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Workflow Task Scheduling Algorithm for Marine Environment Data Processing under multi-core computing Environment

JOURNAL OF COASTAL RESEARCH 2018年第Spe83期000卷 646-650页

作者： Shen, Yang Qi, Deyu Zhou, Naqin Wang, Xinyang South China Univ Technol Inst Comp Syst Guangzhou 510006 Guangdong Peoples R China

When the current workflow task scheduling algorithm is used for marine environment data processing, there is a large amount of calculation, poor load balancing, and lower data transmission efficiency. Therefore, in this paper, a workflow task scheduling algorithm based on multi-task replication strategy is proposed for the marine environment data processing under multi-core computing environment. Firstly, based on the directed acyclic graph, the workflow task structure of marine environment data processing can be described, to establish workflow model, and build a multi-level task replication model the DAG diagram of workflow. The model is combined with the workflow model of marine environment data processing to decide whether to execute the replication task, so as to achieve the task scheduling of marine environment data processing in multi core computing environment. The experimental results show that the computational process of workflow task scheduling is relatively simple, which improves the load balance and the amount of data transmission.

关键词： multi-core computing marine environment data processing workflow task scheduling

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Effective Runtime Management of Tasks and Priorities in GNU OpenMP Applications

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2632-2645页

作者： Silvestri, Emiliano Pellegrini, Alessandro Di Sanzo, Pierangelo Quaglia, Francesco Sapienza Univ Rome I-00185 Rome Italy Lockless Srl I-00137 Rome Italy Univ Aquila I-67100 Laquila Italy Univ Roma Tor Vergata I-00133 Rome Italy

OpenMP has become a reference standard for the design of parallel applications. This standard is evolving quickly, thus offering new opportunities to the application programmers. However, OpenMP runtime environments are often not fully aligned with the actual requirements imposed by the evolution of such a standard. Among the main lacks, we find: (a) a limited capability to effectively cope with task priorities, and (b) the inadequacy in guaranteeing core properties while processing tasks such as the so-called work-conservativeness-the ability of the OpenMP runtime environment to fully exploit the underlying multi-processor/multi-core machine through the avoidance of thread-blocking phases. In this article, we present the design of extensions to the GNU OpenMP (GOMP) implementation, integrated into gcc, which allow the effective management of tasks and their priorities. Our proposal is based on a user-space library-modularly combined with the one already offered by GOMP-and an external kernel-level Linux module-offering the opportunity to exploit raising hardware facilities for task/priority management. We also provide experimental results showing the effectiveness of our proposal, achieved by running either OpenMP common benchmarks or a new benchmark application (Hashtag-Text) that we explicitly devised to stress the runtime environment in relation to the above-mentioned task/priority management aspects.

关键词： Task analysis Message systems Runtime environment Hardware Switches Proposals Instruction sets multi-core computing task parallelism operating system support

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ACt-PBL: An adaptive approach to teach multi-core computing in University education

ACt-PBL: An adaptive approach to teach Multi-core computing ...

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IEEE 5th International Conference on Technology for Education (T4E)

作者： Manogaran, Elavarasi VIT Univ Dept Comp Sci & Technol Vellore Tamil Nadu India

ISBN: (纸本)9780769551418

This paper presents an Active Collaborative PBL-based teaching methodology (ACt-PBL), for reinforcing teaching and learning of multi-core computing in the university education. This approach complements traditional classroom teaching strategy by incorporating live project demonstrations on multi-core, hands on training on parallel programming, practical code dependency analysis task, think-pair-share activity and open discussions on research issues in multi-core computing. Survey results show that 69% (approx.) of the students who attended the ACt-PBL lecture perceived that the lecture method encouraged them to think reflectively on the topic compared to only 36% of students from the traditional lecture. Analysis of assessment results show that the ACt-PBL methodology, being easily adaptable to the students' individual needs, has reduced the failure rate of students by approximately 50% vis-a-vis the traditional method. Using this method, a statistically significant (p=0.006) better learning outcome was obtained for freshmen when compared to their counterparts in the traditional lecture class. In contrast this method did not make any significant difference for the sophomores (p=0.74), who had higher prior knowledge than freshmen.

关键词： multi-core computing multi-threading parallel programming project based learning collaborative learning active learning

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Clustering Algorithms on Low-Power and High-Performance Devices for Edge computing Environments

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SENSORS 2021年第16期21卷 5395-5395页

作者： Lapegna, Marco Balzano, Walter Meyer, Norbert Romano, Diego Univ Naples Federico II Dept Math & Applicat I-80126 Naples Italy Univ Naples Federico II Dept Elect Engn & Informat Technol I-80126 Naples Italy Poznan Supercomp & Networking Ctr PL-61139 Poznan Poland CNR Inst High Performance Comp & Networking I-80131 Naples Italy

The synergy between Artificial Intelligence and the Edge computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

关键词： edge computing low-power devices multi-core computing GPU computing clustering algorithms

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Deferred Correction Methods for Ordinary Differential Equations

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JOURNAL OF SCIENTIFIC computing 2020年第3期83卷 1-29页

作者： Ong, Benjamin W. Spiteri, Raymond J. Michigan Technol Univ Dept Math Sci Houghton MI 49931 USA Univ Saskatchewan Dept Comp Sci 176 Thorvaldson Bldg110 Sci Pl Saskatoon SK S7N 5C9 Canada

Deferred correction is a well-established method for incrementally increasing the order of accuracy of a numerical solution to a set of ordinary differential equations. Because implementations of deferred corrections can be pipelined, multi-core computing has increased the importance of deferred correction methods in practice, especially in the context of solving initial-value problems. In this paper, we review the theoretical underpinnings of deferred correction methods in a unified manner, specifically the classical algorithm of Zadunaisky/Stetter, the method of Dutt, Greengard and Rokhlin, spectral deferred correction, and integral deferred correction. We highlight some nuances of their implementations, including the choice of quadrature nodes, interpolants, and combinations of discretization methods, in a unified notation. We analyze how time-integration methods based on deferred correction can be effective solvers on modern computer architectures and demonstrate their performance. Lightweight and flexible Matlab software is provided for exploration with modern variants of deferred correction methods.

关键词： Ordinary differential equations Initial-value problems Deferred correction Parallel computing Method of lines multi-core computing

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Parallel computing for genome sequence processing

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BRIEFINGS IN BIOINFORMATICS 2021年第5期22卷 bbab070-bbab070页

作者： Zou, You Zhu, Yuejie Li, Yaohang Wu, Fang-Xiang Wang, Jianxin Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Hunan Peoples R China Old Dominion Univ Comp Sci Norfolk VA 23529 USA Univ Saskatchewan Coll Engn Saskatoon SK Canada Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada Cent South Univ Sch Comp Sci & Engn Changsha Hunan Peoples R China

The rapid increase of genome data brought by gene sequencing technologies poses a massive challenge to data processing. To solve the problems caused by enormous data and complex computing requirements, researchers have proposed many methods and tools which can be divided into three types: big data storage, efficient algorithm design and parallel computing. The purpose of this review is to investigate popular parallel programming technologies for genome sequence processing. Three common parallel computing models are introduced according to their hardware architectures, and each of which is classified into two or three types and is further analyzed with their features. Then, the parallel computing for genome sequence processing is discussed with four common applications: genome sequence alignment, single nucleotide polymorphism calling, genome sequence preprocessing, and pattern detection and searching. For each kind of application, its background is firstly introduced, and then a list of tools or algorithms are summarized in the aspects of principle, hardware platform and computing efficiency. The programming model of each hardware and application provides a reference for researchers to choose high-performance computing tools. Finally, we discuss the limitations and future trends of parallel computing technologies.

关键词： parallel computing multi-core computing cluster computing heterogeneous computing genome sequence processing

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Combining multi-core and GPU computing for solving combinatorial optimization problems

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2013年第12期73卷 1563-1577页

作者： Chakroun, I. Melab, N. Mezmaz, M. Tuyttens, D. Univ Lille 1 LIFL UMR CNRS 8022 INRIA Lille Nord Europe F-59655 Villeneuve Dascq France Univ Mons Math & OR Dept B-7000 Mons Belgium

In this paper, we revisit the design and implementation of Branch-and-Bound (B&B) algorithms for solving large combinatorial optimization problems on CPU-enhanced multi-core machines. B&B is a tree-based optimization method that uses four operators (selection, branching, bounding and pruning) to build and explore a highly irregular tree representing the solution space. In our previous works, we have proposed a CPU-accelerated approach in which only a single CPU core is used and only the bounding operator is performed on the CPU device. Here, we extend the approach (LL-GB&B) in order to minimize the CPU-CPU communication latency and thread divergence. Such an objective is achieved through a GPU-based fine-grained parallelization of the branching and pruning operators in addition to the bounding one. The second contribution consists in investigating the combination of a CPU with multi-core processing. Two scenarios have been explored leading to two approaches: a concurrent (RLL-GB&B) and a cooperative one (PLL-GB&B). In the first one, the exploration process is performed concurrently by the CPU and the CPU cores. In the cooperative approach, the CPU cores prepare and off-load to CPU pools of tree nodes using data streaming while the CPU performs the exploration. The different approaches have been extensively experimented on the Flowshop scheduling problem. Compared to a single CPU-based execution, LL-GB&B allows accelerations up to ( x 160) for large problem instances. Moreover, when combining multi-core and CPU, we figure out that using RLL-GB&B is not beneficial while PLL-GB&B enables an improvement up to 36% compared to LL-GB&B. (C) 2013 Elsevier Inc. All rights reserved.

关键词： multi-core computing GPU accelerators Parallel branch-and-bound Flowshop scheduling problem

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Anonymous Readers Counting: A Wait-Free multi-Word Atomic Register Algorithm for Scalable Data Sharing on multi-core Machines

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2019年第2期30卷 286-299页

作者： Ianni, Mauro Pellegrini, Alessandro Quaglia, Francesco Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Univ Roma Tor Vergata Dipartimento Ingn Civile & Ingn Informat Rome Italy

In this article we present Anonymous Readers Counting (ARC), a multi-word atomic (1, N) register algorithm for multi-core machines. ARC exploits Read-Modify-Write (RMW) instructions to coordinate the writer and reader threads in a wait-free manner and enables large-scale data sharing by admitting up to (2(32) - 2) concurrent readers on off-the-shelf 64-bit machines, as opposed to the most advanced RMW-based approach which is limited to 58 readers on the same kind of machines. Further, ARC avoids multiple copies of the register content when accessing it-this is a problem that affects classical register algorithms based on atomic read/write operations on single words. Thus it allows for higher scalability with respect to the register size. Moreover, ARC explicitly reduces the overall power consumption, via a proper limitation of RMW instructions in case of read operations re-accessing a still-valid snapshot of the register content, and by showing constant time for read operations and amortized constant time for write operations. Our proposal has therefore a strong focus on real-world off-the-shelf architectures, allowing us to capture properties which benefit both performance and power consumption. A proof of correctness of our register algorithm is also provided, together with experimental data for a comparison with literature proposals. Beyond assessing ARC on physical platforms, we carry out as well an experimentation on virtualized infrastructures, which shows the resilience of wait-free synchronization as provided by ARC with respect to CPU-steal times, proper of modern paradigms such as cloud computing. Finally, we discuss how to extend ARC for scenarios with multiple writers and multiple readers-the so called (M,N) register. This is achieved not by changing the operations (and their wait-free nature) executed along the critical path of the threads, rather only changing the ratio between the number of buffers keeping the register snapshots and the number of thre

关键词： Atomic registers shared-memory multi-core computing wait-free synchronization instruction-set-architecture

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Persistent Asynchronous Adaptive Specialization for Generic Array Programming

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2019年第2期47卷 164-183页

作者： Grelck, Clemens Wiesinger, Heinrich Univ Amsterdam Amsterdam Netherlands

Generic array programming systematically abstracts from structural array properties such as shape and rank. As usual, generic programming comes at the price of lower runtime performance. The idea of asynchronous adaptive specialization is to exploit parallel computing facilities to reconcile these conflicting objectives through the continuous adaptation of running applications to the ranks and shapes of their arrays. A key parameter for the effectiveness of our approach is the time it takes from requesting a certain specialization until its availability to the running application. We describe the ins and outs of a persistence layer that keeps specialized variants in a repository for future use and thus effectively reduces the average waiting time for re-compilation to nearly zero. A number of critical issues that, among others, stem from the interplay between function specialization and function overloading catch our special attention. We describe the solutions adopted and illustrate the benefits of persistent asynchronous adaptive specialization by a series of experiments.

关键词： Generic programming Array programming Dynamic compilation multi-core computing Single Assignment C Persistence

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Contribution to Speeding-Up the Solving of Nonlinear Ordinary Differential Equations on Parallel/multi-core Platforms for Sensing Systems

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SENSORS 2020年第21期20卷 6130页

作者： Tavakkoli, Vahid Mohsenzadegan, Kabeh Chedjou, Jean Chamberlain Kyamakya, Kyandoghere Univ Klagenfurt Inst Smart Syst Technol A-9020 Klagenfurt Austria

Solving ordinary differential equations (ODE) on heterogenous or multi-core/parallel embedded systems does significantly increase the operational capacity of many sensing systems in view of processing tasks such as self-calibration, model-based measurement and self-diagnostics. The main challenge is usually related to the complexity of the processing task at hand which costs/requires too much processing power, which may not be available, to ensure a real-time processing. Therefore, a distributed solving involving multiple cores or nodes is a good/precious option. Also, speeding-up the processing does also result in significant energy consumption or sensor nodes involved. There exist several methods for solving differential equations on single processors. But most of them are not suitable for an implementation on parallel (i.e., multi-core) systems due to the increasing communication related network delays between computing nodes, which become a main and serious bottleneck to solve such problems in a parallel computing context. Most of the problems faced relate to the very nature of differential equations. Normally, one should first complete calculations of a previous step in order to use it in the next/following step. Hereby, it appears also that increasing performance (e.g., through increasing step sizes) may possibly result in decreasing the accuracy of calculations on parallel/multi-core systems like GPUs. In this paper, we do create a new adaptive algorithm based on the Adams-Moulton and Parareal method (we call it PAMCL) and we do compare this novel method with other most relevant implementations/schemes such as the so-called DOPRI5, PAM, etc. Our algorithm (PAMCL) is showing very good performance (i.e., speed-up) while compared to related competing algorithms, while thereby ensuring a reasonable accuracy. For a better usage of computing units/resources, the OpenCL platform is selected and ODE solver algorithms are optimized to work on both GPUs and CPUs. This

关键词： ODE Solver OpenCL Parareal parallel multi-core computing sensing systems heterogenous embedded systems

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