Computational storage: an efficient and scalable platform for big data and HPC applications
I tiakina i:
| I whakaputaina i: | Journal of Big Data vol. 6, no. 1 (Nov 2019), p. 1 |
|---|---|
| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , , , |
| I whakaputaina: |
Springer Nature B.V.
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2315414097 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2196-1115 | ||
| 024 | 7 | |a 10.1186/s40537-019-0265-5 |2 doi | |
| 035 | |a 2315414097 | ||
| 045 | 2 | |b d20191101 |b d20191130 | |
| 100 | 1 | |a Torabzadehkashi, Mahdi |u University of California, Irvine (UCI), Irvine, USA; NGD Systems, Inc., Irvine, USA | |
| 245 | 1 | |a Computational storage: an efficient and scalable platform for big data and HPC applications | |
| 260 | |b Springer Nature B.V. |c Nov 2019 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively. | |
| 653 | |a Data centers | ||
| 653 | |a Data management | ||
| 653 | |a Data processing | ||
| 653 | |a Storage systems | ||
| 653 | |a Application servers | ||
| 653 | |a Computer storage devices | ||
| 653 | |a Microprocessors | ||
| 653 | |a Electronic devices | ||
| 653 | |a Computational efficiency | ||
| 653 | |a Algorithms | ||
| 653 | |a Engines | ||
| 653 | |a Neon | ||
| 653 | |a Energy consumption | ||
| 653 | |a Servers | ||
| 653 | |a Benchmarks | ||
| 653 | |a Distributed processing | ||
| 653 | |a Big Data | ||
| 653 | |a High performance computing | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Application | ||
| 653 | |a Prototypes | ||
| 653 | |a Storage | ||
| 653 | |a Motivation | ||
| 700 | 1 | |a Rezaei, Siavash |u University of California, Irvine (UCI), Irvine, USA; NGD Systems, Inc., Irvine, USA | |
| 700 | 1 | |a HeydariGorji, Ali |u University of California, Irvine (UCI), Irvine, USA; NGD Systems, Inc., Irvine, USA | |
| 700 | 1 | |a Bobarshad, Hosein |u NGD Systems, Inc., Irvine, USA | |
| 700 | 1 | |a Alves, Vladimir |u NGD Systems, Inc., Irvine, USA | |
| 700 | 1 | |a Bagherzadeh, Nader |u University of California, Irvine (UCI), Irvine, USA | |
| 773 | 0 | |t Journal of Big Data |g vol. 6, no. 1 (Nov 2019), p. 1 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2315414097/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2315414097/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |