Multi-core Research Articles

Analysis of High-Performance Parallel Computing using TOPSIS Method

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

Software architecture and implementation based on multi-core digital signal processors

Abstract Embedded detection systems are a critical research topic in intelligent monitoring equipment. In response to the current challenges in balancing real-time performance, accuracy, and stability in embedded hardware detection systems, this paper proposes a software architecture system based on multi-core digital signal processing (DSP), incorporating the image detection Canny algorithm into this system. Initially, the paper discusses the selection criteria for multi-core DSPs and then introduces a multi-core self-check function that can effectively ensure system stability. It also proposes a multi-core communication handshake protocol that significantly enhances the system’s real-time performance. Furthermore, an improved multi-core master-slave architecture is presented, which includes the addition of a timer to the conventional architecture, providing a temporal guarantee for system operation. Finally, the Canny algorithm is selected and applied to this software architecture, with parallel acceleration processing applied to parts of the algorithm. Operational tests have demonstrated that the system maintains stable internal data flow, with its detection accuracy and speed meeting the set requirements.

MP-ORAM: A Novel ORAM Design for Multicore Processor Systems

MP-ORAM: A Novel ORAM Design for Multicore Processor Systems

New generalizations of the core and dual core inverses

New generalizations of the core and dual core inverses

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces.This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency.

Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

Efficient Velvet-Noise Convolution in Multicore Processors

Velvet noise, a sparse pseudo-random signal, finds valuable applications in audio engineering, such as artificial reverberation, decorrelation filtering, and sound synthesis. These applications rely on convolution operations whose computational requirements depend on the length, sparsity, and bit resolution of the velvet-noise sequence used as filter coefficients. Given the inherent sparsity of velvet noise and its occasional restriction to a few distinct values, significant computational savings can be achieved by designing convolution algorithms that exploit these unique properties. This paper shows that an algorithm called the transposed double-vector filter is the most efficient way of convolving velvet noise with an audio signal. This method optimizes access patterns to take advantage of the processor’s fast caches. The sequential sparse algorithm is shown to be always faster than the dense one, and the speedup is linearly dependent on sparsity. The paper also explores the potential for further speedup on multicore platforms through parallelism and evaluate the impact of data encoding, including 16-bit and 32-bit integers and 32-bit floating-point representations. The results show that using the fastest implementation of a long velvet-noise filter, it is possible to process more than 40 channels of audio in real time using the quad-core processor of a modern system-on-chip.

Numerical investigation of lattice Boltzmann method-based large-eddy simulation in non-isothermal enclosed cavity airflow

This study implemented the lattice Boltzmann method-based large-eddy simulation (LBM-LES) in a wall-heated cavity turbulent flow problem with the Rayleigh number reaching 5.33 × 109. The effects of crucial parameters in LBM temperature simulation, including discrete time intervals, discrete external force models by temperature effect, and temperature discrete velocity schemes, were investigated to obtain the suggested values. In addition, the simulations' accuracy and computational efficiency were compared with those of FVM-LES. LBM-LES generally agreed with experiments, and the accuracy was comparable to FVM-LES. However, exceedingly small discrete time intervals induced numerical oscillations in velocity and temperature at high frequencies, yielding significant errors. The accuracy of the discrete external force model satisfied Guo model > primitive model > He model. For temperature particles' discrete velocity scheme, D3Q7 was already sufficiently accurate, and higher schemes (D3Q19 and D3Q27) did not increase the accuracy further. LBM-LES and FVM-LES were concordant with respect to predicting various physical quantities, and the accuracy of LBM-LES in the near-wall region was slightly higher than that of the FVM-LES. Single-core and multicore computational speeds of LBM-LES were 4–8 fold higher than FVM-LES. The multicore parallelism of LBM-LES also exceeded FVM-LES and became more evident as CPU cores increased.

Lavender: An Efficient Resource Partitioning Framework for Large-Scale Job Colocation

Workload consolidation is a widely used approach to enhance resource utilization in modern data centers. However, the concurrent execution of multiple jobs on a shared server introduces contention for essential shared resources such as CPU cores, Last Level Cache, and memory bandwidth. This contention negatively impacts job performance, leading to significant degradation in throughput. To mitigate resource contention, effective resource isolation techniques at the software or hardware level can be employed to partition the shared resources among colocated jobs. However, existing solutions for resource partitioning often assume a limited number of jobs that can be colocated, making them unsuitable for scenarios with a large-scale job colocation due to several critical challenges. In this study, we propose Lavender, a framework specifically designed for addressing large-scale resource partitioning problems. Lavender incorporates several key techniques to tackle the challenges associated with large-scale resource partitioning, ensuring efficiency, adaptivity, and optimality. We conducted comprehensive evaluations of Lavender to validate its performance and analyze the reasons for its advantages. The experimental results demonstrate that Lavender significantly outperforms state-of-the-art baselines. Lavender is publicly available at https://github.com/yanxiaoqi932/OpenSourceLavender.

The Digital Media Art and Design System under the Integration of Computer Big Data ChatGPT Technology

Traditional digital media art and design systems take a long time to run and cannot handle a large number of objects simultaneously. Therefore, the author proposes a digital media art and design method based on ChatGPT technology. ChatGPT technology is an important part of networking.Prepare to provide conference support for various applications such as customer service, personal assistant, robotic interview, and so on. ChatGPT can not only answer the question that is set, but also solve the question openly.The comprehensiveness and intelligence of ChatGPT have attracted widespread attention since its launch. The hardware system adopts dual core TMS320C6657 and DDR3Thel1333 series processors as the core circuit, together with high speed external storage, to expand the image transmission line of multi-data; System software uses module architecture to design image modules, video modules, and voice modules, all of which are integrated into the core of the system as modules. In this experiment, 10 images were transferred and the running time was tested. Experimental results showed that, the traditional digital media art and design system had three errors, which took more time to run.

GPU-based key-frame selection of pulmonary ultrasound images to detect COVID-19

In the last decades, technological advances have led to a considerable increase in computing power constraints to simulate complex phenomena in various application fields, among which are climate, physics, genomics and medical diagnosis. Often, accurate results in real time, or quasi real time, are needed, especially if related to a process requiring rapid interventions. To deal with such demands, more sophisticated approaches have been designed, including GPUs, multicore processors and hardware accelerators. Supercomputers manage high amounts of data at a very high speed; however, despite their considerable performance, their limitations are due to maintenance costs, rapid obsolescence and notable energy consumption. New processing architectures and GPUs in the medical field can provide diagnostic and therapeutic support whenever the patient is subject to risk. In this context, image processing as an aid to diagnosis, in particular pulmonary ultrasound to detect COVID-19, represents a promising diagnostic tool with the ability to discriminate between different degrees of disease. This technique has several advantages, such as no radiation exposure, low costs, the availability of follow-up tests and the ease of use even with limited resources. This work aims to identify the best approach to optimize and parallelize the selection of the most significant frames of a video which is given as the input to the classification network that will differentiate between healthy and COVID patients. Three approaches have been evaluated: histogram, entropy and ResNet-50, followed by a K-means clustering. Results highlight the third approach as the most accurate, simultaneously showing GPUs significantly lowering all processing times.

Self-centering multiple rocking core systems for mitigating higher mode effects

The structural member forces of mid-story and high-rise self-centering energy-absorbing dual rocking core (SEDRC) systems can be significantly aggravated by higher mode effects. This study aims to introduce the multiple rocking mechanisms into the SEDRC system and propose the self-centering multiple rocking core system (denoted as SMRC system) to reduce the structural member force demands resulting from the higher mode effects. The working principle of the SMRC system is discussed. The performance-based design (PBD) procedures for the SMRC system are presented. The comparative dynamic history analyses are conducted on a 9-story SMRC and two 9-story SEDRC systems. Incremental dynamic analyses (IDAs) are carried out to investigate the dynamic behaviors of the considered systems under increasing seismic intensities. The accuracy of the developed designed procedures is verified with the evidence that the developed SMRC system can achieve the design displacement under design earthquakes. The multiple rocking mechanism can efficiently mitigate structural member force demands caused by the effects of higher mode. The SMRC system can achieve better seismic performance with lower structural member force demands than the SEDRC systems when the earthquake intensity is not higher than the maximum considered earthquakes (MCE). Nevertheless, the SMRC system shows worse performance than the SEDRC systems in resisting structural collapse when the intensity is higher than MCE, which could be a research gap for further investigations.

Temperature self-compensated fiber-optic Pb(II) sensor with improved selectivity by Resorcylaldehyde post-modified organic metal framework

Post-modification of metal-organic framework (MOF) materials is an effective means to enhance their efficient and selective adsorption for heavy metal ions. In this article, we presented a reflection-type optical fiber surface plasmon resonance (SPR) sensor based on dual core fiber (DCF) coated with UiO-66-RSA (UiO-66-NH2 post-modified with resorcinol aldehyde) film for the specific detection of Pb(II) in aqueous media. The composite material UiO-66-RSA exhibits stronger affinity and specificity for Pb(II) than UiO-66-NH2. The adsorption of Pb(II) can change the equivalent refractive index (RI) of UiO-66-RSA film, causing a regular shift in SPR spectra. Experiments have shown that the limit of detection (LOD) of the SPR Pb(II) sensor can reach 0.1 nM. Meanwhile, research has found that the SPR sensor is accompanied by temperature crosstalk of 0.356 nm/°C. Therefore, we constructed a Michelson temperature interferometer using polymer micro tips on the end face of the fiber core to achieve temperature compensation. Based on the above two sensing channels, dual parameter demodulation of Pb(II) concentration and temperature can be achieved with an error rate of less than 0.5 %. This article has potential research value in exploring the application of post modification techniques of MOF in fiber optic lead ion detection.

VSP wavefields separating method based on parallel local slope scanning

Due to the detectors being distributed in the target medium, vertical seismic profile (VSP) is a seismic observation method that has the advantages of high resolution and a high signal-to-noise ratio. Separating the mixed wavefields into upgoing and downgoing waves can obtain more obvious dynamic and kinematic characteristics of seismic waves, guiding subsequent imaging and interpretation. The traditional method is mainly based on the pickup of first breaks. Flattening the global seismic data by first breaks can enhance the seismic events through techniques like median filter and singular value decomposition (SVD). However, this method relies on high-precision pickup of first breaks and is limited by zero offset. To address this limitation, we introduce an improved median filtering separation method. This method employs separations of the local dip angles into positive and negative through multi-window scanning (MWS). Due to the high accuracy and robustness of this method in 2-D VSP data, we propose to use the local dip angle of the wavefields to median filter the wavefield through positive and negative angles to obtain upgoing and downgoing waves. This method for wavefield separation is optimized by iteratively identifying the directions of the seismic data. However, this optimization comes at the cost of increased computational requirements, especially under high-precision conditions. To help alleviate this problem, we use multi-thread parallel computing technique on a multi-core central processing unit (CPU) to improve computational efficiency. Finally, we validate the proposed method by testing it on synthetic seismic data and field VSP data, respectively. The results show that this wavefield separation method has advantages in terms of accuracy and robustness compared to the median filtering method based on the first break picking.

Performance Study of Object Tracking with Multiple Kalman Filters in Autonomous Driving Systems

Object tracking is an important and central aspect of autonomous driving, as it underlies the obstacle detection and avoidance systems of any type of autonomous vehicles. A widely used method for tracking is based on Kalman filters, both for linear and non-linear cases, with different computational burden. Unfortunately, object tracking algorithms are computationally intensive, and they may not easily meet the efficiency and responsiveness requirements of real-time applications such as autonomous driving. This issue motivates ad-hoc investigations to speed up the computation and make Kalman filtering available even within limited computational power. This paper carry out a performance evaluation of a Kalman filter based object tracking system taken from a real tramway use-case, and aims at improving its performance efficiency by leveraging parallelization. In particular, this work analyzes the possibilities of execution parallelization on multi-core processors, proposing a target-specific optimization approach and comparing the obtained results, then summing them in general lessons learned. Our technique achieves up to 80% reduction of single frame processing time in the most crowded cases.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

GPUPRSI: GPU implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings

Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration.

Single layer dual hollow core antiresonant fiber based polarization beam splitters

To confront the growing challenges of integrated photonics, optical devices need to be compact in size to integrate them in communication networks where a polarization beam splitter device may play a crucial role. In this regard, hollow core antiresonant fibers have garnered significant attention as a versatile platform, for achieving efficient polarization splitting, owing to their promising characteristics. In this article, traditional and available multilayer complex cladding geometry, in dual hollow core antiresonant fiber, is simplified to single layer arrangement and created efficient polarization beam splitters device. First, we introduced a splitter with single layer cladding geometry that simultaneously presents a short device length of 2.24 cm and an ultra-wide bandwidth of 415 nm, with an extinction ratio exceeding 20 dB. This bandwidth also encompasses widely used communication wavelengths of 1.31μm and 1.55μm. Moreover, a careful investigation of geometrical dimensions, nesting, and conjoined tubes inclusion suggests that the device structure having two horizontal nested elements has an excellent splitting performance with a concise splitter length of 2.06 cm and an ultra-wide bandwidth of 465 nm, covering most telecom bands. Notably, the splitters have the highest extinction ratio of around 140 dB at 1.55μm, along with an effective single mode performance. Therefore, the claimed single layer polarization splitters might be an attractive candidate for the integration of optical devices in communication systems.

High Figure–of–Merit Multi–Core Fiber With Standard Cladding Diameter for Long–Haul and Wide–Band Transmission

High Figure–of–Merit Multi–Core Fiber With Standard Cladding Diameter for Long–Haul and Wide–Band Transmission

UBlade: Efficient Batch Processing for Uncertainty Graph Queries

The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional workloads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, achieving superior parallelism in the batch processing model. Additionally, our Quasi-Sampling technique reduces the random number generation cost by a factor of B, with O(B) denoting the batch size. We further examine the extra workload resulting from batch processing and introduce an efficient strategy to reorder possible worlds, minimizing this associated overhead. Through comprehensive evaluations, we showcase that uBlade achieves up to two orders of magnitude speedups against the state-of-the-art CPU and GPU-based solutions.