Multi-core Techniques Research Articles

Multiple Kernel Clustering With Compressed Subspace Alignment.

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n2) memory consumption and O(n3) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O(n) and O(n2) , respectively. The proposed algorithm adopts a novel sampling strategy to enhance the performance and accelerate the speed of MKC. Specifically, we first mathematically model the sampling process and then learn it simultaneously during the procedure of information fusion. By this way, the generated anchor point set can better serve data reconstruction across different views, leading to improved discriminative capability of the reconstruction matrix and boosted clustering performance. Although the integrated sampling process makes the proposed algorithm less efficient than the linear complexity algorithms, the elaborate formulation makes our algorithm straightforward for parallelization. Through the acceleration of GPU and multicore techniques, our algorithm achieves superior performance against the compared state-of-the-art methods on six datasets with comparable time cost to the linear complexity algorithms.

Employing of Object Tracking System in Public Surveillance Cameras to Enforce Quarantine and Social Distancing Using Parallel Machine Learning Techniques

Like many countries, Jordan has resorted to lockdown in an attempt to contain the outbreak of Coronavirus (Covid-19). A set of precautions such as quarantines, isolations, and social distancing were taken in order to tackle its rapid spread of Covid-19. However, the authorities were facing a serious issue with enforcing quarantine instructions and social distancing among its people. In this paper, a social distancing mentoring system has been designed to alert the authorities if any of the citizens violated the quarantine instructions and to detect the crowds and measure their social distancing using an object tracking technique that works in real-time base. This system utilises the widespread surveillance cameras that already exist in public places and outside many residential buildings. To ensure the effectiveness of this approach, the system uses cameras deployed on the campus of Al-Zaytoonah University of Jordan. The results showed the efficiency of this system in tracking people and determining the distances between them in accordance with public safety instructions. This work is the first approach to handle the classification challenges for moving objects using a shared-memory model of multicore techniques. Keywords: Covid-19, Parallel computing, Risk management, Social distancing, Tracking system.

A low-cost and duplicable portable solar adaptive optics system based on LabVIEW hybrid programming

Abstract We have developed a portable solar adaptive optics (PSAO) for diffraction-limited imaging based on today’s multi-core personal computer. Our PSAO software is written in LabVIEW code, which features block-diagram function based programming and can dramatically speed up the software development. The PSAO can achieve a ~1000 Hz open-loop correction speed with a Shack–Hartmann Wave-front Sensor (SH-WFS) in 11 × 11 sub-aperture configuration. The image shift measurements for solar wave-front sensing are the most time-consuming computations in a solar adaptive optics (AO) system. Since our current LabVIEW program does not fully support multi-core techniques for the image shift measurements, it cannot fully take advantage of the multi-core computer’s power for parallel computation. In order to accelerate the AO system’s running speed, a dedicated message passing interface/open multi-processing parallel programming technique is developed for our LabVIEW-based AO program, which fully supports multi-core parallel computation in LabVIEW programming. Our experiments demonstrate that the hybrid parallel technique can significantly improve the running speed of the solar AO system, and this work paves the way for the applications of a low-cost and duplicable PSAO system for large solar telescopes.

PVcon: Localizing Hidden Concurrency Errors With Prediction and Verification

Multi-core techniques have been widely used in various hardware platforms, promoting development of concurrent programs. Unfortunately, concurrent programs are prone to concurrency errors. At present, it is still an open issue to provide high coverage detection of concurrency errors. In this paper, we present an enhanced dynamic concurrency error detection technique, called PVcon, which can detect more concurrency errors than existing techniques. PVcon first finds suspicious locations of concurrency errors by applying a novel relation, elastic-causalities (EC), and then strictly verifies each suspicious concurrency errors with a lightweight intentional scheduling technique. We have implemented PVcon in C/C++ and evaluated it by localizing concurrency errors from real-world programs. The experimental results show that PVcon can efficiently detect twice more concurrency errors than other techniques.

A −193.6 dBc/Hz FoMT 28.6-to-36.2 GHz Dual-Core CMOS VCO for 5G Applications

By combining the transformer feedback and VCO multi-core techniques, this paper presents a 0.5 V low voltage 28.6-to-36.2 GHz dual-core VCO for 5G applications. Based on the transformer feedback topology, with the power-ground interconnect inductor and embedded decouple capacitor, the second harmonic real impedance is achieved at the VCO cross-couple pair source terminal. Accordingly, the common-mode current path is reduced and the signal symmetry improved, suppressing the flicker noise up conversion. With a 4 bit switch capacitors and varactors, the proposed VCO achieves a 28.6-to-36.2 GHz (23.5 %) tuning range, and its flicker noise corner is from 250 to 980 kHz. Fabricated in a 65nm CMOS process, the VCO consumes 4.6mW from a 0.5 V supply voltage with a core area of about $0.24\times 0.5$ mm 2 . At 31 GHz, the proposed VCO achieves a phase noise of -103 dBc/Hz@1MHz offset, resulting in a figure-of-merit (FoM) of -186.2 dBc/Hz and tuning range figure-of-merit (FoMT) of -193.6 dBc/Hz, respectively.

P-HS-SFM: a parallel harmony search algorithm for the reproduction of experimental data in the continuous microscopic crowd dynamic models

ABSTRACTFinding the optimal parameters that can reproduce experimental data (such as the velocity–density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times.

High-Performance Video Condensation System

Video synopsis or condensation is a smart solution for fast video browsing and storage. However, most of the existing methods work offline, where two main phases are required. The first phase is to prepare tubes and background images. The second phase is to rearrange tubes and stitch them into backgrounds. However, with a long video sequence, the first phase is memory consuming for data storage, and the second phase is computationally expensive to rearrange all tubes simultaneously. To overcome these problems, we propose a high-performance video condensation system based on an online content-aware framework. The online framework transforms the optimization problem of tube rearrangement into a stepwise optimization problem. Therefore, it can condense video with much less memory and higher speed than the offline framework. With the aid of this transformation, the proposed system can process input videos and produce condensed videos simultaneously. Thus it is suitable for real-time endless surveillance videos. Meanwhile, the online mechanism allows users to directly visit the condensation video that has been generated. Moreover, the content-aware mechanism makes the proposed system able to automatically determine the duration of a condensed video. Finally, the proposed system uses Graphic Processing Unit (GPU) and multicore techniques to improve the speed. Extensive experiments that validate the high efficiency of the system are presented.

A Portable Method to Improve the Performance of Commodity Servers for Massive Data Delivery

With the progress of a Multi Core technique, the number of processes, which are executed concurrently on the server, has been increasing. Accordingly, the amounts of data transmitted by the servers, which deliver a large amount of data to many clients like VOD servers, also have been increasing dramatically. However, the powerful interconnection networks (Myrinet [1], Infiniband [2], etc.) are expensive without exception. Then this paper proposes the low-cost (low-priced and easy-work) method to improve the bandwidth performance. The method achieves it by bringing out the hardware performance of multiple GbE ports installed on the server. Due to advents of low-priced Multi GbE Ports Card and PCI Express slots, it is easy to install multiple GbE ports to the server. In addition, the method offers equal service (bandwidth) to all clients.

Multi-core based parallel computing technique for content-based image retrieval

In this paper, we propose a parallel computing technique for content-based image retrieval (CBIR) system. This technique is mainly used for single node with multi-core processor, which is different from those based on cluster or network computing architecture. Due to its specific applications (such as medical image processing) and the harsh terms of hardware resource requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database, the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition, we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which is supposed to gear to the new trend of the cloud computing.

Multicores in Cloud Computing: Research Challenges for Applications

With modern techniques that allow billions of transistors on a chip, microprocessor design is going to a world of multicore. A cluster of multicores will be commonly used as an efficient computational platform for high performance computing in the near future. Correspondingly, the resource providers, who share their computing elements and storage with customers, will also upgrade their machines to the multicore architecture. At the same time, the dominating resource sharing platforms, in the form of compute Grids, are facing a grand challenge from Cloud computing. With an easy interface, high-level services, and on-demand provision of compute resources, compute Clouds are attracting more users to join their realm. Upcoming Cloud environments will be primarily built on top of multicore technologies. However, to fully exploit the computational capacity and the other advantages of multicores, a lot of novel techniques must be proposed and investigated. This paper serves as an overview for multicore technologies in the context of Cloud computing. This paper firstly introduces multicore technologies and the Cloud computing concept. Then it investigates various open research issues in the context of multicore Cloud computing, for example, how to efficiently employ multicore techniques for Cloud computing, how to achieve the high performance of multicore for high performance applications in the Cloud environments. Several examples and discussion are presented.