2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)最新文献

{"title":"Applying distributed application global states monitoring to speculative query processing in RDBMS","authors":"A. Sasak-Okon, M. Tudruj","doi":"10.1109/ISPDC51135.2020.00022","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00022","url":null,"abstract":"The paper is concerned with the methodology for speculative query execution support in distributed Relational Database Management Systems (RDBMS). The proposed approach is based on the analysis of the multigraph representations of the stream of input queries arriving to a RDBMS. As a result, the optimized set of speculative queries is found to support execution of current queries. The speculative query results are used to speed-up execution of the query input stream. The paper presents how the proposed speculative query execution approach can be implemented inside a novel distributed program design framework PEGASUS DA in which program execution decisions are taken based on the system-supported monitoring of the distributed application global states. The paper shows the architecture of the speculative support provided by such framework for the distributed RDBMS and the assumed speculation approach. The implementation issues of the multithreaded distributed support based on the RDBMS SQLite engines are discussed. Distributed data synchronization and speculative query execution strategy as well as speculation results distribution are discussed. The proposed approach to distributed implementation of the speculative support to RDBMSs using the PEGASUS DA framework is illustrated on the example of the modifying query handling in a RDBMS facing the presented speculative query support for query execution.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116737906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ISPDC 2020 Committees","authors":"M. Tudruj","doi":"10.1109/ispdc51135.2020.00006","DOIUrl":"https://doi.org/10.1109/ispdc51135.2020.00006","url":null,"abstract":"","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Workflow Provisioning and Scheduling on Edge Cloud Computing Using a Gradient Descent Search Approach","authors":"Raed Alsurdeh, R. Calheiros, K. Matawie, B. Javadi","doi":"10.1109/ISPDC51135.2020.00019","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00019","url":null,"abstract":"The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131509168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ISPDC 2020 Committees","authors":"Ispdc, N. Abdennadher, Jorge G. Barbosa, Zina Ben-Miled, F. Ciorba, T. Hanawa, Chih-Wei Lin, A. Marowka","doi":"10.1109/ispdc51135.2020.00007","DOIUrl":"https://doi.org/10.1109/ispdc51135.2020.00007","url":null,"abstract":"Nabil Abdennadher, University of Applied Sciences, Western Switzerland, Switzerland Kubilay Atasu, IBM Research Zurich, Switzerland Ioana Banicescu, Mississippi State University, USA Jorge G. Barbosa, University of Porto, Portugal Zina Ben-Miled, IUPUI, USA Nik Bessis, Edge Hill University, United Kingdom Marian Bubak, AGH University of Science and Technology, Poland Hans-Joachim Bungartz, Technical University of Munich, Germany Sabin Buraga, A.I.Cuza University of Iasi, USA Alfonso Capozzoli, Politecnico di Torino, Italy Aniello Castiglione, University of Naples Parthenope, Italy Jianzhang Chen, Fujian Agriculture and Forestry University, USA Marta Chinnici, ENEA, USA Bastien Chopard, University of Geneva, Switzerland Anthony Chronopoulos, University of Texas San Antonio, USA Florina M. Ciorba, University of Basel, Switzerland Fabio Costa, Federal University of Goias, Brazil Stefania Costache, Vrije Universiteit Amsterdam, Netherlands Raphaël Couturier, University Bourgogne Franche-Comté, France Valentin Cristea, University Politehnica of Bucharest, Romania Paweł Czarnul, Gdańsk University of Technology, Poland Zbigniew J. Czech, Silesian University of Technology, Poland Ciprian Dobre, University Politehnica of Bucharest, Romania Maciej Drozdowski, Poznań University of Technology, Poland Juan J. Durillo, Leibniz Supercomputing Centre (LRZ), Germany Paweł Gepner, Warsaw University of Technology, Poland George Gravvanis, Democritus University of Thrace, Greece Dan Grigoras, UCC, Ireland Daniel Grosu, Wayne State University, USA Todor Gurov, IICT-BAS, Bulgaria Toshihiro Hanawa, University of Tokyo, Japan Ibrahim Hoteit, KAUST, Saudi Arabia Alexandru Iosup, Vrije Universiteit Amsterdam, Netherlands Ali Jannesari, University of California, Berkeley, USA Hai Jin, Huazhong University of Science and Technology, China Peter Kacsuk, MTA-SZTAKI, Hungary Helen Karatza, Aristotle University of Thessaloniki, Greece Gabor Kecskemeti, Liverpool John Moores University, United Kingdom Keiji Kimura, Waseda University, Japan Jacek Kitowski, AGH University of Science and Technology, Poland Henryk Krawczyk, Gdańsk University of Technology, Poland Peter Kropf, University Neuchâtel, Switzerland Eryk Laskowski, Institute of Computer Science Polish Academy of Sciences, Poland Alexey Lastovetsky, University College Dublin, Ireland Laurent Lefevre, École Normale Supérieure de Lyon, France Wei Li, The University of Sydney, Australia","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115998448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ISPDC 2020 Breaker Page","authors":"","doi":"10.1109/ispdc51135.2020.00003","DOIUrl":"https://doi.org/10.1109/ispdc51135.2020.00003","url":null,"abstract":"","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115031360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a Cloud Native Big Data Platform using MiCADO","authors":"A. Mosa, T. Kiss, G. Pierantoni, J. Deslauriers, D. Kagialis, G. Terstyánszky","doi":"10.1109/ISPDC51135.2020.00025","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00025","url":null,"abstract":"In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware $(e. g$., cluster nodes) and software $(e. g$., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms $(e. g$., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling).","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123285914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated implementation of FQSqueezer novel genomic compression method","authors":"Monica Amich, P. D. Luca, S. Fiscale","doi":"10.1109/ISPDC51135.2020.00030","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00030","url":null,"abstract":"Biological data contain very important information for genoma analysis. In last decades, the size of these data is constantly growing. So the Next Generation Sequence (NGS) data has been introduced. These kind of data are represented by different data formats, such as FASTQ, FASTA, SAM, etc. In order to allow a good analysis and storing of them, due to large dimension of these data, several compressors have been performed. FQSqueezer is a novel genomic compressor for FASTQ data files. But several issues are present due to multithread version that runs on multi-core hardware. It is wellknown that the number of cores in a CPU is limited and very minor with respect to GPUs’ cores number. In order to increase the performance related to this compressor method, in this work we present a GPU-parallel implementation of cited compressor by exploiting CUDA framework. More precisely, a suitable domain decomposition is able to give an appreciable gain of performance in terms of time and reliability. Several execution tests confirm the gain of efficiency achieved by our parallel implementation.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126426588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Parallel Shortest Path Algorithms","authors":"David R. Alves, M. Krishnakumar, V. Garg","doi":"10.1109/ISPDC51135.2020.00034","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00034","url":null,"abstract":"Finding the shortest path between nodes in a graph has wide applications in many important areas such as transportation and computer networks. However, the current reference algorithms for this task, Dijkstra’s for single threaded environments and $triangle$-stepping for multi-threaded ones, leave performance and efficiency on the table by not taking advantage of additional information available about the graph. In this paper we present and experimentally evaluate novel algorithms $SP_{1},SP_{2}$ and ParSP2 that leverage these constraints to solve the problem faster and more efficiently in key metrics. In single threaded execution, we show how SP1 and SP2 out-perform Dijsktra’s algorithm by up to 46%. In multi-threaded execution we show how our algorithms compare favorably to $triangle$-stepping algorithm in the ability to establish the shortest path between the source and the median node.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126444905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compiler Assisted Source Transformation of OpenMP Kernels","authors":"Jannek Squar, Tim Jammer, Michael Blesel, Michael Kuhn, T. Ludwig","doi":"10.1109/ISPDC51135.2020.00016","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00016","url":null,"abstract":"Many scientific applications use OpenMP as a relatively easy and fast approach to utilise symmetric multiprocessor systems at their full capacity. However, scalability on shared memory systems is limited and thus distributed parallel computing is inevitable if the full potential through horizontal scaling shall be achieved. Additional software layers like MPI must be used, which require further knowledge on the scientific developers’ side. This paper presents CATO, a tool prototype using LLVM and Clang, to transform existing OpenMP code to MPI; this enables distributed code execution while keeping OpenMP’s relatively low barrier of entry. The main focus lies on increasing the maximum problem size, which a scientific application can work on; converting an intra-node problem into an inter-node problem makes it possible to overcome the limitation of memory of a single node. Our tool does not focus on improving the absolute runtime, even though it might improve it by e.g. introducing concurrency during the I/O phase; but we rather focus on increasing the maximal problem size and our benchmark of a stencil code shows promising results: The transformation preserves the speedup trend of the code to some extent. Another example demonstrates the capability to increase the maximum problem size while using additional compute nodes.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122593914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Matrix Partitioning for Data Parallel Computing on Hybrid Heterogeneous Platforms","authors":"Tania Malik, Alexey L. Lastovetsky","doi":"10.1109/ISPDC51135.2020.00011","DOIUrl":"https://doi.org/10.1109/ISPDC51135.2020.00011","url":null,"abstract":"In this paper, we study the problem of partitioning a matrix over a small number of interconnected heterogeneous processors. This problem is crucial for data parallel dense linear algebra and other applications with similar communication patterns on modern hybrid servers, integrating several heterogeneous compute devices such as CPUs, GPUs and other accelerators. The objective is to balance the load of the heterogeneous devices while minimising the communication cost. While the problem has been solved for the case of two processors, it is still open for three and more processors. The state-of-the-art solution for the case of three processors uses a communication cost function, which does not accurately account for the total amount of data moved between processors and therefore leaves the question of its global optimality open. In this work, we propose a cost function, which accurately represents the total amount of data moved between processors. Then, we formulate and solve the problem of optimal partitioning of a square computational domain, using this accurate communication cost function. Finally, we propose and implement an original experimental methodology for accurate measurement of the communication time of parallel applications on hybrid heterogeneous servers, integrating multi-core CPUs and various accelerators. We apply this methodology to experimental validation of our mathematical result.","PeriodicalId":426824,"journal":{"name":"2020 19th International Symposium on Parallel and Distributed Computing (ISPDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130701877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}