Abstract
The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and energy-efficient computational storage developed for realizing the full potential of in-storage processing. To the best of our knowledge, Newport is the first commodity SSD that can be configured to run a server-like operating system, greatly minimizing the effort for creating and maintaining applications running inside the storage. We analyze the benefits of using Newport by running complex AI applications such as image similarity search and object tracking on a large visual dataset. The results demonstrate that data-intensive AI workloads can be efficiently parallelized and offloaded, even to a small set of Newport drives with significant performance gains and energy savings. In addition, we introduce a comprehensive taxonomy of existing computational storage solutions together with a realistic cost analysis for high-volume production, giving a good big picture of the economic feasibility of the computational storage technology.
- Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. Youtube-8m: A large-scale video classification benchmark. Arxiv Preprint Arxiv:1609.08675 (2016).Google Scholar
- I. Aleksander, M. De Gregorio, F. Maia Galvão França, P. Machado Vieira Lima, and H. Morton. 2009. A brief introduction to weightless neural systems. In Proceedings of the 17th European Symposium on Artificial Neural Networks. Retrieved from https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2009-6.pdf.Google Scholar
- I. Aleksander, W. V. Thomas, and P. A. Bowden. 1984. WISARDa radical step forward in image recognition. Sensor Rev. 4, 3 (1984), 120--124. DOI:https://doi.org/10.1108/eb007637Google Scholar
Cross Ref
- Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull. 2020. ANN-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inf. Syst. 87 (2020), 101374.Google Scholar
Digital Library
- Doug Beaver, Sanjeev Kumar, Harry C. Li, Jason Sobel, Peter Vajgel et al. 2010. Finding a needle in haystack: Facebook’s photo storage. In Proceedings of the Symposium on Operating Systems Design and Implementation, Vol. 10. 1--8.Google Scholar
- D. S. Bolme. 2010. Visual object tracking using adaptive correlation filters. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
Cross Ref
- Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, and Jia Li. 2019. Salient object detection: A survey. Comput. Vis. Media (2019), 1--34.Google Scholar
- Neil Briscoe. 2000. Understanding the OSI 7-layer model. PC Netw. Advis. 120, 2 (2000).Google Scholar
- Li-Pin Chang, Tei-Wei Kuo, and Shi-Wu Lo. 2004. Real-time garbage collection for flash-memory storage systems of real-time embedded systems. ACM Trans. Embed. Comput. Syst. 3, 4 (2004), 837--863.Google Scholar
Digital Library
- Sangyeun Cho, Chanik Park, Hyunok Oh, Sungchan Kim, Youngmin Yi, and Gregory R. Ganger. 2013. Active disk meets flash: A case for intelligent SSDs. In Proceedings of the 27th International ACM Conference on International Conference on Supercomputing. ACM, 91--102.Google Scholar
- Tae-Sun Chung, Dong-Joo Park, Sangwon Park, Dong-Ho Lee, Sang-Won Lee, and Ha-Joo Song. 2009. A survey of flash translation layer. J. Syst. Archit. 55, 5–6 (2009), 332--343.Google Scholar
Digital Library
- Michael Cornwell. 2012. Anatomy of a solid-state drive.Commun. ACM 55, 12 (2012), 59--63.Google Scholar
Digital Library
- Leonardo Dagum and Ramesh Menon. 1998. OpenMP: An industry-standard API for shared-memory programming. Comput. Sci. Eng.1 (1998), 46--55.Google Scholar
Digital Library
- Trevor Darrell, Piotr Indyk, and Gregory Shakhnarovich. 2005. Nearest-neighbor Methods in Learning and Vision: Theory and Practice. The MIT Press.Google Scholar
- Jaeyoung Do, Yang-Suk Kee, Jignesh M. Patel, Chanik Park, Kwanghyun Park, and David J. DeWitt. 2013. Query processing on smart SSDs: Opportunities and challenges. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 1221--1230.Google Scholar
- Jaeyoung Do, Sudipta Sengupta, and Steven Swanson. 2019. Programmable solid-state storage in future cloud datacenters. Commun. ACM 62, 6 (2019), 54--62.Google Scholar
Digital Library
- Daniel N. Do Nascimiento, Rafael Lima De Carvalho, Felix Mora-Camino, Priscila V. M. Lima, and Felipe M. G. Franca. 2015. A WiSARD-based multi-term memory framework for online tracking of objects. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 978–287587014–8. DOI:https://doi.org/10.13140/RG.2.1.3387.5687Google Scholar
- Carlotta Domeniconi, Jing Peng, and Dimitrios Gunopulos. 2002. Locally adaptive metric nearest-neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 24, 9 (2002), 1281--1285.Google Scholar
Digital Library
- Eideticom. 2020. Retrieved from https://www.eideticom.com/.Google Scholar
- K. Eshghi and Rino Micheloni. 2013. SSD architecture and PCI express interface. In Inside Solid State Drives (SSDs). Springer, 19--45.Google Scholar
- A. E. Eshratifar, M. S. Abrishami, and M. Pedram. 2019. JointDNN: An efficient training and inference engine for intelligent mobile cloud computing services. IEEE Trans. Mob. Comput. (2019), 1--1.Google Scholar
- Mark Fasheh. 2006. OCFS2: The Oracle Clustered File System, version 2. In Proceedings of the Linux Symposium, Vol. 1. 289--302.Google Scholar
- Horacio L. França, João Carlos P. da Silva, Omar Lengerke, Max Suell Dutra, Massimo De Gregorio, and Felipe Maia Galvão França. 2010. Movement persuit control of an offshore automated platform via a RAM-based neural network. In Proceedings of the 11th International Conference on Control Automation Robotics 8 Vision. 2437--2441.Google Scholar
Cross Ref
- John Gantz and David Reinsel. 2012. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East. IDC iView: IDC Analyze the future 2007, 2012 (2012), 1--16.Google Scholar
- Richard L. Graham, Timothy S. Woodall, and Jeffrey M. Squyres. 2006. Open MPI: A flexible high performance MPI. In Parallel Processing and Applied Mathematics, Roman Wyrzykowski, Jack Dongarra, Norbert Meyer, and Jerzy Waśniewski (Eds.). Springer Berlin, 228--239.Google Scholar
- William Gropp, Ewing Lusk, Nathan Doss, and Anthony Skjellum. 1996. A high-performance, portable implementation of the MPI message passing interface standard. Parallel Comput. 22, 6 (1996), 789--828.Google Scholar
Digital Library
- SSD Form Factor Working Group. 2011. Retrieved from http://www.ssdformfactor.org/docs/SSD_Form_Factor_Version1_00.pdf.Google Scholar
- Boncheol Gu, Andre S. Yoon, Duck-Ho Bae, Insoon Jo, Jinyoung Lee, Jonghyun Yoon, Jeong-Uk Kang, Moonsang Kwon, Chanho Yoon, Sangyeun Cho et al. 2016. Biscuit: A framework for near-data processing of big data workloads. In ACM SIGARCH Comput. Archit. News, Vol. 44. IEEE Press, 153--165.Google Scholar
- Aayush Gupta, Youngjae Kim, and Bhuvan Urgaonkar. 2009. DFTL: A Flash Translation Layer Employing Demand-based Selective Caching of Page-level Address Mappings. Vol. 44. ACM.Google Scholar
Digital Library
- Trevor Hastie and Robert Tibshirani. 1996. Discriminant adaptive nearest neighbor classification and regression. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 409--415.Google Scholar
- David Held, Sebastian Thrun, and Silvio Savarese. 2016. Learning to track at 100 fps with deep regression networks. In Proceedings of the European Conference on Computer Vision. Springer, 749--765.Google Scholar
Cross Ref
- João F. Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista. 2014. High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 3 (2014), 583--596.Google Scholar
Digital Library
- Ali HeydariGorji, Siavash Rezaei, Mahdi Torabzadehkashi, Hossein Bobarshad, Vladimir Alves, and Pai H. Chou. 2020. HyperTune: Dynamic hyperparameter tuning for efficient distribution of DNN training over heterogeneous systems. Arxiv Preprint Arxiv:2007.08077 (2020).Google Scholar
- Ali HeydariGorji, Mahdi Torabzadehkashi, Siavash Rezaei, Hossein Bobarshad, Vladimir Alves, and Pai H. Chou. 2020. STANNIS: Low-Power Acceleration of Deep Neural Network Training Using Computational Storage Devices. Retrieved from arxiv:cs.DC/2002.07215.Google Scholar
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q . Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4700--4708.Google Scholar
Cross Ref
- Jeff Janukowicz. October 2018. How New QLC SSDs Will Change the Storage Landscape. Technical Report. Micron. Retrieved from https://www.micron.com/-/media/client/global/documents/products/white-paper/how_new_qlc_ssds_will_change_the_storage_landscape.pdf?la=en.Google Scholar
- Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2011. Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1 (2011), 117--128.Google Scholar
Digital Library
- Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. Arxiv Preprint Arxiv:1408.5093 (2014).Google Scholar
- Insoon Jo, Duck-Ho Bae, Andre S. Yoon, Jeong-Uk Kang, Sangyeun Cho, Daniel D. G. Lee, and Jaeheon Jeong. 2016. YourSQL: A high-performance database system leveraging in-storage computing. Proc. VLDB Endow. 9, 12 (2016), 924--935.Google Scholar
Digital Library
- Sang-Woo Jun, Ming Liu, Sungjin Lee, Jamey Hicks, John Ankcorn, Myron King, Shuotao Xu, and Arvind. 2015. BlueDBM: An appliance for big data analytics. In Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA ’15). ACM, New York, NY, 1--13. DOI:https://doi.org/10.1145/2749469.2750412Google Scholar
Digital Library
- Yangwook Kang, Yang-suk Kee, Ethan L. Miller, and Chanik Park. 2013. Enabling cost-effective data processing with smart SSD. In Proceedings of the IEEE 29th Symposium on Mass Storage Systems and Technologies (MSST’13). IEEE, 1--12.Google Scholar
Cross Ref
- Sungchan Kim, Hyunok Oh, Chanik Park, Sangyeun Cho, Sang-Won Lee, and Bongki Moon. 2016. In-storage processing of database scans and joins. Inf. Sci. 327 (2016), 183--200.Google Scholar
Digital Library
- Gunjae Koo, Kiran Kumar Matam, H. V. Narra, Jing Li, Hung-Wei Tseng, Steven Swanson, Murali Annavaram et al. 2017. Summarizer: Trading communication with computing near storage. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, 219--231.Google Scholar
Digital Library
- Matej Kristan, Jiri Matas, Aleš Leonardis, Tomas Vojir, Roman Pflugfelder, Gustavo Fernandez, Georg Nebehay, Fatih Porikli, and Luka Čehovin. 2016. A novel performance evaluation methodology for single-target trackers. IEEE Trans. Pattern Anal. Mach. Intell. 38, 11 (Nov. 2016), 2137--2155. DOI:https://doi.org/10.1109/TPAMI.2016.2516982Google Scholar
Digital Library
- Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. 2018. High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8971--8980.Google Scholar
Cross Ref
- Rafael Lima De Carvalho, Danilo S. C. Carvalho, Felix Mora-Camino, Priscila V. M. Lima, and Felipe M. G. França. 2014. Online tracking of multiple objects using WiSARD. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 541--546. Retrieved from https://hal-enac.archives-ouvertes.fr/hal-01059678.Google Scholar
- David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91--110.Google Scholar
Digital Library
- David J. C. MacKay and Radford M. Neal. 1996. Near Shannon limit performance of low density parity check codes. Electron. Lett. 32, 18 (1996), 1645--1646.Google Scholar
Cross Ref
- Pankaj Mehra. 2019. Samsung smartSSD: Accelerating data-rich applications. In Proceedings of the Flash Memory Summit.Google Scholar
- Dirk Merkel. 2014. Docker: Lightweight Linux containers for consistent development and deployment. Linux J. 2014, 239 (2014), 2.Google Scholar
Digital Library
- Hyeonseob Nam and Bohyung Han. 2016. Learning multi-domain convolutional neural networks for visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4293--4302.Google Scholar
Cross Ref
- Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, and Kushagra Vaid. 2016. SSD failures in datacenters: What? when? and why?. In Proceedings of the 9th ACM International on Systems and Storage Conference. ACM.Google Scholar
Digital Library
- Rishiyur S. Nikhil. 2009. What is bluespec? ACM SIGDA Newslett. 39, 1 (2009), 1--1.Google Scholar
Digital Library
- Guanghan Ning, Zhi Zhang, Chen Huang, Xiaobo Ren, Haohong Wang, Canhui Cai, and Zhihai He. 2017. Spatially supervised recurrent convolutional neural networks for visual object tracking. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’17). IEEE, 1--4.Google Scholar
Cross Ref
- Shigeo Ohshima and Yoichiro Tanaka. 2016. New 3D flash technologies offer both low cost and low power solutions. In Proceedings of the Flash Memory Summit.Google Scholar
- ONFI online. 2017. Open NAND Flash interface specification. Retrieved from http://www.onfi.org/specifications.Google Scholar
- PCI-SIG. 2020. Retrieved from https://pcisig.com/specifications/pciexpress/M.2_Specification/.Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779--788.Google Scholar
Cross Ref
- Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. Arxiv Preprint Arxiv:1804.02767 (2018).Google Scholar
- IDC Report. 2018. The digitization of the world from edge to core. Retrieved from https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf.Google Scholar
- Siavash Rezaei, Eli Bozorgzadeh, and Kanghee Kim. 2019. UltraShare: FPGA-based dynamic accelerator sharing and allocation. In Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig’19). IEEE, 1--5.Google Scholar
Cross Ref
- S. Rezaei, C. Hernandez-Calderon, S. Mirzamohammadi, E. Bozorgzadeh, A. Veidenbaum, A. Nicolau, and M. J. Prather. 2016. Data-rate-aware FPGA-based acceleration framework for streaming applications. In Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig’16). 1--6. DOI:https://doi.org/10.1109/ReConFig.2016.7857162Google Scholar
- Siavash Rezaei, Kanghee Kim, and Eli Bozorgzadeh. 2018. Scalable multi-queue data transfer scheme for FPGA-based multi-accelerators. In Proceedings of the IEEE 36th International Conference on Computer Design (ICCD’18). 374--380.Google Scholar
Cross Ref
- Steve Roddy. 2019. Arm NN: the Easy Way to Deploy Edge ML. Retrieved from https://community.arm.com/developer/tools-software/tools/b/tools-software-ides-blog/posts/arm-nn-the-easy-way-to-deploy-edge-ml?_ga=2.9822706.6940669.1579038789-277442185.1570226249.Google Scholar
- Mohammad Samragh, Mojan Javaheripi, and Farinaz Koushanfar. 2019. CodeX: Bit-flexible encoding for streaming-based FPGA acceleration of DNNs. CoRR abs/1901.05582 (2019).Google Scholar
- Scaleflux. 2020. Retrieved from http://scaleflux.com/index.html.Google Scholar
- Sudharsan Seshadri, Mark Gahagan, Meenakshi Sundaram Bhaskaran, Trevor Bunker, Arup De, Yanqin Jin, Yang Liu, and Steven Swanson. 2014. Willow: A user-programmable SSD. In Proceedings of the Symposium on Operating Systems Design and Implementation. 67--80.Google Scholar
- Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. 2010. The Hadoop distributed file system. In Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST’10). IEEE, 1--10.Google Scholar
Digital Library
- Josef Sivic and Andrew Zisserman. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision, Vol. 2. IEEE, 1470–1477.Google Scholar
Cross Ref
- SNIA. 2019. Computational Storage Technical Working Group. Retrieved from https://www.snia.org/computational.Google Scholar
- SolarWinds. 2018. Can gzip Compression Really Improve Web Performance? Retrieved from https://royal.pingdom.com/can-gzip-compression-really-improve-web-performance/.Google Scholar
- Steven R. Soltis, G. M. Erickson, Kenneth W. Preslan, Matthew T. O’Keefe, and Thomas M. Ruwart. 1997. The global file system: A file system for shared disk storage. IEEE Transactions on Parallel and Distributed Systems 1 (1997), 1.Google Scholar
- Mohan Srinivasan. 2014. Flashcache. Retrieved from https://github.com/facebookarchive/flashcache.Google Scholar
- NGD Systems. 2020. Retrieved from https://www.ngdsystems.com.Google Scholar
- Ted Friedman, Thomas Bittman, Neil MacDonald. 2019. How to Overcome Four Major Challenges in Edge Computing. Retrieved from https://www.gartner.com/doc/reprints?id=1-1XWDQ2PW8ct=1912108st=sb.Google Scholar
- Devesh Tiwari, Simona Boboila, Sudharshan S. Vazhkudai, Youngjae Kim, Xiaosong Ma, Peter Desnoyers, and Yan Solihin. 2013. Active flash: Towards energy-efficient, in-situ data analytics on extreme-scale machines. In Proceedings of the USENIX Conference on File and Storage Technologies. 119--132.Google Scholar
- Mahdi Torabzadehkashi, Ali Heydarigorji, Siavash Rezaei, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Accelerating HPC applications using computational storage devices. In Proceedings of the IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS’19). IEEE, 1878--1885.Google Scholar
- Mahdi Torabzadehkashi, Siavash Rezaei, Vladimir Alves, and Nader Bagherzadeh. 2018. CompStor: An in-storage computation platform for scalable distributed processing. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW’18). IEEE, 1260--1267.Google Scholar
Cross Ref
- Mahdi Torabzadehkashi, Siavash Rezaei, Ali Heydarigorji, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Catalina: In-storage processing acceleration for scalable big data analytics. In Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP’19). IEEE, 430--437.Google Scholar
Cross Ref
- Mahdi Torabzadehkashi, Siavash Rezaei, Ali HeydariGorji, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Computational storage: An efficient and scalable platform for big data and HPC applications. J. Big Data 6, 1 (2019), 100.Google Scholar
Cross Ref
- Jack Valmadre, Luca Bertinetto, João Henriques, Andrea Vedaldi, and Philip H. S. Torr. 2017. End-to-end representation learning for correlation filter based tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2805--2813.Google Scholar
- DRAM Exchange Website. 2020. DRAM Exchange. Retrieved from https://www.dramexchange.com.Google Scholar
- Part Stock Website. 2020. Part Stock Specs for Xilinx XCZU19EG-2FFVC1760E. Retrieved from http://www.part-stock.com/product-part/xilinx__XCZU19EG-2FFVC1760E.html.Google Scholar
- Kilian Q. Weinberger and Lawrence K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(Feb. 2009), 207--244.Google Scholar
- Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. 2015. Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37, 9 (2015), 1834--1848.Google Scholar
Digital Library
- Ming-Chang Yang, Yu-Ming Chang, Che-Wei Tsao, Po-Chun Huang, Yuan-Hao Chang, and Tei-Wei Kuo. 2014. Garbage collection and wear leveling for flash memory: Past and future. In Proceedings of the International Conference on Smart Computing. IEEE, 66--73.Google Scholar
Cross Ref
- Kai Zhao, Wenzhe Zhao, Hongbin Sun, Xiaodong Zhang, Nanning Zheng, and Tong Zhang. 2013. LDPC-in-SSD: Making advanced error correction codes work effectively in solid state drives. In Proceedings of the 11th USENIX Conference on File and Storage Technologies (FAST’13). 243--256.Google Scholar
Digital Library
Index Terms
Cost-effective, Energy-efficient, and Scalable Storage Computing for Large-scale AI Applications
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