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Automated Mapping of the MapReduce Pattern onto Parallel Computing Platforms

Qiang Liu, Tim Todman, Wayne Luk, George A. Constantinides
2010 Journal of Signal Processing Systems  
Gate Array (FPGA) based parallel computing platforms.  ...  Unlike previous work, in which designers explicitly specify how to exploit the pattern, we develop a compilation approach for mapping applications with the MapReduce pattern automatically onto Field-Programmable  ...  Acknowledgements We thank FP6 hArtes project, the EPSRC, AlphaData and Xilinx for their support. We also thank the anonymous reviewers for their comments.  ... 
doi:10.1007/s11265-010-0563-9 fatcat:qq3skkqcyjeiloryxhctjhrrem

Automatic optimisation of MapReduce designs by geometric programming

Qiang Liu, Tim Todman, Wayne Luk, George A. Constantinides
2009 2009 International Conference on Field-Programmable Technology  
Many important applications can be expressed using the MapReduce pattern, where a computation is decomposed into a Map phase on which each element of source data is independently operated, followed by  ...  We develop an approach for compiling applications with the MapReduce pattern into parallel hardware.  ...  We thank FP6 hArtes project, the EPSRC, Al-phaData and Xilinx for their support.  ... 
doi:10.1109/fpt.2009.5377629 fatcat:2z5vmltn7rgn3apeanwd3kf37e

Guest Editorial: Field-Programmable Technology

Lesley Shannon, Oliver Diessel, Neil W. Bergmann
2011 Journal of Signal Processing Systems  
In "Automated Mapping of the MapReduce Pattern onto Parallel Computing Platforms," Qiang Lie, Tim Todman, Wayne Luk and George Constantinides explore using FPGAs effectively for large computational problems  ...  Having identified that many applications use a "MapReduce" computational pattern, they develop a  ...  In "Automated Mapping of the MapReduce Pattern onto Parallel Computing Platforms," Qiang Lie, Tim Todman, Wayne Luk and George Constantinides explore using FPGAs effectively for large computational problems  ... 
doi:10.1007/s11265-011-0653-3 fatcat:7vextmaqsfcuhjiwtais3nk35e

Optimizing Hardware Design by Composing Utility-Directed Transformations

Qiang Liu, Tim Todman, Wayne Luk, George A. Constantinides
2012 IEEE transactions on computers  
We present a systematic approach composing multiple utility-directed transformations for optimizing and mapping a sequential design onto a customizable parallel computing platform such as a Field-Programmable  ...  The utility-directed transformations in this work produce performance-optimized designs by exploiting data reuse, MapReduce, and pipelining for the target parallel computing platform.  ...  We thus map the innermost loop of matrix multiplication onto a parallel computing structure, by loop strip mining.  ... 
doi:10.1109/tc.2011.205 fatcat:o5ekmgzqundk5jzpnhrfrhfnj4

Adapting MPI to MapReduce PaaS Clouds: An Experiment in Cross-Paradigm Execution

Jaroslaw Slawinski, Vaidy Sunderam
2012 2012 IEEE Fifth International Conference on Utility and Cloud Computing  
on MapReduce platforms.  ...  ABSTRACT One desired attribute of utility computing is the ability for any provider's offering to meet any user's requirement, but the variety of programming paradigms and platform models make this non-trivial  ...  Another intriguing research issue concerns the feasibility and scope for mapping the tightly coupled computation model of MPI onto the unrestricted BSP model.  ... 
doi:10.1109/ucc.2012.52 dblp:conf/ucc/SlawinskiS12 fatcat:mm2nyib7mbhxnlewguehnj3xo4

Combining optimizations in automated low power design

Qiang Liu, Tim Todman, Wayne Luk
2010 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010)  
Starting from sequential programs, we present an approach combining data reuse, multi-level MapReduce, and pipelining to automatically find the most power-efficient designs that meet speed and area constraints  ...  missed by separating the optimizations.  ...  Next, the analysis toolbox maps the regular loop iteration space in the code onto a polytope space and determines data dependencies and memory access patterns in the loops, for design space exploration  ... 
doi:10.1109/date.2010.5457104 dblp:conf/date/LiuTL10 fatcat:wsriwopv2fcejlz3lh4zz7qrde

An Abstract Annotation Model for Skeletons [chapter]

Marco Aldinucci, Sonia Campa, Peter Kilpatrick, Fabio Tordini, Massimo Torquati
2013 Lecture Notes in Computer Science  
In this work a first step toward the automated optimization of high level skeleton-based parallel code is discussed.  ...  The paper presents an abstract annotation model for skeleton programs aimed at formally describing suitable mapping of parallel activities on a high-level platform representation.  ...  The work described in this paper is supported by the EU ParaPhrase project (http://www.paraphrase-ict.eu, 2011-2014).  ... 
doi:10.1007/978-3-642-35887-6_14 fatcat:j674vzcgibhz5p2ytwfxbrtehu

Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce

Guillem Pratx, Lei Xing
2011 Journal of Biomedical Optics  
The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment.  ...  In this implementation, Map tasks compute photon histories in parallel while a Reduce task scores photon absorption. The distributed implementation was evaluated on a commercial compute cloud.  ...  The authors would like to acknowledge the Oregon Medical Laser Center for making the MC321 package available online.  ... 
doi:10.1117/1.3656964 pmid:22191916 pmcid:PMC3273307 fatcat:ftanxuevnnfzbeo342gf57mmcu

Riding the elephant

Elif Dede, Madhusudhan Govindaraju, Daniel Gunter, Lavanya Ramakrishnan
2011 Proceedings of the 2011 ACM international workshop on Many task computing on grids and supercomputers - MTAGS '11  
In this paper, we evaluate the programmability of MapReduce and Hadoop for scientific workflow ensembles.  ...  in fluid and solid dynamics -all run large numbers of parallel analyses, which we call scientific ensembles.  ...  Hadoop Hadoop is an open-source distributed computing platform that implements the MapReduce model.  ... 
doi:10.1145/2132876.2132888 dblp:conf/sc/DedeGGR11 fatcat:u2xt63v24rgo3ks2jz7cksjr2i

Hadoop Image Processing Framework

Sridhar Vemula, Christopher Crick
2015 2015 IEEE International Congress on Big Data  
The emergence of processing frameworks such as the Hadoop MapReduce[1] platform addresses the problem of providing a system for computationally intensive data processing and distributed storage.  ...  The main aim of the framework is to allow developers of image processing applications to leverage the Hadoop MapReduce framework without having to master its technical details and introduce an additional  ...  The ability to parallelize tasks allows for scalable, efficient execution of resource-intensive applications. The Hadoop MapReduce framework provides a platform for such tasks.  ... 
doi:10.1109/bigdatacongress.2015.80 dblp:conf/bigdata/VemulaC15 fatcat:hwww2m2vynamxpvznbuorn6gja

The application of Hadoop in Structural Bioinformatics [article]

Jamie Alnasir, Hugh Shanahan
2018 bioRxiv   pre-print
This paper reviews the use of the Hadoop platform in Structural Bioinformatics applications.  ...  We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms.  ...  Acknowledgements The research in this article was made possible from support from the Department of Computer Science, Royal Holloway, University of London.  ... 
doi:10.1101/376467 fatcat:3qoeg4h77rfarn5a2gneg3xmhe

Designing Parallel Data Processing for Large-Scale Sensor Orchestration

Milan Kabac, Charles Consel
2016 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)  
We have developed a prototype 1 of our approach, using Apache Hadoop. We applied it to a case study and obtained significant speedups by parallelizing computations over twelve nodes.  ...  Specifically, an application design exposes declarations that are used to generate a programming framework based on the MapReduce programming model.  ...  Gupta et al. propose sMapReduce [31] , a programming pattern inspired by the MapReduce programming model for mapping application behavior onto a sensor network and enabling complex data aggregation. sMapReduce  ... 
doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0031 dblp:conf/uic/KabacC16 fatcat:qvz2oybacbfr3icqpogvh5pxw4

Melia: A MapReduce Framework on OpenCL-Based FPGAs

Zeke Wang, Shuhao Zhang, Bingsheng He, Wei Zhang
2016 IEEE Transactions on Parallel and Distributed Systems  
We further develop a series of FPGA-centric optimization techniques to improve the efficiency of Melia, and a cost-and resource-based approach to automate the parameter settings for those optimizations  ...  However, the design and implementation of MapReduce on FPGAs can be challenging: firstly, FPGAs are usually programmed with hardware description languages, which hurts the programmability of the MapReduce  ...  On the other hand, Melia improves the programmability so that the user only needs to implement two primitives (map and reduce), and MapReduce is able to exploit the parallelism in the underlying computing  ... 
doi:10.1109/tpds.2016.2537805 fatcat:wguvs7g7gfe4fdv2ft2a7sde2y

The application of Hadoop in structural bioinformatics

Jamie J Alnasir, Hugh P Shanahan
2018 Briefings in Bioinformatics  
The paper reviews the use of the Hadoop platform in structural bioinformatics applications.  ...  We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms.  ...  Acknowledgements The research in this article was made possible from support from the Department of Computer Science, Royal Holloway, University of London.  ... 
doi:10.1093/bib/bby106 pmid:30462158 fatcat:jbjd3n6eungbfo4etdtlns4wji

An optimized MapReduce workflow scheduling algorithm for heterogeneous computing

Zhuo Tang, Min Liu, Almoalmi Ammar, Kenli Li, Keqin Li
2014 Journal of Supercomputing  
The MapReduce framework is considered to be an effective resolution for huge and parallel data processing.  ...  The experimental results show that the optimized MapReduce workflow scheduling algorithm can improve the performance of task scheduling and the rationality of resources allocation in heterogeneous computing  ...  Acknowledgment The authors are grateful to the three anonymous reviewers for their criticism and comments which have helped to improve the presentation and quality of the paper.  ... 
doi:10.1007/s11227-014-1335-2 fatcat:cyayhon2fzgkbdvpb4edrigoae
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