MapReduce: simplified data processing on large clusters, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008. ,
,
Apache Spark: a unified engine for Big Data processing, Communications of the ACM, vol.59, issue.11, pp.56-65, 2016. ,
Apache Hama: an emerging bulk synchronous parallel computing framework for Big Data applications, IEEE Access, vol.4, pp.8879-8887, 2016. ,
FlameMR: an event-driven architecture for MapReduce applications, Future Generation Computer Systems, vol.65, pp.46-56, 2016. ,
, Enhancing in-memory efficiency for MapReduce-based data processing, Journal of Parallel and Distributed Computing, vol.120, pp.323-338, 2018.
The Hadoop Distributed File System, IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST'2010), pp.1-10, 2010. ,
NativeTask: a Hadoop compatible framework for high performance, 2013 IEEE International Conference on Big Data, pp.94-101, 2013. ,
MARIANE: using MapReduce in HPC environments, Future Generation Computer Systems, vol.36, pp.379-388, 2014. ,
High-performance RDMA-based design of Hadoop MapReduce over InfiniBand, 27th IEEE International Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW'13), pp.1908-1917, 2013. ,
Kira: processing astronomy imagery using Big Data technology, IEEE Transactions on Big Data, 2016. ,
DOI : 10.1109/tbdata.2016.2599926
Big Data in HEP: a comprehensive use case study, Journal of Physics: Conference Series, vol.898, issue.7, p.72012, 2017. ,
DOI : 10.1088/1742-6596/898/7/072012
URL : http://iopscience.iop.org/article/10.1088/1742-6596/898/7/072012/pdf
ROOT-an object oriented data analysis framework, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol.389, issue.1-2, pp.81-86, 1997. ,
DOI : 10.1016/s0168-9002(97)00048-x
Experiences and benefits of running RDMA-Hadoop and Spark on SDSC Comet, 5th Annual Conference on Diversity, Big Data, and Science at Scale (XSEDE'16), vol.23, p.5, 2016. ,
High-performance design of Apache Spark with RDMA and its benefits on various workloads, 2016 IEEE International Conference on Big Data, pp.253-262, 2016. ,
OEHadoop: accelerate Hadoop applications by co-designing Hadoop with Data Center Network, IEEE Access, vol.6, pp.25-849, 2018. ,
DOI : 10.1109/access.2018.2830799
URL : https://doi.org/10.1109/access.2018.2830799
Interactive analytical processing in Big Data systems: a cross-industry study of MapReduce workloads, Proceedings of the VLDB Endowment, vol.5, pp.1802-1813, 2012. ,
M3R: increased performance for in-memory Hadoop jobs, Proceedings of the VLDB Endowment, vol.5, pp.1736-1747, 2012. ,
Using memory in the right way to accelerate Big Data processing, Journal of Computer Science and Technology, vol.30, issue.1, pp.30-41, 2015. ,
X10: an object-oriented approach to non-uniform cluster computing, 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA'05), pp.519-538, 2005. ,
A Hadoop use case for engineering data, 12th International Conference on Cooperative Design, Visualization and Engineering (CDVE'15), pp.134-141, 2015. ,
DOI : 10.1007/978-3-319-24132-6_16
URL : https://hal.archives-ouvertes.fr/hal-01167510
, Hadoop distributed Big Data store, 2018.
, Grid'5000: large-scale resource provisioning network, 2018.
CloudRS: an error correction algorithm of high-throughput sequencing data based on scalable framework, 2013 IEEE International Conference on Big Data, pp.717-722, 2013. ,
High-quality draft assemblies of mammalian genomes from massively parallel sequence data, Proceedings of the National Academy of Sciences, vol.108, issue.4, pp.1513-1518, 2011. ,
, DDBJ Sequence Read Archive (DRA), 2018.
BDEv 3.0: energy efficiency and microarchitectural characterization of Big Data processing frameworks, Future Generation Computer Systems, vol.86, pp.565-581, 2018. ,
MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud, Bioinformatics, vol.33, issue.17, pp.2762-2764, 2017. ,
ParDRe: faster parallel duplicated reads removal tool for sequencing studies, Bioinformatics, vol.32, issue.10, pp.1562-1564, 2016. ,