Multi-core Research Articles

3D-Printed (CoCrFeMnNi)3O4@C-GR dual core–shell composites: Multilevel control and mechanisms of microwave absorption performance

With the rapid development of modern electronic technology, there is an increasing demand for microwave-absorbing materials in various applications. These include reducing electromagnetic interference, enhancing communication quality, and ensuring stealth capabilities for military equipment. This study has developed a novel microwave-absorbing material, which is a composite system consisting of a high-entropy dual-core–shell (CoCrFeMnNi)3O4@C combined with graphene (GR), and is composed of polylactic acid (PLA) as the matrix. The material is prepared through a process involving high-temperature oxidation, carbonization to form the shell, and Powder extrusion molding techniques. The results demonstrate that when the dual-core–shell (CoCrFeMnNi)3O4@C microspheres are added at a loading of 20 wt%, and GR is added at 7 wt%, with a thickness of 2.22 mm, the composite material achieves a minimum reflection loss (RLmin) value of −51.36 dB and an effective absorption bandwidth (EAB) of 5.20 GHz. This excellent performance is attributed to the lattice distortion, a large number of oxygen vacancies, and abundant heterogeneous interfaces and conductive networks within the composites. These factors work together to stimulate multiple relaxation mechanisms, enhance the magnetic loss effect, and greatly improve the microwave absorption capability. The introduction of high-entropy microspheres effectively modulates the high conductivity of GR, further enhancing the comprehensive absorption performance of the material. This study not only offers a new strategy for designing and preparing high-performance microwave-absorbing materials but also establishes a valuable scientific foundation for materials research and application in related fields.

Proposing Disk Scheduling Algorithm to Enhance the Efficiency of Disk Performance

Management of disk scheduling is a very important aspect of operating system. Disk scheduling involves a careful examination of pending requests to determine the most efficient way to serve these requests. A disk scheduler examines the positional relationship among waiting requests, then reorders the queue so that the requests will be serviced with minimum seek. Performance of the disk scheduling completely depends on how efficient is the scheduling algorithm to allocate services to the request in a better manner. After the arrival of the multi-core processor, the speed of the processors became much faster with the Hyper-Threading technology, and with this the speed of the processors increased dramatically with a record speed. All processers need CPU time and read/write time together to complete their execution. Read/write operations require the operating system to access the disk to store and retrieve data. In the recent years many algorithms (FIFO, SSTF, SCAN, C-SCAN, LOOK, etc.) are developed in order to optimize the system disk I/O performance. The purpose of this study is to obtain a new scheduling algorithm that reduces seek time, sum of head movement, spin time, and transfer time, so as to improve disk performance efficiency in a better way to try to synchronize with CPU performance.

A new approach for software-simulation of membrane systems using a multi-thread programming model

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a new model for simulating P systems using a multi-threaded approach in a multi-core processor. This simulation approach establishes a new paradigm that is entirely in line with the philosophy of P-systems: since objects must react in parallel, asynchronously and autonomously with other objects, simulation using multiple synchronized threads completely mimics the behavior of objects within a membrane. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The obtained results show that the new model is correct and that it can be extended to other more complex types of P systems, in order to discover which are the limit of this multi-threaded approach when running it in multi-core processors.

A discrete unified gas kinetic scheme with sparse velocity grid for rarefied gas flows

In this paper, a discrete unified gas kinetic scheme (DUGKS) with a sparse grid method applied in velocity space (DUGKS-SG) is proposed for simulating rarefied gas flows. The DUGKS-SG decomposes the computationally demanding problem into smaller and independent subproblems, thereby reducing the computational costs and exhibiting good parallelism. Several numerical tests, including the two-dimensional Riemann problem and the lid-driven microcavity flow, have been conducted to validate the performance of the DUGKS-SG. Comparisons with the original DUGKS and the Direct Simulation Monte Carlo (DSMC) method demonstrate that DUGKS-SG can provide satisfactory results with improved efficiency. Specifically, a maximum speedup of 9.486 for a 2D case with 7 CPU cores and 13.035 for a 3D case with 8 CPU cores can be achieved. These results suggest that the proposed DUGKS-SG can serve as an efficient numerical method for rarefied gas flow simulations.

Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca.

Motivated by theoretical and practical issues that arise when applying Principal component analysis (PCA) to count data, Townes et al. introduced "Poisson GLM-PCA", a variation of PCA adapted to count data, as a tool for dimensionality reduction of single-cell RNA sequencing (scRNA-seq) data. However, fitting GLM-PCA is computationally challenging. Here we study this problem, and show that a simple algorithm, which we call "Alternating Poisson Regression" (APR), produces better quality fits, and in less time, than existing algorithms. APR is also memory-efficient and lends itself to parallel implementation on multi-core processors, both of which are helpful for handling large scRNA-seq datasets. We illustrate the benefits of this approach in three publicly available scRNA-seq datasets. The new algorithms are implemented in an R package, fastglmpca. The fastglmpca R package is released on CRAN for Windows, macOS and Linux, and the source code is available at github.com/stephenslab/fastglmpca under the open source GPL-3 license. Scripts to reproduce the results in this paper are also available in the GitHub repository and on Zenodo.

PC-Kriging-powered parallelizing Bayesian updating for stochastic vehicle-track dynamical system with contact force measurements and Gaussian process discrepancy model

High-precision vehicle-track coupled dynamical models play a vital role in assessing the running safety of the vehicle, tracking fatigue behavior, and establishing a digital twin for high-speed railways. This study established a high-fidelity vehicle-track coupled dynamical model as a forward solver, which was then updated in a Bayesian inference framework based on the field tests of rail irregularities and wheel/rail normal contact forces. The prediction errors corresponding to different time steps were represented through a Gaussian process (GP) model characterized by a covariance function with the Gaussian kernel to incorporate the correlation between different time steps. To cope with the considerable expense of the likelihood function calculations with large-scale loop operations and the computational burden due to the large batch of repetitive evaluations of the high-fidelity forward model involved in the stochastic sampling from the posterior probability distribution, a metamodel-powered parallelizing stochastic sampling scheme was adopted. The posterior distribution could be formulated by replacing the expensive explicit model evaluation involved in the likelihood function with a Polynomial-Chaos-Kriging (PC-Kriging) predictor providing a surrogate mapping between the wheel/rail normal contact forces and physical parameters. Finally, a parallelizing computing scheme running across multiple CPU cores on high-performance computers was realized to sample from the vectorized formula of the emulator-powered posterior distribution. Field test data from a high-speed railway in China was adopted to demonstrate that the Bayesian inference scheme can quantify the uncertainties of the core physical parameters of the high-fidelity vehicle-track coupled dynamical system by efficiently trading off accuracy and computational efficiency.

Transformer coupled toroidal wave-heated remote plasma sources operating in Ar/NF3 mixtures

Remote plasmas are used in semiconductor device manufacturing as sources of radicals for chamber cleaning and isotropic etching. In these applications, large fluxes of neutral radicals (e.g. F, O, Cl, H) are desired with there being negligible fluxes of potentially damaging ions and photons. One remote plasma source (RPS) design employs toroidal, transformer coupling using ferrite cores to dissociate high flows of moderately high pressure (up to several Torr) electronegative gases. In this paper, results are discussed from a computational investigation of moderate pressure, toroidal transformer coupled RPS sustained in Ar and Ar/NF3 mixtures. Operation of the RPS in 1 Torr (133 Pa) of argon with a power of 1.0 kW at 0.5 MHz and a single core produces a continuous toroidal plasma loop with current continuity being maintained dominantly by conduction current. Operation with dual cores introduces azimuthal asymmetries with local maxima in plasma density. Current continuity is maintained by a mix of conduction and displacement current. Operation in NF3 for the same conditions produces essentially complete NF3 dissociation. Electron depletion as a result of dissociative attachment of NF3 and NF x fragments significantly alters the discharge topology, confining the electron density to the downstream portion of the source where the NF x density has been lowered by this dissociation.

New feeding mechanism to enhance the properties of wrapped elastic composite yarn and denim fabrics thereof

In light of the recent technological advancements in composite yarns with multicomponent cores, also known as dual core spun yarns, their utilisation in the textile industry has become widespread. A V-grooved roller is employed to enhance the feeding of multicomponent cores. In this context, diverse composite yarn designs with varying feeding mechanisms can be employed to optimise yarn performance. This study presents an alternative feeding mechanism utilising a W-grooved roller for commercial use in composite yarn production. To achieve this, dual core yarns and wound elastic composite yarns (filament feeding varies with the sheath cotton fibre with elastane in the centre wrapped on the right and left side) were produced using a modified ring spinning system with different parameters. Denim fabrics were then produced using these composite yarns as weft yarns. The results demonstrated that the wrapped elastic composite yarns with left and right filament core positioning exhibited superior yarn properties in terms of strength, elongation at break, evenness and hairiness. Furthermore, denim fabrics made with wrapped elastic composite yarns exhibited superior breaking load and tearing force results on weft basis. These fabrics also exhibited lower growth, defined as the percentage of the fabric returning to its original length after tensile stress removal.

Crater identification by perspective cone alignment

Crater identification aims to match the craters of a planetary body observed in an image to the corresponding entries in a catalogue of known craters of the same body. It is a key step in crater-based navigation algorithms for planetary missions. Many crater identification methods extract descriptors from the observed craters and compare them to an index of descriptors constructed a priori from the crater catalogue. The need for viewpoint invariance motivates descriptors defined over crater tuples (e.g., crater triads for projective invariance). However, this implies a descriptor index that grows rapidly (e.g., cubically) with the size of the catalogue, which practically limits the spatial resolution and/or coverage of the catalogue. Another serious drawback is the sensitivity of the descriptors and the matching accuracy to noise and outliers in the crater detection result, which are inevitable due to imperfect crater detection algorithms. In this paper, we propose a novel descriptorless crater identification technique. At its core, our method solves the perspective cone alignment problem with geometric verification iteratively over the crater catalogue. We show that our simple algorithm, with time and space complexities that are linear in the catalogue size, is substantially more accurate than state-of-the-art descriptor-based methods in the presence of noise. Moreover, the availability of multi-core onboard processors raises the prospect of speeding up our method through parallelisation.

Adapting arepo-rt for exascale computing: GPU acceleration and efficient communication

ABSTRACT Radiative transfer (RT) is a crucial ingredient for self-consistent modelling of numerous astrophysical phenomena across cosmic history. However, on-the-fly integration into radiation hydrodynamics (RHD) simulations is computationally demanding, particularly due to the stringent time-stepping conditions and increased dimensionality inherent in multifrequency collisionless Boltzmann physics. The emergence of exascale supercomputers, equipped with extensive CPU cores and GPU accelerators, offers new opportunities for enhancing RHD simulations. We present the first steps towards optimizing arepo-rt for such high-performance computing environments. We implement a novel node-to-node (n-to-n) communication strategy that utilizes shared memory to substitute intranode communication with direct memory access. Furthermore, combining multiple internode messages into a single message substantially enhances network bandwidth utilization and performance for large-scale simulations on modern supercomputers. The single-message n-to-n approach also improves performance on smaller scale machines with less optimized networks. Furthermore, by transitioning all RT-related calculations to GPUs, we achieve a significant computational speedup of around 15 for standard benchmarks compared to the original CPU implementation. As a case study, we perform cosmological RHD simulations of the Epoch of Reionization, employing a similar setup as the THESAN project. In this context, RT becomes sub-dominant such that even without modifying the core arepo codebase, there is an overall threefold improvement in efficiency. The advancements presented here have broad implications, potentially transforming the complexity and scalability of future simulations for a wide variety of astrophysical studies. Our work serves as a blueprint for porting similar simulation codes based on unstructured resolution elements to GPU-centric architectures.

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

Efficient parallel solver for rarefied gas flow using GSIS

Recently, the general synthetic iterative scheme (GSIS) has been proposed to find the steady-state solution of the Boltzmann equation in the whole range of gas rarefaction, where its fast-converging and asymptotic-preserving properties lead to the significant reduction of iteration numbers and spatial cells in the near-continuum flow regime. However, the efficiency and accuracy of GSIS have only been demonstrated in two-dimensional problems with small numbers of spatial cells and discrete velocities. Here, a large-scale parallel computing strategy is designed to extend the GSIS to three-dimensional flow problems, including the supersonic flows which are usually difficult to solve by the discrete velocity method. Since the GSIS involves the calculation of the mesoscopic kinetic equation which is defined in six-dimensional phase-space, and the macroscopic high-temperature Navier–Stokes–Fourier equations in three-dimensional physical space, the proper partition of the spatial and velocity spaces, and the allocation of CPU cores to the mesoscopic and macroscopic solvers, are the keys to improving the overall computational efficiency. These factors are systematically tested to achieve optimal performance, up to 100 billion spatial and velocity grids. For hypersonic flows around the Apollo reentry capsule, the X38-like vehicle, and the space station, our parallel solver can obtain the converged solution within one hour.

Responses of Ecosystem Services to Land Use/Cover Changes in Rapidly Urbanizing Areas: A Case Study of the Shandong Peninsula Urban Agglomeration

The rapid expansion of built-up land, a hallmark of accelerated urbanization, has emerged as a pivotal factor contributing to regional climate change and the degradation of ecosystem functions. The decline in ecosystem service value (ESV) has consequently garnered significant attention in global sustainable development research. The Shandong Peninsula urban agglomeration is crucial for promoting the construction of the Yellow River Economic Belt in China, with its ecological status increasingly gaining prominence. This study investigated the ESV response to land use/cover change (LUCC) through the elasticity coefficient in order to analyze the degree of disturbance caused by land use activities on ecosystem functions in the Shandong Peninsula urban agglomeration. This analysis was based on the examination of LUCC characteristics and ESV from 1990 to 2020. The findings reveal that (1) the Shandong Peninsula urban agglomeration experienced a continuous increase in the proportion of built-up land from 1990 to 2020, alongside a highly complex transfer between different land use types, characterized by diverse transfer trajectories. The most prominent features were noted to be the rapid expansion of built-up land and the simultaneous decline in agricultural land. (2) The analysis of four landscape pattern indices, encompassing Shannon’s diversity index, indicates that the continuous development of urbanization has led to increased fragmentation in land use and decreased connectivity. However, obvious spatial distribution differences exist among different districts and counties. (3) The ESV was revised using the normalized difference vegetation index, revealing a slight decrease in the total ESV of the Shandong Peninsula urban agglomeration. However, significant differences were observed among districts and counties. The number of counties and districts exhibiting low and high ESVs continuously increased, whereas those with intermediate levels generally remained unchanged. (4) The analysis of the elasticity coefficient reveals that LUCC exerts a substantial disturbance and influence on ecosystem services, with the strongest disturbance ability occurring from 2000 to 2010. The elasticity coefficient exhibits obvious spatial heterogeneity across both the entire urban agglomeration and within individual cities. Notably, Qingdao and Jinan, the dual cores of the Shandong Peninsula urban agglomeration, exhibit markedly distinct characteristics. These disparities are closely related to their development foundations in 1990 and their evolution over the past 30 years. The ESV response to LUCC displays significant variation across different time periods and spatial locations. Consequently, it is imperative to formulate dynamic management policies on the basis of regional characteristics. Such policies aim to balance social and economic development while ensuring ecological protection, thereby promoting the social and economic advancement and ecological environment preservation of the Shandong Peninsula urban agglomeration.

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While measuring inference latency of a huge set of candidate architectures during NAS is not feasible, latency prediction for mobile devices is challenging, because of hardware heterogeneity, optimizations applied by machine learning frameworks, and diversity of neural architectures. Motivated by these challenges, we first quantitatively assess the characteristics of neural architectures (specifically, convolutional neural networks for image classification), ML frameworks, and mobile devices that have significant effects on inference latency. Based on this assessment, we propose an operation-wise framework which addresses these challenges by developing operation-wise latency predictors and achieves high accuracy in end-to-end latency predictions, as shown by our comprehensive evaluations on multiple mobile devices using multicore CPUs and GPUs. To illustrate that our approach does not require expensive data collection, we also show that accurate predictions can be achieved on real-world neural architectures using only small amounts of profiling data.

Refractive insensitive directional bend sensor based on specialty microstructure optical fiber with dumbbell shape core

With the development of information society, more and more special occasions are calling for a wide range of sensors with special properties. In this work, a refractive index insensitive directional bend sensor is proposed based on a specialty microstructure optical fiber with dumbbell shape core, which works as a dual core fiber (DCF). Firstly, this specialty microstructure optical fiber with dumbbell shape core has been fabricated with self-pressurised drawing technique. Then, it has been constructed as a Mach-Zehnder interferometer (MZI) sensor. Consequently, its bend sensing characteristics has been investigated from both theory and experiment. The response of the proposed sensor to the bending evidently displays the directional dependence. In the bending along x direction, the interference peak wavelength is almost insensitive to the curvature. But the transmission is sensitive, which almost obeys the exponential function. Correspondingly, in the bending along y direction the wavelength almost linearly varies with curvature, giving out an averaged sensitivity of 2.44 nm/m−1 in the curvature range of 0–3.79 m−1. But the peak transmission fluctuates without typical principle. In addition, the response of the proposed sensor to the refractive index variation has been checked. It is found that the peak wavelength is almost insensitive to the change of refractive index from 1.33 to 1.45, and the peak transmission fluctuates without clear principle. All these results demonstrate that the proposed sensor has great potential for future internet of things (IoT) applications.

Direct 3D-printed ring-resonator photonic circuit on a dual core fiber tip for remote sensing applications.

On-chip optical sensors using ring- and disk-resonators have many potential sensing applications, yet robust and efficient fiber-to-chip coupling and the differing form factor between the two pose deployment challenges. To resolve this, we 3D-printed a ring-resonator onto the tip of a dual-core fiber and demonstrate its use as a remote temperature sensor. The fiber-tip optical circuit is fabricated using direct laser writing (DLW) with two-photon absorption photopolymer material IP-Dip, forming micrometer-scale waveguide cores having a refractive index of 1.53 with a surrounding air cladding. We connect the two-fiber cores by a printed bus-waveguide, utilizing total internal reflection mirrors, allowing light launched into one core to be guided back to the other core. Furthermore, a DLW printed racetrack resonator evanescently coupled to the bus waveguide (Q ∼ 3000) imposes spectral dips on resonance wavelengths. Light sent down into one core is interrogated upon return from the second core, all from the distal end of the sensor. When the sensing end's temperature is varied, we find a sensitivity of 78 pm/K, due to the polymer's thermo-optic index variation. The ring-resonator could be functionalized for other sensing applications.

PowerTrain: Fast, generalizable time and power prediction models to optimize DNN training on accelerated edges

Accelerated edge devices, like Nvidia’s Jetson with 1000+ CUDA cores, are increasingly used for DNN training and federated learning, rather than just for inferencing workloads. A unique feature of these compact devices is their fine-grained control over CPU, GPU, memory frequencies, and active CPU cores, which can limit their power envelope in a constrained setting while throttling the compute performance. Given this vast 10k+ parameter space, selecting a power mode for dynamically arriving training workloads to exploit power–performance trade-offs requires costly profiling for each new workload, or is done ad hoc. We propose PowerTrain, a transfer-learning approach to accurately predict the power and time that will be consumed when we train a given DNN workload (model + dataset) using any specified power mode (CPU/GPU/memory frequencies, core-count). It requires a one-time offline profiling of 1000s of power modes for a reference DNN workload on a single Jetson device (Orin AGX) to build Neural Network (NN) based prediction models for time and power. These NN models are subsequently transferred (retrained) for a new DNN workload, or even a different Jetson device, with minimal additional profiling of just 50 power modes to make accurate time and power predictions. These are then used to rapidly construct the Pareto front and select the optimal power mode for the new workload, e.g., to minimize training time while meeting a power limit. PowerTrain’s predictions are robust to new workloads, exhibiting a low MAPE of <6% for power and <15% for time on six new training workloads (MobileNet, YOLO, BERT, LSTM, etc.) for up to 4400 power modes, when transferred from a ResNet reference workload on Orin AGX. It is also resilient when transferred to two entirely new Jetson devices (Xavier AGX and Jetson Orin Nano) with prediction errors of <14.5% and <11%. These outperform baseline predictions by more than 10% and baseline optimizations by up to 45% on time and 88% on power.

Benchmarking parallel programming for single-board computers

Within the computing continuum, SBCs (single-board computers) are essential in the Edge and Fog, with many featuring multiple processing cores and GPU accelerators. In this way, parallel computing plays a crucial role in enabling the full computational potential of SBCs. However, selecting the best-suited solution in this context is inherently complex due to the intricate interplay between PPI (parallel programming interface) strategies, SBC architectural characteristics, and application characteristics and constraints. To our knowledge, no solution presents a combined discussion of these three aspects. To tackle this problem, this article aims to provide a benchmark of the best-suited parallelism PPIs given a set of hardware and application characteristics and requirements. Compared to existing benchmarks, we introduce new metrics, additional applications, various parallelism interfaces, and extra hardware devices. Therefore, our contributions are the methodology to benchmark parallelism on SBCs and the characterization of the best-performing parallelism PPIs and strategies for given situations. We are confident that parallel computing will be mainstream to process edge and fog computing; thus, our solution provides the first insights regarding what kind of application and parallel programming interface is the most suited for a particular SBC hardware.

Self-centering dual rocking core system with viscous dampers in additional rocking sections

To mitigate the high mode effects and reduce the force requirements of the structural members in the multi-story self-centering dual rocking core (SDRC) system, this paper proposes the multiple self-centering dual rocking core system with viscous dampers in additional rocking sections (MSDRCV system). The sketch and ideal working mechanisms of the MSDRCV system are interpreted. The additional rocking sections are set to decrease the force requirements of structural members and viscous dampers are included to mitigate high mode responses of the additional rocking sections, introducing seismic performance enhancement. Comparative analyses on a 9-story MSDRCV system with viscous dampers in rocking sections, a 9-story multiple self-centering dual rocking core system without viscous dampers in rocking sections (MSDRC system) and a 9-story SDRC system were carried out. The MSDRCV and MSDRC system can realize the reduction of the force demands in structural members in contrast to the SDRC system. Nevertheless, the high mode responses caused the uncontrollable displacement concentration in the MSDRC system and hence aggravated the seismic performance under rare seismic events. The analysis results in the frequency domain confirmed that the viscous dampers in the MSDRCV system can efficiently control the high-mode responses. The effects of the design parameters of the viscous dampers on the peak responses of the MSDRCV systems were comprehensively studied via parametric analyses. The results offer guidance for the viscous dampers design.

Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca.

Motivated by theoretical and practical issues that arise when applying Principal Components Analysis (PCA) to count data, Townes et al introduced "Poisson GLM-PCA", a variation of PCA adapted to count data, as a tool for dimensionality reduction of single-cell RNA sequencing (RNA-seq) data. However, fitting GLM-PCA is computationally challenging. Here we study this problem, and show that a simple algorithm, which we call "Alternating Poisson Regression" (APR), produces better quality fits, and in less time, than existing algorithms. APR is also memory-efficient, and lends itself to parallel implementation on multi-core processors, both of which are helpful for handling large single-cell RNA-seq data sets. We illustrate the benefits of this approach in two published single-cell RNA-seq data sets. The new algorithms are implemented in an R package, fastglmpca. The fastglmpca R package is released on CRAN for Windows, macOS and Linux, and the source code is available at github.com/stephenslab/fastglmpca under the open source GPL-3 license. Scripts to reproduce the results in this paper are also available in the GitHub repository. mstephens@uchicago.edu. Supplementary data are available on BioRxiv online.