International audienceWe introduce the papers submitted to the special issue of Computation, Concurrency: Practice and Experience on parallel and distributed algorithms

Aupy, Guillaume

Tang, Xueyan

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HAL Id: hal-01971630https://hal.inria.fr/hal-01971630Submitted on 7 Jan 2019HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Parallel and distributed algorithmsGuillaume Aupy, Xueyan TangTo cite this version:Guillaume Aupy, Xueyan Tang. Parallel and distributed algorithms. Concurrency and Computation:Practice and Experience, pp.3, 2018, ￿10.1002/cpe.4663￿. ￿hal-01971630￿Received 18 April, 2018; Revised ; Accepted 22 April, 201810.1002/cpe.4663EDITORIALParallel and Distributed AlgorithmsGuillaume Aupy1 | Xueyan Tang21Inria Bordeaux Sud – Ouest & Univ. ofBordeaux, France2School of Computer Science and Engineering,Nanyang Technological University, SingaporeCorrespondenceEmail: guillaume.aupy@inria.fr,Email: asxytang@ntu.edu.sgAbstractWe introduce the papers submitted to the special issue of Computation, Concurrency: Practice andExperience on parallel and distributed algorithms.KEYWORDS:Editorial1 INTRODUCTIONWelcome to this special issue! This issue compiles latest research results on parallel and distributed algorithms. Selected papers from the algorithmtrack of the 17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP-2017) and from the 12th Schedulingfor Large Scale Systems Workshop were invited for extension and submission to this issue. The general objectives of these two events is to targetboth theoretical and experimental algorithms for novel parallel architectures. We also received submissions from an open call.2 THEMES OF THIS SPECIAL ISSUEFive papers were finally accepted to this special issue. These papers cover a wide range of topics coming from parallel computing: run time-systems to deal with heterogeneous architecture, linear algebra applications, but also energy-efficient issues linked to infrastructure such as mobilecomputing or cloud computing.2.1 Runtime systems to deal with heterogeneous architectureRuntime systems have gained tremendous momentum with the increasing complexity of novel parallel architectures. From an application pointof view, leaving the hard scheduling part to a highly optimized system (such as StarPU, Parsec or Pycomps) is very interesting. In this issue wewelcome two very different paper, one offering solution to improve the performance of these runtime systems, and another where an applicationis using such implementations.Fast Approximation Algorithms for Task-Based Runtime Systems 1, conducts a theoretical study of the HeteroPrio scheduling policy for complexcomputations on heterogeneous platforms. Heterogeneity is common in high performance computing with specialized accelerators like GPUsproviding efficient computational power. It proves approximation bounds compared to the optimal schedule in both cases of independent tasks andgeneral task graphs. The paper also provides an experimental evaluation of HeteroPrio on real task graphs from dense linear algebra computation.Dataflow programming models are becoming popular as a means to deliver a good balance between performance and portability in the post-petascale era. Evaluation of Dataflow Programming Models for Electronic Structure Theory 2, evaluate two programming paradigms for expressingscientific applications in a dataflow form: "explicit dataflow" where the dataflow is specified explicitly by the developer, and "implicit dataflow"where a task scheduling runtime derives the dataflow using per-task data-access information embedded in a serial program. The paper comparesthese two models in terms of programmability, resource utilization, and scalability.2 Aupy and Tang2.2 Matrix factorization with MapReduceThe third paper, Distributed Geometric Nonnegative Matrix Factorization and Hierarchical Alternating Least Squares-based Nonnegative TensorFactorization with the MapReduce Paradigm 3, proposes various parallel and distributed computation strategies for the latent factors in bothfactorization models based on partitioning the computational tasks according to the MapReduce paradigm. These factorization models are widelyused in machine learning and data analysis for feature extraction and dimensionality reduction. Experiments using various large-scale data setsdemonstrated that the proposed algorithms are efficient and robust to noisy data.2.3 Energy-efficiencyIn energy-efficient computing, shutdown techniques have been developed to adapt the number of switched-on servers to the actual workload.The fourth paper, Quantifying the Impact of Shutdown Techniques for Energy-Efficient Data Centers 4, evaluates the potential gain of shutdowntechniques on recent server architectures and future energy-aware architectures by taking into account shutdown and boot up costs in time andenergy. The evaluation exploits real traces collected on production infrastructures with several shutdown policies, with and without workloadprediction.The low performance and reduced power-capacity ofmobile devices severely limit the complexity and duration ofmobile gaming. The fifth paper,A Mirroring Architecture for Sophisticated Mobile Games using Computation-Offloading 5, explores moving the compute-intensive parts of mobilegames to execute on remote infrastructure. It proposes a system for computation-offloading that supports the demanding performance require-ments of sophisticated mobile games. A real-world prototype is implemented and applied to the popular game OpenTTD, which demonstrates thatthe system can significantly improve performance and power consumption, while also delivering smooth gameplay.ACKNOWLEDGEMENTSWe would like to express sincere appreciation to all the authors and the reviewers for their contributions to this special issue. We are also verygrateful to the Editor-in-Chief, Professor Geoffrey C. Fox, for his kind support and guidance. We hope that you will enjoy reading this special issue.References1. Olivier Beaumont, Lionel Eyraud-Dubois, and Suraj Kumar, Fast Approximation Algorithms for Task-Based Runtime Systems. Concurrency andComputation: Practice and Experience [this issue].2. Heike Jagode, Anthony Danalis, Reazul Hoque, Mathieu Faverge, and Jack Dongarra, Evaluation of Dataflow Programming Models for ElectronicStructure Theory. Concurrency and Computation: Practice and Experience [this issue].3. Rafal Zdunek and Krzysztof Fonal, Distributed Geometric Nonnegative Matrix Factorization and Hierarchical Alternating Least Squares-basedNonnegative Tensor Factorization with the MapReduce Paradigm. Concurrency and Computation: Practice and Experience [this issue].4. Issam Rais, Anne-Cécile Orgerie, Martin Quinson, and Laurent Lefèvre. Quantifying the Impact of Shutdown Techniques for Energy-Efficient DataCenters. Concurrency and Computation: Practice and Experience [this issue].5. Minghai Jiang, Otto Visser, Wishnu Prasetya, and Alexandru Iosup, A Mirroring Architecture for Sophisticated Mobile Games using Computation-Offloading. Concurrency and Computation: Practice and Experience [this issue].AUTHOR BIOGRAPHIESGuillaume Aupy received his PhD from ENS Lyon in 2014. He is currently a researcher at Inria Bordeaux Sud-Ouest. Hisresearch interests include data-aware scheduling at the different levels of thememory hierarchy, but also energy-efficient andreliable scheduling strategies in High-Performance Computing. He was the Technical Program vice-chair of the ACM/IEEESC’17 conference.Aupy and Tang 3Xueyan Tang received the B.Eng. degree in computer science and engineering from Shanghai Jiao Tong University in 1998,and the Ph.D. degree in computer science from The Hong Kong University of Science and Technology in 2003. He is currentlyan Associate Professor with the School of Computer Science and Engineering, Nanyang Technological University, Singapore.His research interests include distributed systems, cloud computing, mobile and pervasive computing, and wireless sensornetworks. He has served as an Associate Editor of the IEEE Transactions on Parallel and Distributed Systems, and a ProgramCo-Chair of the IEEE ICPADS 2012 and the IEEE CloudCom 2014 conferences.How to cite this article: G. Aupy, and X. Tang (2018), Foreword to the Special Issue on Parallel and Distributed Algorithms, CCPE, 30.

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