Multi-core Support Research Articles

Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description

Remaining useful life prediction of industrial robot RV reducer with multiple deep networks and multicore support vector data description

Mining of extended signal temporal logic specifications with ParetoLib 2.0

Cyber-physical systems are complex environments that combine physical devices (i.e., sensors and actuators) with a software controller. The ubiquity of these systems and dangers associated with their failure require the implementation of mechanisms to monitor, verify and guarantee their correct behaviour. This paper presents ParetoLib 2.0, a Python tool for offline monitoring and specification mining of cyber-physical systems. ParetoLib 2.0 uses signal temporal logic (STL) as the formalism for specifying properties on time series. ParetoLib 2.0 builds upon other tools for evaluating and mining STL expressions, and extends them with new functionalities. ParetoLib 2.0 implements a set of new quantitative operators for trace analysis in STL, a novel mining algorithm and an original graphical user interface. Additionally, the performance is optimised with respect to previous releases of the tool via data-type annotations and multi core support. ParetoLib 2.0 allows the offline verification of STL properties as well as the specification mining of parametric STL templates. Thanks to the implementation of the new quantitative operators for STL, the tool outperforms the expressiveness and capabilities of similar runtime monitors.

Inglämnlagare: a tool for restructuring Swedish HER record site data for statistical analysis.

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Adding multi-core support to the ALICE Grid Middleware

The major upgrade of the ALICE experiment for the LHC Run3 poses unique challenges and opportunities for new software development. In particular, the entirely new data taking and processing software of ALICE relies on process parallelism and large amounts of shared objects in memory. Thus from a single-core single thread workload in the past, the new workloads are exclusively multithreaded. This requires a profound change in the ALICE Grid midleware job handling, from scheduling to execution, and thus the entire middleware has been rewritten during the past 3 years to support the new multithreaded reality.This paper presents the ALICE middlewre development for multi-core job management and the tools used to achieve an efficient and secure environment. In particular, it covers job isolation and scheduling and how they can be implemented in different site configurations, such as sites shared with other experiments or High Performance Computing resources.

Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment.

Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

Inglämnlagare - a tool for restructuring Swedish site data for statistical analysis

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.

Toward Practical Weakly Hard Real-Time Systems: A Job-Class-Level Scheduling Approach

Recent applications of the Internet of Things and cyber–physical systems require the integration of many sensing and control tasks into resource-constrained embedded devices. Such tasks can often tolerate a bounded number of timing violations. The concept of weakly hard real-time systems can effectively improve resource efficiency without sacrificing system safety. However, the existing studies have limitations on their practical use due to the restrictions imposed on the task timing behavior, high analysis complexity, and the lack of multicore support. In this article, we propose a new job-class-level fixed-priority preemptive scheduler and its schedulability analysis framework for weakly hard real-time tasks. Our proposed scheduler employs the meet-oriented classification of jobs of a task in order to reduce the worst-case temporal interference imposed on other tasks. Under this approach, each job is associated with a “job-class” that is determined by the number of deadlines previously met (with a bounded number of consecutively missed deadlines). This approach allows decomposing the complex weakly hard schedulability problem into two subproblems that are easier to solve: 1) analyzing the response time of a job with each job-class, which can be done by an extension of the existing task-level analysis and 2) finding possible job-class patterns, which can be modeled as a simple reachability tree. We also present a semipartitioned task allocation method for multicore platforms, which enhances the schedulability of weakly hard tasks under the proposed scheduling framework. Experimental results indicate that our scheduler outperforms the prior work in terms of task schedulability and analysis time complexity. We have also implemented a prototype of a job-class-level scheduler in the Linux kernel running on Raspberry Pi with acceptably small-runtime overhead.

Research on a Thangka Image Classification Method Based on Support Vector Machine

As an art image, Thangka images have rich themes, various forms of expression, complex picture content and many layers of color representation. This paper mainly constructs a multi-core support vector machine (SVM) based on the information entropy feature-weighted radial basis kernel function. In this paper, the kernel function is optimized, and the feature reduction is performed by using the random forest feature selection algorithm with average accuracy degradation. Finally, the effective classification of the icon image and the mandala image in Thangka is realized. The research results provide support for the follow-up study of Thangka image annotation and retrieval.

Multi-core SVM optimized visual word package model for garment style classification

One clothing style classification research algorithm based on multi-core support vector machine (SVM) optimized visual word package model has been proposed to improve the computational performance of clothing style classification algorithm effectively. Firstly, detection technique of human body parts has been adopted to locate key parts of clothing and remove redundant information, which improves the accuracy of attribute classification; secondly, multi-core SVM characteristics has been applied to map input clothing style from original data space to high-dimensional data space. Lagrange method has been adopted to realize dual solving of original problem and then realize clothing style multi-core SVM classification recognition based on Mercer principle; finally, effectiveness of the proposed method has been verified through experimental test.

Mplot: An R Package for Graphical Model Stability and Variable Selection Procedures

The mplot package provides an easy to use implementation of model stability and variable inclusion plots (M\"uller and Welsh 2010; Murray, Heritier, and M\"uller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao 2009) for linear and generalised linear models. We provide a number of innovations on the standard procedures and address many practical implementation issues including the addition of redundant variables, interactive visualisations and approximating logistic models with linear models. An option is provided that combines our bootstrap approach with glmnet for higher dimensional models. The plots and graphical user interface leverage state of the art web technologies to facilitate interaction with the results. The speed of implementation comes from the leaps package and cross-platform multicore support.

Multi-core model checking and maximum satisfiability applied to hardware-software partitioning

Bounded model checking (BMC) based on satisfiability modulo theories (SMT) is well-known by its capability to verify software. However, its use as optimisation tool, to solve hardware and software (HW-SW) partitioning problems, is something new. In particular, its integration with the maximum satisfiability solver vZ tool, which provides a portfolio of approaches for solving linear optimisation problems over SMT formulas, is unprecedented. We present new alternative approaches to solve the HW-SW partitioning problem. First, we use SMT-based BMC in conjunction with a multi-core support using open multi-processing to create four variants to solve the partitioning problem. The multi-core SMT-based BMC approaches allow initialising many verification instances based on the number of available processing cores, where each instance checks a different optimum value until the optimisation problem is satisfied. Additionally, we integrate the vZ into the BMC, making it a specialised solution for optimisation in a single-core environment. We implement all five approaches on top of the efficient SMT-based context-bounded model checker (ESBMC) and compare them to a state-of-the-art optimisation tool. Experimental results show that there is no single optimisation tool to solve all HW-SW partitioning benchmarks, but based on medium-size benchmarks, ESBMC-vZ had better performance.

Multi-core model checking and maximum satisfiability applied to hardware-software partitioning

Bounded model checking (BMC) based on satisfiability modulo theories (SMT) is well-known by its capability to verify software. However, its use as optimisation tool, to solve hardware and software (HW-SW) partitioning problems, is something new. In particular, its integration with the maximum satisfiability solver vZ tool, which provides a portfolio of approaches for solving linear optimisation problems over SMT formulas, is unprecedented. We present new alternative approaches to solve the HW-SW partitioning problem. First, we use SMT-based BMC in conjunction with a multi-core support using open multi-processing to create four variants to solve the partitioning problem. The multi-core SMT-based BMC approaches allow initialising many verification instances based on the number of available processing cores, where each instance checks a different optimum value until the optimisation problem is satisfied. Additionally, we integrate the vZ into the BMC, making it a specialised solution for optimisation in a single-core environment. We implement all five approaches on top of the efficient SMT-based context-bounded model checker (ESBMC) and compare them to a state-of-the-art optimisation tool. Experimental results show that there is no single optimisation tool to solve all HW-SW partitioning benchmarks, but based on medium-size benchmarks, ESBMC-vZ had better performance.

Research of a resource-efficient, real-time and fault-tolerant wireless sensor network system

Memory optimization, energy conservation, real-time scheduling and fault tolerance are the essential research topics in the wireless sensor network (WSN). In this paper, a memory-efficient, energy-efficient, real-time and fault-tolerant WSN system LiveOS is designed and implemented. Compared to the other systems, LiveOS has several typical features. Firstly, the optimized Rate-Monotonic Scheduling (RMS) scheduling algorithm is implemented. By doing this, the memory cost of the multithreaded real-time operating system (RTOS) can be 47.3% optimized if compared to the traditional single-core node. Consequently, the RTOS becomes appropriate to run even on the memory-constrained WSN nodes. Secondly, the new research approach that addresses the WSN challenges by combining both the multi-core hardware technique and the software technique is applied in LiveOS. By means of the multi-core hardware support, the energy cost of LiveOS can be 35% optimized compared to the traditional single-core platform. Moreover, the real-time scheduling performance and the fault-tolerant ability of the WSN nodes can be optimized significantly. Due to the above features, LiveOS becomes the WSN system which has small memory footprint, long lifetime, high real-time scheduling performance and upstanding fault-tolerant ability. The evaluation on the different prototype nodes proves that LiveOS is appropriate to run on the resource-constrained WSN platforms and is competent to execute the outdoor real-time WSN applications.

The Bio3D Project: Interactive Tools for Structural Bioinformatics

We present extensive updates to Bio3D, a package for both interactive and batch analysis of biomolecular structure, sequence and molecular simulation data. These updates include unique high-throughput ensemble normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, new consensus methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis, interactive structure ensemble visualization as well as multicore support for many time intensive tasks. We demonstrate a new online WebApp server powered by Bio3D for comparative protein structure analysis. This server facilitates: (1) The identification of related protein structures to user specified thresholds of similarity; (2) Their alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) ensemble normal mode analysis for comparison of predicted deformations across protein families. The WebApp offer unparalleled capabilities for ensemble analysis of protein structures in a user friendly web browser environment from http://thegrantlab.org/bio3d/webapps. The previous version of Bio3D has been downloaded by over 13,700 researchers and cited over 200 times in the last six years. Merging these new methods with the existing Bio3D infrastructure represents an important advance that we hope will further stimulate biophysicists to use structural bioinformatics methods as an aid in solving their research problems. The Bio3D package and associated WebApps are distributed with full source code and extensive documentation under a GPL2 license from http://thegrantlab.org/bio3d/.

Efficient provisioning for multi-core applications with LSF

Tier-1 sites providing computing power for HEP experiments are usually tightly designed for high throughput performances. This is pursued by reducing the variety of supported use cases and tuning for performances those ones, the most important of which have been that of singlecore jobs. Moreover, the usual workload is saturation: each available core in the farm is in use and there are queued jobs waiting for their turn to run. Enabling multi-core jobs thus requires dedicating a number of hosts where to run, and waiting for them to free the needed number of cores. This drain-time introduces a loss of computing power driven by the number of unusable empty cores. As an increasing demand for multi-core capable resources have emerged, a Task Force have been constituted in WLCG, with the goal to define a simple and efficient multi-core resource provisioning model. This paper details the work done at the INFN Tier-1 to enable multi-core support for the LSF batch system, with the intent of reducing to the minimum the average number of unused cores. The adopted strategy has been that of dedicating to multi-core a dynamic set of nodes, whose dimension is mainly driven by the number of pending multi-core requests and fair-share priority of the submitting user. The node status transition, from single to multi core et vice versa, is driven by a finite state machine which is implemented in a custom multi-core director script, running in the cluster. After describing and motivating both the implementation and the details specific to the LSF batch system, results about performance are reported. Factors having positive and negative impact on the overall efficiency are discussed and solutions to reduce at most the negative ones are proposed.

The Bio3D Package: New Interactive Tools for Structural Bioinformatics

We present extensive updates to Bio3D, a package for the interactive analysis of biomolecular structure, sequence and molecular simulation data. Features include the ability to read and write biomolecular structure, sequence and dynamic trajectory data, query and search online sequence and structure databases, perform alignment, structural superposition, rigid core and dynamic domain identification, sequence and conformational clustering, distance and correlation matrix analysis, conservation analysis, normal mode analysis, principal component analysis, and many other common structural bioinformatics tasks. Bio3D also leverages the extensive graphical and statistical capabilities of the R environment (http://www.r-project.org) and thus provides a useful framework for the exploratory interactive analysis of biomolecular sequence and structure data. Recent notable additions to the package include consensus dynamical community analysis of network based coupled motions, enhanced coarse grained force fields and methods for normal mode analysis, and multicore support for many time intensive tasks. Here we describe these new capabilities with example applications. The previous version of Bio3D has been downloaded by over 13,700 researchers and cited over 100 times in the last six years. Merging these new methods represents an important advance that we hope will further stimulate biophysicists to use structural bioinformatics methods as an aid in solving their research problems. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/.

Adapting FreeRTOS for multicores: an experience report

Multicore processors are ubiquitous. Their use in embedded systems is growing rapidly, and given the constraints on uniprocessor clock speeds, their importance in meeting the demands of increasingly processor-intensive embedded applications cannot be understated. To harness this potential, system designers need to have available to them embedded operating systems with built-in multicore support for widely available embedded hardware. This paper documents our experience of adapting FreeRTOS, a popular embedded real-time operating system, to support multiple processors. A working multicore version of FreeRTOS that is able to schedule tasks on multiple processors as well as provide full mutual-exclusion support for use in concurrent applications is presented. Mutual exclusion is achieved in an almost completely platform-agnostic manner, preserving one of FreeRTOS's most attractive features: portability. Copyright © 2013 John Wiley & Sons, Ltd.

On-Chip Debug Architecture for Multicore Processor

Because of the intrinsic lack of internal-system observability and controllability in highly integrated multicore processors, very restricted access is allowed for the debugging of erroneous chip behavior. Therefore, the building of an efficient debug function is an important consideration in the design of multicore processors. In this paper, we propose a flexible on-chip debug architecture that embeds a special logic supporting the debug functionality in the multicore processor. It is designed to support run-stop-type debug functions that can halt and control the execution of the multicore processor at breakpoint events and inspect the possible causes of any errors. The debug architecture consists of the following three functional components: the core debug support block, the multicore debug support block, and the debug interface and control block. By embedding this debug infrastructure, the embedded processor cores within the multicore processor can be debugged simultaneously as well as independently. The debug control is performed by employing a JTAG-based scanning operation. We apply this on-chip debug architecture to build a debugger for a prototype multicore processor and demonstrate the validity and scalability of our approach.