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multi-core computing: A novel accelerating method for chemometrics calculation

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2009年第1期96卷 94-97页

作者： Zhang, Zhi-Min Liang, Yi-Zeng Xu, Qing-Song Cent S Univ Res Ctr Modernizat Chinese Med Coll Chem & Chem Engn Changsha 410083 Hunan Peoples R China Cent S Univ Sch Math Sci & Comp Technol Changsha 410083 Peoples R China

The higher speed is the eternal pursuit of any chemometric algorithm. In order to take full advantage of the multi-core processor's computing resources (the prevailing current of personal computer) and accelerate the time-consuming algorithms in chemometrics, a novel multi-core computing method is introduced. Leave-one-out cross-validation is taken as an example to show the powerful capability of the multi-core computing. The comparison results show that the execution time drops rapidly with the increasing number of computing cores, which demonstrates that the multi-core computing is a promising tool for solving computing-intensive and data-intensive problems in chemometrics. (C) 2008 Published by Elsevier B.V.

关键词： multi-core computing Cross-validation Parallel algorithm Message Passing Interface

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

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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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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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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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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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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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Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using multi-core Architectures

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REMOTE SENSING 2014年第8期6卷 7212-7232页

作者： Kang, Xiaochen Liu, Jiping Lin, Xiangguo Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China

As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input/Output (I/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points.

关键词： airborne LiDAR multi-core computing stream computing progressive TIN densification filtering

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Automatic parallelization of XQuery programs on multi-core systems

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JOURNAL OF SUPERcomputing 2016年第4期72卷 1517-1548页

作者： Chen, Rongxin Liao, Husheng Wang, Zongyue Su, Hang Jimei Univ Comp Engn Coll Xiamen Peoples R China Beijing Univ Technol Sch Software Engn Beijing Peoples R China Beijing Univ Technol Coll Comp Sci Beijing Peoples R China

The popularity of multi-core systems makes software parallelization become an important way to improve performance. As a mainstream XML query language, XQuery is the core of XML processing. It is critical to take full advantage of multi-core computing to improve XML processing performance through parallelization of XQuery. However, usually it is difficult to parallelize XQuery programs because of the nested style of XQuery expressions. Moreover, implicit parallelism is necessary to simplify the development of parallel XML application. In this paper, we propose an automatic parallelization approach, which can automatically select proper types of parallelism for a specific XQuery query. Specifically, we propose a functional intermediate language called pFXQL (parallel Functional XML Query Language) to describe parallel query plans. pFXQL has parallel semantics and is well complied with XQuery. We propose a cost model to effectively support plan generation and the selection of a preferred plan. The model estimates both computational cost and parallel cost. We implement our approach in XQuery engine and conduct experiments on various multi-core systems. Experimental results verify the effectiveness of our approach.

关键词： XQuery Automatic parallelization Intermediate language Cost model multi-core computing

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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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A high-performance face detection system using OpenMP

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2009年第15期21卷 1819-1837页

作者： Hadjidoukas, P. E. Dimakopoulos, V. V. Delakis, M. Garcia, C. Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece Orange Labs F-35512 Rennes France

We present the development of a novel high-performance face detection system using a neural network-based classification algorithm and an efficient parallelization with OpenMP. We discuss the design of the system in detail along with experimental assessment. Our parallelization strategy starts with one level of threads and moves to the exploitation of nested parallel regions in order to further improve, by up to 19%, the image-processing capability. The presented system is able to process images in real time (38 images/sec) by sustaining almost linear speedups on a system with a quad-core processor and a particular OpenMP runtime library. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： face detection image processing nested parallelism OpenMP multi-core computing

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