Multi-core Architectures Research Articles

On vulnerabilities in EVT-based timing analysis: an experimental investigation on a multi-core architecture

On vulnerabilities in EVT-based timing analysis: an experimental investigation on a multi-core architecture

Focusing on features with higher importance degree: A significance-wise mechanism for image restoration

With the aim of retrieving high-quality images from corrupted versions, image restoration meets the extensive demand of application scenarios. State-of-the-art methods solve this problem by means of designing convolution blocks in multistage architectures. However, the existing methods blindly extract and fuse features without considering which parts of the features are more effective for image restoration. In this paper, we propose a significance-wise mechanism with the goal of identifying the importance degree of each region in the feature representation and providing crucial feature information. Through the calculation of nonlinear function in the significance-wise mechanism, the region which is more conducive to image restoration will get higher importance values. Two important types of information are generated within the attention mechanism, including: (i) feature-sufficient maps with abundant feature representations, and (ii) significance-wise maps with the importance degree of patch information in the corrupted images. With the coordination of feature-sufficient maps and significance-wise maps, the network architecture can focus higher attention on crucial parts of feature information. Furthermore, we design a multistage feature fusion block with the significance-wise mechanism. Compared with existing attention mechanisms, our significance-wise mechanism has the ability to identify and generate crucial feature representation for multiple image restoration tasks. Due to the novel design of the network, we successfully implement multiple restoration tasks only by fine-tuning the number of channels once on a network. Abundant quantitative and qualitative experiments demonstrate that the effective image restoration network (EIRN) outperforms existing state-of-the-art algorithms on eleven datasets across a series of image restoration tasks, including image deblurring, denoising, super-resolution, and deraining. The source code and pretrained models will be available at https://github.com/XinyueZhangqdu/EIRN.

Performance Analysis of a Multicore Approach Proposed for Efficient Community Detection and Recommendation System

Community detection is a well-known area of research that yields essential results in various fields such as social media, biological networks, pandemic spread, recommendation systems, etc. There are two important areas for improvement in existing community detection algorithms: quality of community formed should be improved and a parallel approach to community detection is needed to handle massive data. In this paper, we have proposed a three-step parallel algorithm, Par-Com, using the concept of k Clique and modularity optimization to address both of the above issues. The proposed algorithm increases the execution speed and also improves the quality of the community formed by optimizing modularity. Par-Com uses dynamic load balancing on the multicore architecture of Supercomputer ParamShivay. We have also evaluated Par-Com’s performance against nine sequential and three parallel community detection algorithms on varying size datasets, i.e. karate, macaque, email, immuno, soc-epinions, facebook, and com-Friendster. The experiment result shows that Par-Com outperforms other algorithms under consideration with up to 45% increase in modularity and up to 84% increase in execution speed. Par-Com is also capable of detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes. Nodes within a community that have the highest influence are deemed as experts. The choices made by an expert in a particular community are served as recommendations to other users within that community.

Solution of nonlinear fractional-order models of nuclear reactor with parallel computing: Implementation on GPU platform

This paper present novel parallel computing algorithms for numerical solution of nonlinear fractional-order (FO) models of nuclear reactor. These FO models arise by virtue of considering neutron transport in nuclear reactor as subdiffusion. The numerical computation of these FO models is carried out by parallelizing the sequential steps in Adams–Bashforth–Moulton method. A comparison of parallel execution on MATLAB and CUDA platforms is also given. The parallel computation is executed by harnessing multicore architecture of the general purpose graphics processing unit (GPGPU). A detailed analysis of the proposed procedure is presented considering different values of fractional derivative order in the fractional differential equations (FDEs). It is shown with extensive computational exercise that the proposed methodology achieves substantial speed up for all the three FDEs models used in nuclear reactor.

A consensus-based alternating direction method of multipliers approach to parallelize large-scale minimum-lap-time problems

Minimum-lap-time planning (MLTP) problems, which entail finding optimal trajectories for race cars on racetracks, have received significant attention in the recent literature. They are commonly addressed as optimal control problems (OCPs) and are numerically discretized using direct collocation methods. Subsequently, they are solved as nonlinear programs (NLPs). The conventional approach to solving MLTP problems is serial, whereby the resulting NLP is solved all at once. However, for problems characterized by a large number of variables, distributed optimization algorithms, such as the alternating direction method of multipliers (ADMM), may represent a viable option, especially when multicore CPU architectures are available.This study presents a consensus-based ADMM approach tailored to solving MLTP problems through a distributed optimization algorithm. The algorithm partitions the problem into smaller subproblems based on different sectors of a track, distributing them among multiple processors. ADMM is then used to ensure consensus among the distributed computational processes. In particular, here the term “consensus” denotes the requirement for each subproblem to achieve mutual agreement across the junction areas. The paper also outlines specific strategies leveraging domain knowledge to improve the convergence of the distributed algorithm. The ADMM approach is validated against the serial approach, and numerical results are presented for both single-lap and multilap scenarios. In both cases, the ADMM approach proves superior for problem dimensions of 70k+ variables compared to serial methods. In planning scenarios with complex vehicle models on long track horizons, i.e., for problems with 1M+ variables, the efficiency gain of the ADMM approach is substantial, and it becomes the only viable option to maintain computational times within acceptable limits.

End-to-End Service Availability in Heterogeneous Multi-Tier Cloud–Fog–Edge Networks

With the evolution towards the interconnected future internet spanning satellites, aerial systems, terrestrial infrastructure, and oceanic networks, availability modeling becomes imperative to ensure reliable service. This paper presents a methodology to assess end-to-end availability in complex multi-tiered architectures using a Markov model tailored to the unique characteristics of cloud, fog, edge, and IoT layers. By quantifying individual tier reliability and combinations thereof, the approach enables setting availability targets during the design and evaluation of operational systems. In the paper, a methodology is proposed to construct a Markov model for the reliability of discrete tiers and end-to-end service availability in heterogeneous multi-tier cloud–fog–edge networks, and the model is demonstrated through numerical examples assessing availability in multi-tier networks. The numerical examples demonstrate the adaptability of the model to various topologies from conventional three-tier to arbitrary multi-level architectures. As connectivity becomes ubiquitous across heterogeneous devices and networks, the proposed approach and availability modeling provide an effective tool for reinforcing the future internet’s fault tolerance and service quality.

Energy-Efficient Federated Edge Learning in Multi-Tier NOMA-Enabled HetNet

We propose a novel multi-tier (top, intermediate, and bottom tiers) architecture at the edge of a heterogeneous network (HetNet) where non-orthogonal multiple access (NOMA) provides access to user equipment (UE) to participate in federated edge learning (FEL). The HetNet consists of a macro base station (MBS) and several small base stations (SBSs) where each BS is equipped with an edge server (ES). SBSs use the same system bandwidth to increase the system capacity. The top tier consists of the MBS-ES which works as the global model aggregator while ESs of SBSs and UEs connected with MBS reside in the intermediate tier. Similarly, UEs connected with an SBS-ES of the intermediate tier occupy the bottom tier. ESs of SBSs work as the intermediate model aggregators between the ES of the top tier and the UEs of the bottom tier. To minimize the total energy consumption (EC) for local computing (LC) and uplink transmission (UT) of UEs, we formulate a non-linear programming (NLP) optimization problem, present our solution by decomposing the problem into sub-problems, and propose two sequential algorithms to estimate EC for both LC and UT with less complexity. Our extensively simulated results demonstrate the viability of our proposed work.

Comprehensive characterization and development of multi-core shell superparamagnetic nanoparticles for controlled delivery of drugs and their kinetic release modelling

The nanoparticles designed for application in cancer treatment should have biocompatibility, colloidal stability and triggered release at tumor sites. Magnetic nanoparticles arise as an interesting option to be used as drug nanocarriers, considering the possibility of driving nanoparticles to the correct delivery site and exploring different triggers to achieve such accomplishment. In this study, nickel ferrite nanoparticles are explored as a magnetic core for drug delivery systems, using doxorubicin and omeprazole as model drugs. The developed nickel ferrite presents a strong superparamagnetic behavior and high purity, as demonstrated by magnetometry and TGA results. The carbon-coating procedure and functionalization allowed the nanoparticle to achieve the desired characteristics for biomedical applications (i.e. stability in water, biocompatibility, and size). According to TEM results, the final carbon-coated magnetic nanoparticles have an average size of 25.09 ± 0.58 nm and multi-core shell architecture, which is suitable for biomedical applications as drug nanocarriers. In addition, DLS demonstrated that functionalized nanoparticles are monodisperse, with a hydrodynamic diameter of 167 ± 59 nm, which fits the recommended range (100–200 nm) to benefit from enhanced permeability and retention effect. Drug loading tests with doxorubicin and omeprazole revealed the versatility of the designed nanoparticles, able to load 97% of doxorubicin and 51% of omeprazole. The pH-triggered release was also confirmed for both pharmacological compounds, showing a higher cumulative drug under acidic conditions (simulating a tumor microenvironment). Finally, the kinetic analysis applied to the study of the release mechanism of both medicines showed that non-linear models fit with higher accuracy the experimental data.

A Multi-stage framework for COVID-19 detection and severity assessment from chest radiography images using advanced fuzzy ensemble technique

Chest radiography (CXR) is a popular imaging modality for screening lung abnormalities. It plays a vital role in monitoring disease progression, allocating limited medical resources effectively, and planning treatment in clinical practice. However, manual CXR image screening is time-consuming and susceptible to human errors. Therefore, computer-aided diagnosis (CAD) systems can be an additional decision-making tool for radiologists. Although prior research had remarkable success in COVID-19 classification tasks, it is still difficult to accurately localize the infection and quantify its severity, which restricts the generalizability and interpretability of CAD-based systems. This work proposes an end-to-end multi-stage architecture for COVID-19 classification, infection region identification, and disease severity assessment. The first stage of the proposed architecture identifies the relevant lung regions by eliminating the irrelevant portions. The second stage discriminates COVID-19 from pneumonia and normal images. In this study, instead of a single pre-trained model, we used three models and a fuzzy rank-based ensemble method to combine the results. Unlike the simple ensemble techniques, the proposed approach generates the final predictions for the test samples by evaluating the confidence in the predictions of the base models. In the final stage of the network, the infection regions are identified, and the amount of infection is calculated when the image is classified as COVID-19. Subsequently, a severity score ranging from 0 to 3 is assigned based on the level of infection severity. Based on the predicted scores, COVID-19 images are categorized into four severity levels: mild, moderate, severe, and critical. The final ensemble segmentation model exceeds all the individual segmentation models with an IoU score of 98.99% and a dice score of 99.49%. Additionally, the infection segmentation model also outperforms all other segmentation models individually, achieving a dice score of 96.75% and an IoU score of 93.71%. Our proposed segmentation-based classification model achieves a maximum accuracy of 98.05% with an F1-score of 97.76% and outperforms five state-of-the-art (SOTA) CNN models and five existing ensemble techniques in the literature. An external dataset also validated the model, and the results showed an accuracy of 97.63% and an F1-score of 97.22%.

Background-detail restoration image deraining network based on convolutional dictionary network

The heavy rain streaks can seriously fade the color of images and destroy the background texture information. Although the existing deraining methods perform well in rain removal, we find that most of them tend to favor rain removal or restoration. In this paper, we propose a background-detail restoration image deraining network based on convolutional dictionary network to achieve effective deraining results with better-restored background details. Our framework includes two sub-networks, namely the rain-removal network and the background-restoration network. The former sub-network is constructed as the multi-stage architecture. In order to accurately maintain as much background detail as possible while removing rain streaks, we break down the overall rain removal process into more manageable steps. We introduce convolutional dictionary network and Multi-Scale Detail Restoration Block (MSDRB) to achieve the balance between the effectiveness of deraining and background restoration. The second sub-network is Residual-Unet with Squeeze-and-Excitation (RUSE) operation, which can utilize the output of every stage generated from the former sub-network to fine-tune the background details in rain-free images. Experiments on benchmark public synthetic datasets and real-world datasets demonstrate the effectiveness and generalization capability of the proposed method in rain streak removal and background restoration with different rain patterns.

Efficient microwave absorbing performance of Ni/carbon nanotubes assembled coronal hollow clusters

The development of wide-band microwave absorbing materials based on carbonaceous fillers remains a great challenge. In this study, carbon nanotubes (CNT) assembled coronal hollow clusters with efficient microwave absorbing performance have been obtained through successive hydrothermal and calcination process. Microstructural analysis reveals the distinct features of multiple assembly structures in the fabricated materials. The electromagnetic responses of Ni/CNT assembled clusters were examined across a frequency range of 2–18 GHz. Results demonstrate that the microwave frequency dispersion of electromagnetic parameters and reflection loss of Ni/CNT clusters are strongly influenced by weight loading. Multi-absorbing peaks with reflection loss better than − 40 dB could be achieved, while the equivalent absorbing band for RL< −10 dB covers 3.2–18 GHz, which account for 92.5% of the whole evaluated frequency range, and 62.5% for condition of RL< −20 dB. Furthermore, intrinsic attenuation beyond thickness derived semi-wavelength resonant absorption makes a major contribution to this superior performance. The underlying mechanism of dielectric response was investigated through Cole-Cole analysis and finite element simulation. ‘Micro-dipoles’ relaxation and hopping relaxation originated from defects within CNT and barrier between CNT, while the high magnetic dissipation capability of Ni nanoparticles make a major contribution to overall absorbing performance. All these are beneficial to regulation and optimization of microwave absorption performance of novel Ni/ CNT functionalized multistage architecture.

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The latter can be divided into custom accelerators (e.g., ASICs) and programmable domain-specific cores (e.g., DSIPs). VWR2A [1] is a programmable architecture that integrates high computational density and low power memory structures. The flexibility of VWR2A allows a large portion of applications to be covered, resulting in better performance and energy efficiency than ASICs and general-purpose processors. However, while this has been well studied for data-intensive kernels, this is not the case for control-intensive kernels —code with complex if-else and nested loop structures. Traditionally, control-intensive code is left to be executed by the host processor. This situation unnecessarily restricts the potential impact of energy-efficient acceleration, especially at the application level. In this paper, we evaluate the performance and energy consumption of VWR2A for control-intensive code and compare it with an ARM Cortex-M4 processor and a RISC-V Ibex processor. The performance and energy consumption are evaluated at the kernel and application levels. Our results confirm that VWR2A is faster and more energy-efficient than the two considered general-purpose processors also for control-intensive code.

A hybrid crossbar-ring on chip network topology for performance improvement of multicore architectures

A hybrid crossbar-ring on chip network topology for performance improvement of multicore architectures

Time–Energy Correlation for Multithreaded Matrix Factorizations

The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance.

Fuzzy-based Misbehavior Detection for Internet of Things in Multi-access Edge Computing Environment

Today, an emerging paradigm of edge computing, called Multi-Access Edge Computing (MEC), is widely used in a broad range of applications. MEC has been recently used as a driving technology for 5G networks. However, MEC encompasses some security problems, such as communicating with untrusted third-party service providers commonly known as location-based service (LBS) providers. In this paper, we propose a novel fuzzy-based misbehavior detection system for Internet of Things (IoT) in a 5G-based MEC environment to solve the previous problems. Our proposed system is comprised of a multi-tier architecture that is linked by IoT gateways and mobile core network. In our architecture, we employ a novel mechanism that embeds a security entity called Regional Certificate Authority in a 5G macro base station. This security entity acts as a passport for the IoT gateway to communicate with other security entities present in our system that are located in the core infrastructure. Besides that, our system utilizes a distributed certificate revocation mechanism for malicious LBS providers via simultaneously updating the internal blocklists employed by more than one system entity. In addition, we adopt in our architecture a novel penalty system for malicious LBS providers, which is used for penalizing providers in case proved to be malicious as well as actively isolating them from the network. On the other hand, our system provides conditional anonymity for the incident’s reporter. Also, we present various security features maintained by the proposed system to thwart well-known attacks as well as we demonstrate efficiency and robustness of our system. Finally, we provide the overhead of our system and compare it with other state-of-the-art approaches where results show that the proposed system relatively incurs the least overhead.

McDVFS: cycle conserving DVFS scheduler for multi-core mixed criticality systems

Multi-core architectures have grown to be a popular choice for deploying Mixed Criticality Systems (MCS). The focus of research in MCS has been to provide timing assurances for jobs with different criticality levels. Due to their significant processing demands and energy-aware/constrained nature, energy conservation in these systems is becoming mandatory. This article presents, mcDVFS, an energy management technique based on Dynamic-Voltage-and-Frequency-Scaling for multi-core MCS. mcDVFS achieves significant energy reduction while maintaining timing guarantees. It also prioritizes quality of service whenever feasible. Extensive experimental simulations show energy savings of ≈ 52% and 34% when compared to EDF-VD and EDF-VD with QoS.

CasaPuNet: Channel Affine Self-Attention- Based Progressively Updated Network for Real Image Denoising

Recently, the popularity of deep learning has brought broad applications of computer vision technology in industrial information systems. However, the process of image acquisition will inevitably introduce noise, which may heavily degrade image visual quality. Most of the proposed denoising methods are non-blind and they have limited performance in removing real noise with different noise levels. To overcome this problem, we propose a deep CNN-based blind model, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , channel affine self-attention based progressively updated network (CasaPuNet) for real image denoising. First, we introduce degradation mapping module (DMM) to extract degradation information, which makes the remaining subnetwork of CasaPuNet perform non-blind denoising. Then, CasaPuNet adopts a multi-stage architecture, which resolves the large gap between the noisy input and clean output into several small gaps and eliminates these small gaps step by step through progressive inference. Finally, a novel channel affine self-attention (CASA) is designed to adaptively fuse the features from multiple stages according to input statistics. Specifically, CASA extracts channel information from different features and converts them into channel weights through an affine structure for adaptive adjustment. CASA brings a significant performance gain with a small number of parameters. Extensive experiments demonstrate that CasaPuNet outperforms state-of-the-art denoising methods both quantitatively and visually. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chaoren88/CasaPuNet</uri> .

Fast slope algorithm with the use of vectorization and parallelization for multicore architectures

The slope calculation algorithm is one of the most widely used geospatial algorithms employing the 3x3 moving window technique (along with calculation of aspect, curvature and flow direction). This work presents an approach consisting of transforming a slope algorithm from a sequential form into a version that can exploit vector and parallel traits of multicore architectures with vector instructions. This approach allows us to take advantage of the potential of the modern multicore processors. The basic idea for optimizing the 3x3 moving window computation is to split the equation used to calculate the result into parts that operate on data that are known to exist in adjacent memory locations. The research was conducted on two multicore architectures without the change in the code — the older architecture was Sandy Bridge and the newer one was Haswell (with more cores). The efficiency of the developed slope algorithm was verified in practice with the use of DEM files of the same resolution but of different sizes. We showed through the numerical experiments that our approach gives better time performance than the original algorithm (and other tools) — and with no loss of accuracy.

Hungarian Qubit Assignment for Optimized Mapping of Quantum Circuits on Multi-Core Architectures

Hungarian Qubit Assignment for Optimized Mapping of Quantum Circuits on Multi-Core Architectures

A Simulation Framework for Electrochemical Processes with Electrolyte Flow

Software tools for simulating electrochemical processes (e.g. COMSOL Multiphysics, ELSYCA) are mostly of commercial type. Besides, three-dimensional simulations in complex cell geometries are known to become resource-expensive, as typically thin concentration boundary layers need to be resolved. This work presents a simulation framework for electrochemical processes based on the open source platform OpenFOAM. The finite volume method used and combined with domain decomposition is able to efficiently benefit from multi-core computer architectures. Our framework takes into account electrolyte flow, which is well known to affect mass transfer, and allows to consider multi-species electrolytes and forcing of the electrolyte. The stability and fast convergence of the method presented is found to rely on the linearization of the Butler-Volmer condition in the iterative solver. The framework is validated against an analytical solution valid for simplified conditions and an electrodeposition process at a conically shaped electrode in an external magnetic field. The latter exhibits transient departure of the concentration boundary layer from the cathode, and excellent agreement with COMSOL simulation results is found.