Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Nusrat Sharmin Islam"'
Publikováno v:
Journal of Parallel and Distributed Computing. 120:237-250
MapReduce is the most popular parallel computing framework for big data processing which allows massive scalability across distributed computing environment. Advanced RDMA-based design of Hadoop MapReduce has been proposed that alleviates the perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7afc50f1d01175d0b6989e1bbfd91392
https://doi.org/10.1016/j.jpdc.2017.11.004
https://doi.org/10.1016/j.jpdc.2017.11.004
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 28:633-646
With high performance interconnects and parallel file systems, running MapReduce over modern High Performance Computing (HPC) clusters has attracted much attention due to its uniqueness of solving data analytics problems with a combination of Big Dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fde9e4f6d8ad3959f9fd32e5d95074e
https://doi.org/10.1109/tpds.2016.2591947
https://doi.org/10.1109/tpds.2016.2591947
Publikováno v:
EuroMPI
Triggered operations and counting events or counters are building blocks used by communication libraries, such as MPI, to offload collective operations to the Host Fabric Interface (HFI) or Network Interface Card (NIC). Triggered operations can be us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2030e7d074b3866f31c735ccdb474b8
https://doi.org/10.1145/3343211.3343222
https://doi.org/10.1145/3343211.3343222
Publikováno v:
The Journal of Supercomputing. 72:4573-4600
With the emergence of high-performance data analytics, the Hadoop platform is being increasingly used to process data stored on high-performance computing clusters. While there is immense scope for improving the performance of Hadoop MapReduce (inclu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd4e2ba985570f1ded425fa88de86386
https://doi.org/10.1007/s11227-016-1760-5
https://doi.org/10.1007/s11227-016-1760-5
Publikováno v:
IEEE BigData
In this paper, we propose an accelerated execution framework (NVMD) for MapReduce and Directed Acyclic Graph (DAG) based processing engines to leverage the benefits of Non-Volatile Memory (NVM). Through NVMD, novel features for MapReduce, such as a h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::522510c770bfe0f82f3697261c2e3233
https://doi.org/10.1109/bigdata.2017.8257947
https://doi.org/10.1109/bigdata.2017.8257947
Publikováno v:
IEEE BigData
The most popular Big Data processing frameworks of these days are Hadoop MapReduce and Spark. Hadoop Distributed File System (HDFS) is the primary storage for these frameworks. Big Data frameworks like Hadoop MapReduce and Spark launch tasks based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11833568db8556486d92f50db06e5662
https://doi.org/10.1109/bigdata.2016.7840608
https://doi.org/10.1109/bigdata.2016.7840608
Publikováno v:
2016 1st Joint International Workshop on Parallel Data Storage and data Intensive Scalable Computing Systems (PDSW-DISCS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af1aadb3ab3306a3f058b25b0f04761a
https://doi.org/10.1109/pdsw-discs.2016.008
https://doi.org/10.1109/pdsw-discs.2016.008
Publikováno v:
SBAC-PAD
MapReduce is the most popular parallel computing framework for big data processing which allows massive scalability across distributed computing environment. Advanced RDMA-based design of Hadoop MapReduce has been proposed that alleviates the perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1804faa31d17b2fd4fa7ce927cbcef9b
https://doi.org/10.1109/sbac-pad.2016.33
https://doi.org/10.1109/sbac-pad.2016.33
Publikováno v:
IPDPS
High-performance, distributed key-value store-based caching solutions, such as Memcached, have played a crucial role in enhancing the performance of many Online and Offline Big Data applications. The advent of high-performance storage (e.g. NVMe SSD)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60b64bdeb02a66297b26bb73eb8b3da8
https://doi.org/10.1109/ipdps.2016.112
https://doi.org/10.1109/ipdps.2016.112
Autor:
Dhabaleswar K. Panda, Xiaoyi Lu, Dipti Shankar, Md. Wasi-ur-Rahman, Adithya Bhat, Nusrat Sharmin Islam
Publikováno v:
Big Data Benchmarks, Performance Optimization, and Emerging Hardware ISBN: 9783319290058
BPOE
BPOE
Hadoop Distributed File System (HDFS) has been popularly utilized by many Big Data processing frameworks as their underlying storage engine, such as Hadoop MapReduce, HBase, Hive, and Spark. This makes the performance of HDFS a primary concern in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8da03fd29f62f4b403c336fdd5b52abc
https://doi.org/10.1007/978-3-319-29006-5_10
https://doi.org/10.1007/978-3-319-29006-5_10
