skip to main content
article

Disaggregated GPU Acceleration for Serverless Applications

Published:28 June 2023Publication History
Skip Abstract Section

Abstract

Serverless platforms have been attracting applications from traditional platforms because infrastructure management responsibilities are shifted from users to providers. Many applications well-suited to serverless environments could leverage GPU acceleration to enhance their performance. Unfortunately, current serverless platforms do not expose GPUs to serverless applications.

References

  1. ArcFace. (Accessed: October 2021).Google ScholarGoogle Scholar
  2. Best practices for GPU-accelerated instances. (Accessed: May, 2023).Google ScholarGoogle Scholar
  3. Deploy GPU-enabled container instance - Azure Container Instances | Microsoft Learn. (Accessed: May, 2023).Google ScholarGoogle Scholar
  4. End-to-End Solutions for AI/ML Workloads | VMware. (Accessed: October, 2021).Google ScholarGoogle Scholar
  5. NVIDIA GRID. (Accessed: October 2021).Google ScholarGoogle Scholar
  6. OpenFaaS - Serverless Functions Made Simple. (Accessed: January 2021).Google ScholarGoogle Scholar
  7. ShahinSHH/COVID-CT-MD : A COVID-19 CT Scan Dataset Applicable in Machine Learning and Deep Learning. (Accessed: October, 2021).Google ScholarGoogle Scholar
  8. Underutilizing Cloud Computing Resources. (Accessed: October 2021).Google ScholarGoogle Scholar
  9. M. Amaral, Jordà Polo, David Carrera, N. Gonzalez, Chih-Chieh Yang, Alessandro Morari, Bruce D. D'Amora, A. Youssef, and M. Steinder. Drmaestro: orchestrating disaggregated resources on virtualized datacenters. Journal of Cloud Computing, 10:1--20, 2021.Google ScholarGoogle Scholar
  10. Zhihao Bai, Zhen Zhang, Yibo Zhu, and Xin Jin. Pipeswitch: Fast pipelined context switching for deep learning applications. In 14th USENIX OSDI 2020, pages 499--514. USENIX Association, November 2020.Google ScholarGoogle Scholar
  11. Chandra Chekuri and Sanjeev Khanna. On multidimensional packing problems. In Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms, pages 185--194. Citeseer, 1999.Google ScholarGoogle Scholar
  12. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR 09. IEEE, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  13. Jiankang Deng, Jia Guo, Xue Niannan, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  14. Jiankang Deng, Jia Guo, Zhou Yuxiang, Jinke Yu, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-stage dense face localisation in the wild. In arxiv, 2019.Google ScholarGoogle Scholar
  15. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.Google ScholarGoogle Scholar
  16. K. M. Diab, M. M. Rafique, and M. Hefeeda. Dynamic sharing of gpus in cloud systems. In 2013 IEEE ISPA, Workshops and Phd Forum, pages 947--954, 2013.Google ScholarGoogle Scholar
  17. Yaozu Dong, Xiaowei Yang, Jianhui Li, Guangdeng Liao, Kun Tian, and Haibing Guan. High Performance Network Virtualization with SR-IOV. Journal of Parallel and Distributed Computing, 72(11):1471--1480, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Yaozu Dong, Zhao Yu, and Greg Rose. SR-IOV Networking in Xen: Architecture, Design and Implementation. In Workshop on I/O Virtualization, 2008.Google ScholarGoogle Scholar
  19. Micah Dowty and Jeremy Sugerman. GPU virtualization on VMware's hosted I/O architecture. ACM SIGOPS Operating Systems Review, 43(3):73--82, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Dong Du, Tianyi Yu, Yubin Xia, Binyu Zang, Guanglu Yan, Chenggang Qin, Qixuan Wu, and Haibo Chen. Catalyzer: Sub-millisecond startup for serverless comGoogle ScholarGoogle Scholar
  21. José Duato, Antonio J. Pena, Federico Silla, Juan C. Fernandez, Rafael Mayo, and Enrique S. Quintana-Orti. Enabling CUDA Acceleration Within Virtual Machines Using rCUDA. In Proceedings of the 2011 18th HIPC, pages 1--10, Washington, DC, USA, 2011. IEEE Computer Society.Google ScholarGoogle Scholar
  22. Henrique Fingler, Zhiting Zhu, Esther Yoon, Zhipeng Jia, EmmettWitchel, and Christopher J. Rossbach. Dgsf: Disaggregated gpus for serverless functions. In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 739--750, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  23. G. Giunta, R. Montella, G. Agrillo, and G. Coviello. A gpgpu transparent virtualization component for high performance computing clouds. Euro-Par 2010-Parallel Processing, pages 379--391, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  24. Anubhav Guleria, J Lakshmi, and Chakri Padala. Quadd: Quantifying accelerator disaggregated datacenter efficiency. In 2019 IEEE 12th International CLOUD, pages 349--357, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  25. Fan Guo, Yongkun Li, John C. S. Lui, and Yinlong Xu. Dcuda: Dynamic gpu scheduling with live migration support. In Proceedings of the ACM SoCC, page 114--125, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarGoogle Scholar
  26. Vishakha Gupta, Ada Gavrilovska, Karsten Schwan, Harshvardhan Kharche, Niraj Tolia, Vanish Talwar, and Parthasarathy Ranganathan. GViM: GPU-accelerated Virtual Machines. In Proceedings of the 3rd ACM Workshop HPCVirt, pages 17--24, New York, NY, USA, 2009. ACM.Google ScholarGoogle Scholar
  27. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE CVPR, pages 770--778, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  28. B. Hu and C. J. Rossbach. Altis: Modernizing gpgpu benchmarks. In 2020 IEEE ISPASS, pages 1--11, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  29. Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07--49, University of Massachusetts, Amherst, October 2007.Google ScholarGoogle Scholar
  30. Paras Jain, Xiangxi Mo, Ajay Jain, Harikaran Subbaraj, Rehan Sohail Durrani, Alexey Tumanov, Joseph Gonzalez, and Ion Stoica. Dynamic space-time scheduling for GPU inference. In Thirty-second Conference on Neural Information Processing Systems, 2018.Google ScholarGoogle Scholar
  31. Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar, Reza Reiazi, Fatemeh Homayounieh, Engy Abbas, Azadeh Laali, Amir Reza Radmard, Mohammad Hadi Gharib, Seyed Ali Javad Mousavi, Omid Ghaemi, Rosa Babaei, Hadi Karimi Mobin, Mehdi Hosseinzadeh, Rana Jahanban-Esfahlan, Khaled Seidi, Mannudeep K. Kalra, Guanglan Zhang, L. T. Chitkushev, Benjamin Haibe-Kains, Reza Malekzadeh, and Reza Rawassizadeh. Covidctnet: an open-source deep learning approach to diagnose covid-19 using small cohort of ct images. npj Digital Medicine, 4(1), December 2021.Google ScholarGoogle Scholar
  32. Hee Seung Jo, Myung Ho Lee, and Dong Hoon Choi. Gpu virtualization using PCI direct pass-through. In Information, Communication and Engineering, volume 311 of Applied Mechanics and Materials, pages 15--19. Trans Tech Publications Ltd, 5 2013.Google ScholarGoogle Scholar
  33. Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, and Benjamin Recht. Occupy the cloud: Distributed computing for the 99%. In Proceedings SoCC 2017, pages 445--451, New York, NY, USA, 2017. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, D. Killebrew, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon. Indatacenter performance analysis of a tensor processing unit. In 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), pages 1--12, June 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jaewook Kim, Tae Joon Jun, Daeyoun Kang, Dohyeun Kim, and Daeyoung Kim. Gpu enabled serverless computing framework. In 2018 26th Euromicro International Conference on Parallel, Distributed and Networkbased Processing (PDP), pages 533--540, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  36. U. Kurkure, H. Sivaraman, and L. Vu. Virtualized gpus in high performance datacenters. In 2018 HPCS, pages 887--894, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  37. Kuan-Ching Li,Keunsoo Kim,WonW. Ro, Tien-Hsiung Weng, Che-Lun Hung, Chen-Hao Ku, Albert Cohen, andGoogle ScholarGoogle Scholar
  38. Anup Mohan, Harshad Sane, Kshitij Doshi, Saikrishna Edupuganti, Naren Nayak, and Vadim Sukhomlinov. Agile cold starts for scalable serverless. In 11th USENIX HotCloud 19, Renton, WA, July 2019. USENIX Association.Google ScholarGoogle Scholar
  39. Diana M. Naranjo, Sebastián Risco, Carlos de Alfonso, Alfonso Pérez, Ignacio Blanquer, and Germán Moltó. Accelerated serverless computing based on gpu virtualization. Journal of Parallel and Distributed Computing, 139:32--42, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Bo Peng, Haozhong Zhang, Jianguo Yao, Yaozu Dong, Yu Xu, and Haibing Guan. MDev-NVMe: a NVMe storage virtualization solution with mediated pass-through. In 2018 USENIX ATC, pages 665--676, 2018.Google ScholarGoogle Scholar
  41. Javier Prades and Federico Silla. Gpu-job migration: The rcuda case. IEEE Transactions on Parallel and Distributed Systems, 30(12):2718--2729, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  42. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Squad: 100, 000+ questions for machine comprehension of text. CoRR, abs/1606.05250, 2016.Google ScholarGoogle Scholar
  43. Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, and Srimat Chakradhar. Supporting gpu sharing in cloud environments with a transparent runtime consolidation framework. In Proceedings of the 20th HPDC, page 217--228, New York, NY, USA, 2011. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Carlos Reaño, Antonio J. Peña, Federico Silla, José Duato, Rafael Mayo, and Enrique S. Quintana-Ortí. CU2rCU: Towards the complete rCUDA remote GPU virtualization and sharing solution. 20th Annual International Conference on High Performance Computing, 0:1--10, 2012.Google ScholarGoogle Scholar
  45. Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, and Yuchen Zhou. Mlperf inference benchmark, 2019.Google ScholarGoogle Scholar
  46. Mehdi Sheikhalishahi, Richard M. Wallace, Lucio Grandinetti, José Luis Vazquez-Poletti, and Francesca Guerriero. A multi-dimensional job scheduling. Future Generation Computer Systems, 54:123--131, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Lin Shi, Hao Chen, Jianhua Sun, and Kenli Li. vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Trans. Comput., 61(6):804--816, June 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Jike Song, Zhiyuan Lv, and Kevin Tian. KVMGT: a Full GPU Virtualization Solution. In KVM Forum, volume 2014, 2014.Google ScholarGoogle Scholar
  49. State of the cloud report. https://www.rightscale.com/lp/state-of-the-cloud. (Accessed: January, 2021).Google ScholarGoogle Scholar
  50. Yusuke Suzuki, Hiroshi Yamada, Shinpei Kato, and Kenji Kono. Gloop: An event-driven runtime for consolidating gpgpu applications. In Proceedings SoCC 2017, page 80--93, New York, NY, USA, 2017. Association for Computing Machinery.Google ScholarGoogle Scholar
  51. Kun Tian, Yaozu Dong, and David Cowperthwaite. A Full GPU Virtualization Solution with Mediated Pass- Through. In 2014 USENIX ATC, pages 121--132. USENIX Association, June 2014.Google ScholarGoogle Scholar
  52. Alexey Tumanov, James Cipar, Gregory R. Ganger, and Michael A. Kozuch. Alsched: Algebraic scheduling of mixed workloads in heterogeneous clouds. In Proceedings of the Third ACM Symposium on Cloud Computing, New York, NY, USA, 2012. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Alexey Tumanov, Timothy Zhu, Jun Woo Park, Michael A. Kozuch, Mor Harchol-Balter, and Gregory R. Ganger. Tetrisched: Global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters. In Proceedings of the Eleventh ACM European Conference in Computer Systems (EuroSys), New York, NY, USA, 2016. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Lan Vu, Hari Sivaraman, and Rishi Bidarkar. GPU Virtualization for High Performance General Purpose Computing on the ESX Hypervisor. In Proceedings of HPC Symposium, pages 2:1--2:8, 2014.Google ScholarGoogle Scholar
  55. Lei Xia, Jack Lange, Peter Dinda, and Chang Bae. Investigating virtual passthrough I/O on commodity devices. ACM SIGOPS Operating Systems Review, 43(3):83--94, 2009. 19Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Shucai Xiao, Pavan Balaji, James Dinan, Qian Zhu, Rajeev Thakur, Susan Coghlan, Heshan Lin, Gaojin Wen, Jue Hong, and Wu-chun Feng. Transparent accelerator migration in a virtualized GPU environment. In Proceedings of the 12th IEEE/ACM CCGrid, pages 124--131, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, and Lidong Zhou. Gandiva: Introspective cluster scheduling for deep learning. In 13th USENIX 2018 OSDI, pages 595--610, Carlsbad, CA, October 2018. USENIX Association.Google ScholarGoogle Scholar
  58. Mengting Yan, Paul Castro, Perry Cheng, and Vatche Ishakian. Building a chatbot with serverless computing. In Proceedings of the 1st MOTA, New York, NY, USA, 2016. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Shuo Yang, Ping Luo, Chen Change Loy, and Xiaoou Tang. Wider face: A face detection benchmark. In 2016 IEEE CVPR, pages 5525--5533, 2016.Google ScholarGoogle Scholar
  60. Hangchen Yu, Arthur Michener Peters, Amogh Akshintala, and Christopher J. Rossbach. AvA: Accelerated virtualization of accelerators. In International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 807-- 825. ACM, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Hangchen Yu and Christopher J Rossbach. Full Virtualization for GPUs Reconsidered. In 14th WDDD, ISCA, 2017.Google ScholarGoogle Scholar
  62. Peifeng Yu and Mosharaf Chowdhury. Fine-grained gpu sharing primitives for deep learning applications. In I. Dhillon, D. Papailiopoulos, and V. Sze, editors, PLMR 20, volume 2, pages 98--111, 2020.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGOPS Operating Systems Review
    ACM SIGOPS Operating Systems Review  Volume 57, Issue 1
    SIGOPS
    June 2023
    53 pages
    ISSN:0163-5980
    DOI:10.1145/3606557
    Issue’s Table of Contents

    Copyright © 2023 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 June 2023

    Check for updates

    Qualifiers

    • article
  • Article Metrics

    • Downloads (Last 12 months)356
    • Downloads (Last 6 weeks)27

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader