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international symposium on parallel and distributed Processing (IPDPS)

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EQueue: Elastic Lock-Free FIFO Queue for Core-to-Core Communication on Multi-Core Processors

IEEE ACCESS 2020年 8卷 98729-98741页

作者： Wang, Junchang Tian, Yangfeng Fu, Xiong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210003 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

In recent years, the number of CPU cores in a multi-core processor keeps increasing. To leverage the increasing hardware resource, programmers need to develop parallelized software programs. One promising approach to parallelizing high-performance applications is pipeline parallelism, which divides a task into a serial of subtasks and then maps these subtasks to a group of CPU cores, making the communication scheme between the subtasks running on different cores a critical component for the parallelized programs. One widely-used implementation of the communication scheme is software-based, lock-free first-in-first-out queues that move data between different subtasks. The primary design goal of the prior lock-free queues was higher throughput, such that the technique of batching data was heavily used in their enqueue and dequeue operations. Unfortunately, a lock-free queue with batching heavily depends on the assumption that data arrive at a constant rate, and the queue is in an equilibrium state. Experimentally we found that the equilibrium state of a queue rarely happens in real, high-performance use cases (e.g., 10Gbps & x002B;network applications) because data arriving rate fluctuates sharply. As a result, existing queues suffer from performance degradation when used in real applications on multi-core processors. In this paper, we present EQueue, a lock-free queue to handle this robustness issue in existing queues. EQueue is lock-free, efficient, and robust. EQueue can adaptively (1) shrink its queue size when data arriving rate is low to keep its memory footprint small to utilize CPU cache better, and (2) enlarge its queue size to avoid overflow when data arriving rate is in burstiness. Experimental results show that when used in high-performance applications, EQueue can always perform an enqueue/dequeue operation in less than 50 CPU cycles, which outperforms FastForward and MCRingBuffer, two state-of-the-art queues, by factors 3 and 2, respectively.

关键词： parallel processing Task analysis Multicore processing Pipelines Robustness Throughput Licenses Lock-free queue pipeline parallelism multi-core processors

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DPDNS Introduction and Committees

DPDNS Introduction and Committees

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Dimiter Avresky Erik Maehle Salvatore Distefano International Research Institute on Autonomic Network Computing IRIANC Boston USA/Munich Germany University of Luebeck Germany Politecnico di Milano Italy

Provides a listing of current committee members and society officers.

关键词： Educational institutions Conferences Committees distributed processing Cloud computing Information technology IEEE Computer Society

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A distributed power-efficient data gathering and aggregation protocol for wireless sensor networks

A distributed power-efficient data gathering and aggregation...

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3rd international symposium on parallel and distributed Processing and Applications, ISPA 2005

作者： Liu, Ming Cao, Jiannong Gong, Hai-Gang Chen, Li-Jun Li, Xie Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

ISBN: (纸本)3540297693

In this paper, we propose a distributed power-efficient data gathering and aggregation algorithm (DPEG), in which a node, according to its residual energy and the strength of signal received from its neighboring nodes, independently makes its decision to compete for becoming a cluster head. In addition, assume that the inter-cluster communication data is, in a multi-hop manner, sent to the designated node, which then sends the data gathered by the whole network to the base station. DPEG also proposes a simple approach to solve the cluster coverage problem. With the increase in node density, this approach lets sensor network lifetime be linear in the number of nodes. Our experimental results have proved that DPEG algorithm, in the best case, lets sensor network lifetime be respectively increased by 1800% and 300% as compared with another two data gathering and aggregation protocols - LEACH and PEGASIS. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Mobile computing

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Development of a parallel computing enabled optimisation tool for hydrological model calibration 21

Development of a parallel computing enabled optimisation too...

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21st international Congress on Modelling and Simulation (MODSIM) held jointly with the 23rd National Conference of the Australian-Society-for-Operations-Research / DSTO led Defence Operations Research symposium (DORS

作者： Yang, Ang Hughes, Justin Dutta, Dushmanta Kim, Shaun Vaze, Jai CSIRO Land & Water Canberra ACT Australia

ISBN: (纸本)9780987214355

A river model is a semi-distributed hydrological model and it includes many processes such as flow routing, irrigation diversion, overbank flow, ground water interaction for simulating flows a river system for water resources planning and management. A number of calibration parameters are introduced in such models to represent various processes using simplified mathematical equations. Traditionally, a river model is calibrated using a reach-by-reach calibration approach starting from the top of the system cascading down to the end of the system. While the reach-by-reach approach is suitable for obtaining optimum model performance at a single river reach with high quality observed data, it does have the limitation of error propagation from upstream to downstream reaches if poor quality data are used in the calibration. A system-wide calibration approach has recently been developed for river system modelling in large river basins. Comparing with traditional reach-by-reach calibration, this new method optimises parameters of all river reaches within a region simultaneously using a weighted global objective function. The results of its application of this new approach in the Murray-Darling basin, Australia have shown its potential to overcome over-fitting and improve fitness of each individual gauge. However, due to the system-wide optimization of multiple reach parameters in a region, the search space and computational time required for system calibration increase exponentially with the increase of number of parameters. This limits the number of parameters that can be optimised and thus, the size of the region. To potentially overcome this limitation, a parallel computing enabled shuffled complex evolution (SCE) optimisation tool has been developed. A series of comparison studies have been conducted to evaluate the performance of this approach over normal SCE. These are: 1) comparison of computation time and performance for the same number of parameters;2) comparison o

关键词： River system model Australian Water Resources Assessment Shuffled complex evolution parallel computing optimisation

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parallel matching and sorting with TACO's distributed collections-a case study from molecular biology research

Parallel matching and sorting with TACO's distributed collec...

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international symposium on High Performance distributed computing

作者： J. Nolte P. Horton Real World Computing Partnership Tsukuba Ibaraki Japan

TACO is a template library that implements higher-order parallel operations on distributed object sets by means of reusable topology classes and C++ function templates. We discuss an experimental application that exploits TACO's distributed object groups and collective operations for computing the similarity between groups of molecular sequences, a computationally intensive core problem in molecular biology research. In particular we show how TACO's distributed collections can be conveniently combined with well known concepts found in the C++ standard template library (STL) to solve matching and sorting problems effectively on distributed hardware platforms. The resulting implementation is concise and gives excellent parallel performance on PC- and workstation clusters.

关键词： Sorting Computer aided software engineering Sequences Biology computing Libraries distributed computing Proteins Concurrent computing Containers DNA

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Literature Review of Trust Models for Cloud computing

Literature Review of Trust Models for Cloud Computing

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international symposium on parallel and distributed computing

作者： Zafeiroula Georgiopoulou Costas Lambrinoudakis Digital Systems University of Piraeus Athens Greece

ISBN: (纸本)9781509041534

Proper trust management in cloud computing environments can significantly assist their widespread adoption. Trust can act as a countermeasure to the several security threats that the cloud faces. This paper provides an overview of how trust has been applied in cloud computing. Trust models suggested in contemporary literature for cloud computing systems are presented. Furthermore, a critical comparison, based on the set of the main characteristics of an appropriate trust management method, is included.

关键词： Cloud computing Computational modeling Authentication Measurement Computer architecture Organizations

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Evaluating Cluster Configurations for Big Data Processing: An Exploratory Study 9

Evaluating Cluster Configurations for Big Data Processing: A...

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IEEE 9th international symposium on the Maintenance and Evolution of Service-Oriented Systems and Cloud-Based Environments (MESOCA)

作者： Sandel, Roni Shtern, Mark Fokaefs, Marios Litoiu, Marin York Univ Elect Engn & Comp Sci N York ON M3J 1P3 Canada IBM Canada Markham ON Canada

ISBN: (纸本)9781467379359

As data continues to grow rapidly, NoSQL clusters have been increasingly adopted to address the storage and processing demands of these large amounts of data. In parallel, cloud computing is also increasingly being adopted due to its flexibility, cost efficiency and scalability. However, evaluating and modelling NoSQL clusters present many challenges. In this work, we explore these challenges by performing a series of experiments with various configurations. The intuition is that this process is laborious and expensive and the goal of our experiments is to confirm this intuition and to identify the factors that impact the performance of a Big Data cluster. Our experiments mostly focus on three factors: data compression, data schema and cluster topology. We performed a number of experiments based on these factors and measured and compared the response times of the resulting configurations. Eventually, the outcomes of our study are encapsulated in a performance model that predicts the cluster's response time as a function of the incoming workload and evaluates the cluster's performance less costly and faster. This systematic and effortless evaluation method will facilitate the selection and migration to a better cluster as the performance and budget goals change. We use HBase as the large data processing cluster and we conduct our experiments on traffic data from a large city and on a distributed community cloud infrastructure.

关键词： cloud computing big data design performance modelling and evaluation migration

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Message from the EduPar-20 Workshop Chairs

Message from the EduPar-20 Workshop Chairs

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sushil K Prasad Tia Newhall David Bunde Martina Barnas Satish Puri The University of Texas at San Antonio (Workshop Chair) Swarthmore College (Technical Program Chair) Knox College (Technical Program Vice Chair and Peachy Assignments Chair) Indiana University (Technical Program Panel and Posters Chair) Marquette University (Proceedings Chair)

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Welcome to the NSF/TCPP Workshop on parallel and distributed computing Education (EduPar-20) proceedings. The EduPar-20 workshop, held in conjunction with the IEEE international parallel and computing symposium (IPDPS), is devoted to the development and assessment of educational and curricular innovations and resources for undergraduate and graduate education in parallel and distributed computing (PDC). EduPar brings together individuals from academia, industry, and other educational and research institutes to explore new ideas, challenges, and experiences related to PDC pedagogy and curricula. The workshop is designed in coordination with the IEEE TCPP curriculum initiative on parallel and distributed computing (http://***/~tcpp/curriculum) for computer science and computer engineering undergraduates, and is supported by the NSF and the NSF-supported Center for parallel and distributed computing Curriculum Development and Educational Resources (CDER).

关键词： distributed processing Conferences

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A view on relational data on the Grid

A view on relational data on the Grid

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international symposium on parallel and distributed Processing (IPDPS)

作者： J. Magowan IBM United Kingdom Laboratories Limited Winchester UK

Grid computing "the next generation of the internet" has become an important topic in distributed computing. This paper explores how relational data can be exploited using Grid computing. This paper provides a better understanding of Grid computing and reviews the potential effects of relational data within the Grid will have on future distributed computing. The experiences are taken from two ongoing projects: open grid services architecture - database access and integration (OGSA-DAI) and the European DataGrid's relational grid monitoring architecture (R-GMA). Both projects have early working implementations which can be exploited for commercial or scientific use.

关键词： Grid computing Web services Computer architecture Service oriented architecture distributed computing Resource management Simple object access protocol Winches Internet Relational databases

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