up of a constant baseline flow profile that can be derived from quiet-Sun regions, variations due to inflows around active regions, and solar-cycle-scale variation of about 2 m s−1. Torsional oscillation, on the other hand, is found to be a global phenomenon, i.e., exclusion of active region neighborhoods does not significantly affect its magnitude or phase. This nonvariation in torsional oscillation with distance away from active regions and the three-component breakdown of the near-surface meridional flow serve as vital constraints for solar dynamo models and surface flux-transport simulations.

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Peking University

Ankit Srivastava et al. [1] proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with Centos-compatible Oracle Linux. Our experiments, covering the same three algorithms of ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences.

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Tsinghua University

Srivastava et al. propose a parallel framework to optimize Bayesian network learning in the SC20 paper entitled “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery”. They parallelize all the phases in network constructing algorithms to achieve high performance and scalability. In this paper, we reproduce the strong scaling and weak scaling experiments in that SC paper. We conduct experiments on a 4-node cluster with Intel CPUs provided by the SCC committee. We further analyze the results of communication overhead. Our results show that the proposed method in that SC paper scales well on the provided cluster, in accordance with the SC paper.

Spatiotemporal parallel physics-informed neural networks: A framework to solve inverse problems in fluid mechanics

Physics-informed neural networks (PINNs) are widely used to solve forward and inverse problems in fluid mechanics. However, the current PINNs framework faces notable challenges when presented with problems that involve large spatiotemporal domains or high Reynolds numbers, leading to hyper-parameter tuning difficulties and excessively long training times. To overcome these issues and enhance PINNs' efficacy in solving inverse problems, this paper proposes a spatiotemporal parallel physics-informed neural networks (STPINNs) framework that can be deployed simultaneously to multi-central processing units. The STPINNs framework is specially designed for the inverse problems of fluid mechanics by utilizing an overlapping domain decomposition strategy and incorporating Reynolds-averaged Navier–Stokes equations, with eddy viscosity in the output layer of neural networks. The performance of the proposed STPINNs is evaluated on three turbulent cases: the wake flow of a two-dimensional cylinder, homogeneous isotropic decaying turbulence, and the average wake flow of a three-dimensional cylinder. All three turbulent flow cases are successfully reconstructed with sparse observations. The quantitative results along with strong and weak scaling analyses demonstrate that STPINNs can accurately and efficiently solve turbulent flows with comparatively high Reynolds numbers.

PaScaL_TCS: A versatile solver for large-scale turbulent convective heat transfer problems with temperature-dependent fluid properties

PaScaL_TCS is an efficient and scalable solver for large-scale direct numerical simulations of natural convective flow that considers temperature-dependent fluid properties. For increased scalability on a massive-scale distributed memory system, the solver decomposes the computational domain into a cubic sub-domain. The numerical procedure is based on the monolithic projection method with staggered time discretization [1], which is an implicit and non-iterative solver for wall-bounded domains. The PaScaL_TDMA library is used to solve batched tridiagonal systems, which are partitioned according to domain decomposition. For parallel fast Fourier transform in a Fourier Poisson solver for pressure, two transpose schemes with different communicator sizes are proposed and compared according to the number of cores. An explicit intermediate aggregation scheme for MPI-IO is suggested to reduce the number of processes that simultaneously take part in parallel IO. The overall implementation was evaluated using the Nurion cluster system of the Korea Institute of Science and Technology Information with detailed performance profiling. With the efficient communication, the results showed good strong and weak scalability up to 131,072 cores. The file IO performance improved with the suggested MPI-IO scheme using explicit aggregation, especially when many processes are involved in parallel IO. The solver was verified by conducting large-scale differentially heated vertical convection flow simulations under the Oberbeck–Boussinesq (OB) approximation. The capability of the solver to capture and quantitatively describe the non-OB effect was demonstrated via glycerol Rayleigh–Bénard convection flow simulations. Program summaryProgram Title: PaScaL_TCSCPC Library link to program files:https://doi.org/10.17632/3b2bysrcxm.1Developer's repository link:https://github.com/MPMC-LabLicensing provisions: MITProgramming language: Fortran90Nature of problem: Solving the incompressible Navier-Stokes equations for natural convective flow considering the non-Oberbeck–Boussinesq effect, which considers the temperature dependence of fluids properties, in three-dimensional channel domain.Solution method: PaScaL_TCS uses the monolithic projection-based method with staggered time discretization (MPM-STD) [1]. It employs the Crank-Nicolson scheme along with staggered time which evaluates scalar and vector variables at the (n+1/2) and (n+1) time level, respectively. PaScaL_TDMA [2] is used to solve multiple tridiagonal matrix systems for decoupled energy and momentum equations in a distributed memory system. The Poisson equation solver for the pressure-correction scheme is based on Fourier diagonalization. MPI_Alltoallw is used for parallel FFT. PaScaL_TCS supports a single-file-based parallel IO for efficient data generation in large-scale computing.

Improving the performance of classical linear algebra iterative methods via hybrid parallelism

We propose fork-join and task-based hybrid implementations of four classical linear algebra iterative methods (Jacobi, Gauss–Seidel, conjugate gradient and biconjugate gradient stabilized) on CPUs as well as variations of them. This class of algorithms, that are ubiquitous in computational frameworks, are duly documented and the corresponding source code is made publicly available for reproducibility. Both weak and strong scalability benchmarks are conducted to statistically analyse their relative efficiencies.The weak scalability results assert the superiority of a task-based hybrid parallelisation over MPI-only and fork-join hybrid implementations. Indeed, the task-based model is able to achieve speedups of up to 25% larger than its MPI-only counterpart depending on the numerical method and the computational resources used. For strong scalability scenarios, hybrid methods based on tasks remain more efficient with moderate computational resources where data locality does not play an important role. Fork-join hybridisation often yields mixed results and hence does not seem to bring a competitive advantage over a much simpler MPI approach.

Search for leptophilic dark matter at the LHeC

The Large Hadron electron Collider (LHeC) has been designed to push the field of deep inelastic scattering to the high energy and intensity frontier using an intense electron beam with a proton beam from the High Luminosity–Large Hadron Collider. However, LHeC is also a great laboratory for new physics. In this work, we propose a search for dark matter that couples with leptons. This may yield and signals that can be potentially observed through simple missing-energy cuts that suppress the Standard Model background. Considering direct dark matter detection and LHC constraints, we show that LHeC offers a complementary probe for a weak scale dark matter fermion for masses up to 350 GeV, which reproduces the correct relic density, and has interesting implications for lepton flavor violation.

A Parallel Solver for FSI Problems with Fictitious Domain Approach

We present and analyze a parallel solver for the solution of fluid structure interaction problems described by a fictitious domain approach. In particular, the fluid is modeled by the non-stationary incompressible Navier–Stokes equations, while the solid evolution is represented by the elasticity equations. The parallel implementation is based on the PETSc library and the solver has been tested in terms of robustness with respect to mesh refinement and weak scalability by running simulations on a Linux cluster.

Assessing the performance of satellite derived and reanalyses data in capturing seasonal changes in extreme precipitation scaling rates over the Indian subcontinent

Intensity and frequency of short duration precipitation extremes are expected to increase under a warming climate at ≈7%K-1, following the Clausius-Claypeyron scaling relationship. Recent studies have analyzed global and regional scaling rates for precipitation extremes against temperature over an annual period, however assessment of seasonal variations of precipitation-temperature scaling, is largely unexplored, especially over the Indian subcontinent. Satellite derived and reanalysis based precipitation data sets can function as a suitable alternative to rain-gauge based data sets over data-sparse regions. In the present study, the performance of three high resolution data sets - GPM-IMERG satellite derived, ERA5 and IMDAA reanalysis precipitation - in determining the seasonal variations in precipitation-temperature scaling rates are investigated. When compared with the IMD data, IMERG and IMDAA capture the spatial variations and magnitude of scaling rates of daily precipitation extremes much better than ERA5. Two calibrated datasets - AIMERG and AERA5 are also analyzed and compared to IMD scaling rates. Following this, we further probe into the performance of the IMERG, ERA5 and IMDAA data sets over the entire Indian subcontinent, by including the results of scaling rates over oceans. The scaling rates of all three are found to be comparable in terms of magnitude and spatial distribution when analyzed over the entire Indian subcontinent. Significant seasonal variations in scaling rates are found over the Indian subcontinent, with the highest scaling rates in the post-monsoon and pre-monsoon seasons, and weaker rates in the monsoon season. Land–ocean contrast in scaling rates is evident across all seasons, but weak ocean scaling rates are found in the monsoon season for all three data sets.

A Portalino to the Twin Sector

Extensions of the Standard Model are often highly constrained by cosmology. New states in the theory can dramatically alter observed properties of the universe by the presence of additional matter or entropy. In particular, attempts to solve the hierarchy problem through naturalness invariably predict new particles near the weak scale which come into thermal equilibrium. Without a means to deposit this energy into the SM, these models are often excluded. Scenarios of “neutral naturalness” in particular, such as the Twin Higgs, frequently suffer from this. However, the Portalino, a singlet fermion that marries gauge neutral fermion operators, can naturally help provide a portal for entropy to return to the SM and to lift fermionic degrees of freedom in the Twin Sector. Together with spontaneous breaking of the Z2 SM ↔ Twin symmetry, there are new opportunities to confront the cosmological challenges of these models. Here, we attempt to develop such ideas. We shall show how one can lift many of the light fields by breaking Z2 with a U(1)Y scalar and its Twin partner. The introduction of Portalinos can lift the remaining degrees of freedom. We shall find that such models are highly constrained by precision SM measurements, motivating moderate extensions beyond this. We will discuss two, both of which include Z2 breaking, one with additional electroweak matter and another with additional colored matter. The electroweak model will involve simple dim-6 operators, which are easily UV completed. The strong model will involve the presence of new leptoquarks and diquarks. We will discuss the implications for the observed value of the muon anomalous magnetic moment, contributions to μ → eγ decay and possible colored signals even within these models of neutral naturalness, some of which might appear at the LHC or future colliders.

Efficient Distributed Matrix-free Multigrid Methods on Locally Refined Meshes for FEM Computations

This work studies three multigrid variants for matrix-free finite-element computations on locally refined meshes: geometric local smoothing, geometric global coarsening (both h -multigrid), and polynomial global coarsening (a variant of p -multigrid). We have integrated the algorithms into the same framework—the open source finite-element library deal.II —, which allows us to make fair comparisons regarding their implementation complexity, computational efficiency, and parallel scalability as well as to compare the measurements with theoretically derived performance metrics. Serial simulations and parallel weak and strong scaling on up to 147,456 CPU cores on 3,072 compute nodes are presented. The results obtained indicate that global-coarsening algorithms show a better parallel behavior for comparable smoothers due to the better load balance, particularly on the expensive fine levels. In the serial case, the costs of applying hanging-node constraints might be significant, leading to advantages of local smoothing, even though the number of solver iterations needed is slightly higher. When using p - and h -multigrid in sequence ( hp -multigrid), the results indicate that it makes sense to decrease the degree of the elements first from a performance point of view due to the cheaper transfer.

Dimension-8 SMEFT matching conditions for the low-energy effective field theory

In particle physics, the modern view is to categorize things in terms of effective field theories (EFTs). Above the weak scale, we have the SMEFT, formed when the heavy new physics (NP) is integrated out, and for which the Standard Model (SM) is the leading part. Below MW, we have the LEFT (low-energy EFT), formed when the heavy SM particles (W±, Z0, H, t) are also integrated out. In order to determine how low-energy measurements depend on the underlying NP, it is necessary to compute the matching conditions of LEFT operators to SMEFT operators. These matching conditions have been worked out for all LEFT operators up to dimension 6 in terms of SMEFT operators up to dimension 6 at the one-loop level. However, this is not sufficient for all low-energy observables. In this paper we present the momentum-independent matching conditions of all such LEFT operators to SMEFT operators up to dimension 8 at tree level.

弱圧縮性手法を用いた多孔質体内混相流の大規模計算

The simulation of gas-liquid two-phase flow through a porous media by using multiple GPUs have performed. The ideal parallel efficiency of the weak scaling is achieved by applying weakly compressible method, which can avoid to solve Poisson's equation. We can calculate a wide range of parameters by introducing weakly compressible method. The conservative Allen-Cahn equation is solved to capture the gas-liquid interface by using the finite volume method to conserve the total mass of each phase. The simulations of gas-liquid two-phase flow through two-dimensional porous medium are performed by using our solver by using multi GPUs. Ideal parallelization efficiency is achieved by overlapping computation and communication time.