Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Dipti Shankar"'
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep learning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1923ae10e21213895d608dac797e1cc6
https://doi.org/10.7551/mitpress/11451.001.0001
https://doi.org/10.7551/mitpress/11451.001.0001
Publikováno v:
Current Research Journal of Social Sciences & Humanities; 2023, Vol. 6 Issue 1, p122-131, 10p
Publikováno v:
SC
Byte-addressable persistent memory (PMEM) can be directly manipulated by Remote Direct Memory Access (RDMA) capable networks. However, existing studies to combine RDMA and PMEM can not deliver the desired performance due to their PMEM-oblivious commu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0da305640a63ed7e4c22330e6d3c61a
https://doi.org/10.1109/sc41405.2020.00056
https://doi.org/10.1109/sc41405.2020.00056
Byte-addressable persistent memory (PMEM) can be directly manipulated by Remote Direct Memory Access (RDMA) capable networks. However, existing studies to combine RDMA and PMEM can not deliver the desired performance due to their PMEM-oblivious commu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d97592819bc79e4a4a3bbdc3e2e77a4
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:
HiPC
Modern distributed key-value store-based applications rely on bulk-read operations like 'Multi-Get' (MGet) to accelerate their data serving phase. While state-of-the-art database systems employ SIMD-based techniques to optimize data-parallel operatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d804938f9a2e26a007ee864df698b82
https://doi.org/10.1109/hipc.2019.00040
https://doi.org/10.1109/hipc.2019.00040
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep learning.Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness
Publikováno v:
IISWC
With the emergence of modern multi-core CPU architectures that support data parallelism via vectorization, several storage systems have been employing SIMD-based techniques to optimize data-parallel operations on in-memory structures like hash-tables
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a544f06f1271632e1759c092e576534
https://doi.org/10.1109/iiswc47752.2019.9042069
https://doi.org/10.1109/iiswc47752.2019.9042069
Publikováno v:
HPDC
Distributed storage systems typically need data to be stored redundantly to guarantee data durability and reliability. While the conventional approach towards this objective is to store multiple replicas, today's unprecedented data growth rates encou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89ce68b96a96ed2d9d4f8e735d21ca6c
https://doi.org/10.1145/3307681.3325406
https://doi.org/10.1145/3307681.3325406
Publikováno v:
IEEE BigData
Efficient Big Data analytics on Cloud Computing systems is still full of challenges. One of the biggest hurdles is the unsatisfactory performance offered by underlying virtualized I/O devices such as networks. To address this issue, the modern cloud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db09717b82a39390bbba3f55f3d22398
https://doi.org/10.1109/bigdata.2018.8622615
https://doi.org/10.1109/bigdata.2018.8622615