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Evaluating MapReduce for Multi-core and Multiprocessor Systems

Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis
2007 2007 IEEE 13th International Symposium on High Performance Computer Architecture  
This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems.  ...  We study Phoenix with multi-core and symmetric multiprocessor systems and evaluate its performance potential and error recovery features.  ...  We showed that Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors.  ... 
doi:10.1109/hpca.2007.346181 dblp:conf/hpca/RangerRPBK07 fatcat:nuhuc3qrp5h7pnr6hazqybjmme

Guest Editorial: The Parallel Storage, Processing and Analysis for Big Data

Maozhen Li, Zhuo Tang
2016 International journal of parallel programming  
The Map and Reduce processes are implemented on a single computer with multiple CPU cores.  ...  Liu et al. [5] further evaluate the performance of MapReduce in computation in comparison with Spark [6], an in-memory computing technology which can be deployed on MapReduce.  ...  Renuka Nidhi, an Assistant of the Journals Editorial Office of Springer, for her great support in publication of the special issue.  ... 
doi:10.1007/s10766-016-0475-9 fatcat:cmcgdkn6xvaezdunnac63zp4q4

A MapReduce architecture for embedded multiprocessor system-on-chips

Hao Xiao, Huajuan Zhang, Fen Ge, Ning Wu
2016 IEICE Electronics Express  
Therefore, to enable embedded processors with more data processing capabilities, this paper presents a MapReduce-based multiprocessor system-onchip (MPSoC) for providing efficient architectural supports  ...  We implement the proposed MPSoC in cycle-accurate SystemC and evaluate its performance using a set of representative MapReduce applications.  ...  Fundamental Research Funds for the Central Universities NS2015043.  ... 
doi:10.1587/elex.13.20151025 fatcat:o3xwrtaazzexhly75w3euyoyqy

Accelerating MapReduce on a coupled CPU-GPU architecture

Linchuan Chen, Xin Huo, Gagan Agrawal
2012 2012 International Conference for High Performance Computing, Networking, Storage and Analysis  
For 4 of the applications, our system achieves 1.21 to 2.1 speedup over the better of the CPUonly and GPU-only versions. The speedups over a single CPU core execution range from 3.25 to 28.68.  ...  We have evaluated the different design decisions using 5 popular MapReduce applications.  ...  [18] implemented a shared-memory MapReduce library Phoenix for multi-core systems, and Yoo et al. [25] optimized Phoenix specifically for large-scale multicore systems.  ... 
doi:10.1109/sc.2012.16 dblp:conf/sc/ChenHA12 fatcat:t4d3smjlqncxzimjnlcsuwfn4y

A Reconfigurable MapReduce accelerator for multi-core all-programmable SoCs

Christoforos Kachris, Georgios Ch. Sirakoulis, Dimitrios Soudris
2014 2014 International Symposium on System-on-Chip (SoC)  
Phoenix MapReduce is a programming framework for multi-core systems that is used to automatically parallelize and schedule the programs based on the MapReduce framework.  ...  This paper presents a novel reconfigurable MapReduce accelerator that can be augmented to multi-core SoCs and it can speedup the indexing and the processing of the MapReduce key-value pairs.  ...  : General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State.  ... 
doi:10.1109/issoc.2014.6972430 dblp:conf/issoc/KachrisSS14 fatcat:ujzdefgxyfglzleuyrnxn2cnsa

Big Data Analytics for Images in Public Cloud using Map Reduce on Local Clusters

2019 International journal of recent technology and engineering  
MapReduce is a programming model used for parallel computing of big data in public cloud. Big Data have characteristics like variety, velocity and volume.  ...  The research considers unstructured image data in public cloud Dropbox as big data and applies MapReduce algorithm to map and reduce all the images stored in it.  ...  Multi cores are preferred over multi processes because communication cost is low in multi core compared to multiprocessor. Internal communication cost is low in multicore compared to external cost.  ... 
doi:10.35940/ijrte.d5303.118419 fatcat:iwt66wjko5dddd6hku6uoftk6a

Evaluating SPLASH-2 Applications Using MapReduce [chapter]

Shengkai Zhu, Zhiwei Xiao, Haibo Chen, Rong Chen, Weihua Zhang, Binyu Zang
2009 Lecture Notes in Computer Science  
By completely evaluating them in Hadoop, an open-source MapReduce framework for clusters, we analyze the major performance bottleneck of them in the MapReduce framework.  ...  MapReduce has been prevalent for running data-parallel applications.  ...  The prevalent of heterogeneous multi-core systems open opportunities to run MapReduce originally for clusters in a signal machine. Ranger et al.  ... 
doi:10.1007/978-3-642-03644-6_35 fatcat:ghwmz4ykb5dx3lptu2l3cqhfli

MapReduce for Data Intensive Scientific Analyses

Jaliya Ekanayake, Shrideep Pallickara, Geoffrey Fox
2008 2008 IEEE Fourth International Conference on eScience  
Although there are many evaluations of the MapReduce technique using large textual data collections, there have been only a few evaluations for scientific data analyses.  ...  Second, we present CGL-MapReduce, a streaming-based MapReduce implementation and compare its performance with Hadoop. MapReduce, Message passing, Parallel processing, Scientific Data Analysis I.  ...  Systems Division project number IIS-0536947.  ... 
doi:10.1109/escience.2008.59 dblp:conf/eScience/EkanayakePF08 fatcat:pgrhvxhmrzh7npwhdjvagq27g4

Pattern matching of signature-based IDS using Myers algorithm under MapReduce framework

Monther Aldwairi, Ansam M. Abu-Dalo, Moath Jarrah
2017 EURASIP Journal on Information Security  
The results show 1.3 and 1.7 times improvement for Phoenix++ and MAPCG MapReduce implementations over MPI respectively.  ...  In this paper, we parallelize a bit-vector algorithm, Myers algorithm, on a multi-core CPU under the MapReduce framework.  ...  Acknowledgements We would like to acknowledge the efforts of Yaser Jararweh and the valuable feedback of the reviewers. Availability of data and material Not applicable.  ... 
doi:10.1186/s13635-017-0062-7 fatcat:nmpetehk2jgyvdmkwcshhy4b2u

Optimizing MapReduce for GPUs with effective shared memory usage

Linchuan Chen, Gagan Agrawal
2012 Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing - HPDC '12  
We have evaluated our framework with seven commonly used MapReduce applications and compared it with the sequential implementations, MapCG, a recent MapReduce implementation on GPUs, and Ji et al.'  ...  For many classes of applications, MapReduce has emerged as the framework for easing parallel programming and improving programmer productivity.  ...  [19] have implemented a shared-memory MapReduce library named Phoenix in multi-core systems, and Yoo et al. [22] optimized Phoenix specifically for large-scale multi-core systems.  ... 
doi:10.1145/2287076.2287109 dblp:conf/hpdc/ChenA12 fatcat:oi75yvs57zhqpjx5iaio4pa4d4

Multicore-Enabled Smart Storage for Clusters

Zhiyang Ding, Xunfei Jiang, Shu Yin, Xiao Qin, Kai-Hsiung Chang, Xiaojun Ruan, Mohammed I. Alghamdi, Meikang Qiu
2012 2012 IEEE International Conference on Cluster Computing  
We present a multicore-enabled smart storage for clusters in general and MapReduce clusters in particular.  ...  We have implemented a programming framework for data-intensive applications running on a computing system coupled with McSD.  ...  Phoenix is an implementation of Google's MapReduce for shared-memory multi-core and multi-processor systems.  ... 
doi:10.1109/cluster.2012.70 dblp:conf/cluster/DingJYQCRAQ12 fatcat:g65jpyhoxzc7pilzdrqfock5qa

Providing Source Code Level Portability Between CPU and GPU with MapCG

Chun-Tao Hong, De-Hao Chen, Yu-Bei Chen, Wen-Guang Chen, Wei-Min Zheng, Hai-Bo Lin
2012 Journal of Computer Science and Technology  
A prototype of the MapCG runtime, supporting multi-core CPUs and NVIDIA GPUs, was implemented.  ...  We describe the design of MapCG, including the MapReduce-style high-level programming framework and the runtime system on the CPU and GPU.  ...  As multi-core CPUs have become mainstream, researchers also seek to use MapReduce for programming models on multi-core CPU platforms.  ... 
doi:10.1007/s11390-012-1205-4 fatcat:37y5ilv4kva5jcodoyzsusdnre

Mars

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang
2008 Proceedings of the 17th international conference on Parallel architectures and compilation techniques - PACT '08  
MapReduce framework on multi-core processors.  ...  As the first attempt to harness GPU's power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains hundreds of processors, and evaluated it in comparison with Phoenix, the state-ofthe-art  ...  To evaluate the efficiency of our framework, we compared our framework with Phoenix [23] , the stateof-the-art MapReduce framework on the multi-core processor.  ... 
doi:10.1145/1454115.1454152 dblp:conf/IEEEpact/HeFLGW08 fatcat:pjbsa7lhgnh2jk7vexppxwv2iy

Adaptive Task Partitioning for Performance Evaluation in Cluster based Heterogeneous Environments

Gosula Anitha
2021 Revista GEINTEC  
The suggested framework Adaptive Control Self-tuning provides a significant improvement over existing methods at moderate to high system utilizations, according to the evaluation results. designs homogeneous  ...  Meanwhile, datacenters are frequently used by a variety of users for a variety of purposes. Due to multi-tenant interferences, it frequently exhibits high performance heterogeneity.  ...  The disparity in MapReduce node handling capabilities can interrupt the assumption that MapReduce A heterogeneous CPU topology system is a system that uses the same ISA, but the core itself differs in  ... 
doi:10.47059/revistageintec.v11i4.2351 fatcat:dd2xruaz4bg27mpt6gy33htbce

Multi-dimensional characterization of temporal data mining on graphics processors

Jeremy Archuleta, Yong Cao, Tom Scogland, Wu-chun Feng
2009 2009 IEEE International Symposium on Parallel & Distributed Processing  
Using neuro science as the application vehicle, the re sults of our multi dimensional peiformance evaluation show that a "one-size fits-all" approach maps poorly across diff erent CPGPU cards.  ...  For example, the exponential growth in data volume now presents an obstacle for high-throughput data mining in fields such as neuroscience and bioinformatics.  ...  Acknowledgments We would like to thank Naren Ramakrishnan, Debprakash Patnaik, and Patrick Butler for their invalu able discussions concerning the theory behind temporal data mining and Sean Ponce for  ... 
doi:10.1109/ipdps.2009.5161049 dblp:conf/ipps/ArchuletaCSF09 fatcat:7fwcl3hq3vefrlp56hjpoys3oa
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