Abstract
In this paper, we present a novel communication scheme called zero-sided RDMA, enabling data exchange as a native network service using a programmable switch. In contrast to one- or two-sided RDMA, in zero-sided RDMA, neither the sender nor the receiver is actively involved in data exchange. Zero-sided RDMA thus enables efficient RDMA-based data shuffling between heterogeneous hardware devices in a disaggregated setup without the need to implement a complete RDMA stack on each heterogeneous device or the need for a CPU that is co-located with the accelerator to coordinate the data transfer. As such, we think that zero-sided RDMA is a major building block to make efficient use of heterogeneous accelerators in future cloud DBMSs. In our evaluation, we show that zero-sided RDMA can outperform existing one-sided RDMA-based schemes for accelerator-to-accelerator communication and thus speed up typical distributed database operations such as joins.
- Elena Agostini, Davide Rossetti, and Sreeram Potluri. 2018. GPUDirect Async: Exploring GPU synchronous communication techniques for InfiniBand clusters. J. Parallel Distributed Comput., Vol. 114 (2018), 28--45. https://doi.org/10.1016/j.jpdc.2017.12.007Google ScholarCross Ref
- Wei Bai, Shanim Sainul Abdeen, Ankit Agrawal, Krishan Kumar Attre, Paramvir Bahl, Ameya Bhagat, Gowri Bhaskara, Tanya Brokhman, Lei Cao, Ahmad Cheema, Rebecca Chow, Jeff Cohen, Mahmoud Elhaddad, Vivek Ette, Igal Figlin, Daniel Firestone, Mathew George, Ilya German, Lakhmeet Ghai, Eric Green, Albert G. Greenberg, Manish Gupta, Randy Haagens, Matthew Hendel, Ridwan Howlader, Neetha John, Julia Johnstone, Tom Jolly, Greg Kramer, David Kruse, Ankit Kumar, Erica Lan, Ivan Lee, Avi Levy, Marina Lipshteyn, Xin Liu, Chen Liu, Guohan Lu, Yuemin Lu, Xiakun Lu, Vadim Makhervaks, Ulad Malashanka, David A. Maltz, Ilias Marinos, Rohan Mehta, Sharda Murthi, Anup Namdhari, Aaron Ogus, Jitendra Padhye, Madhav Pandya, Douglas Phillips, Adrian Power, Suraj Puri, Shachar Raindel, Jordan Rhee, Anthony Russo, Maneesh Sah, Ali Sheriff, Chris Sparacino, Ashutosh Srivastava, Weixiang Sun, Nick Swanson, Fuhou Tian, Lukasz Tomczyk, Vamsi Vadlamuri, Alec Wolman, Ying Xie, Joyce Yom, Lihua Yuan, Yanzhao Zhang, and Brian Zill. 2023. Empowering Azure Storage with RDMA. In 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, Boston, MA, April 17--19, 2023, Mahesh Balakrishnan and Manya Ghobadi (Eds.). USENIX Association, 49--67. https://www.usenix.org/conference/nsdi23/presentation/baiGoogle Scholar
- Claude Barthels, Simon Loesing, Gustavo Alonso, and Donald Kossmann. 2015a. Rack-Scale In-Memory Join Processing using RDMA. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31 - June 4, 2015, Timos K. Sellis, Susan B. Davidson, and Zachary G. Ives (Eds.). ACM, 1463--1475. https://doi.org/10.1145/2723372.2750547Google ScholarDigital Library
- Claude Barthels, Simon Loesing, Gustavo Alonso, and Donald Kossmann. 2015b. Rack-Scale In-Memory Join Processing using RDMA. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31 - June 4, 2015, Timos K. Sellis, Susan B. Davidson, and Zachary G. Ives (Eds.). ACM, 1463--1475. https://doi.org/10.1145/2723372.2750547Google ScholarDigital Library
- Carsten Binnig, Andrew Crotty, Alex Galakatos, Tim Kraska, and Erfan Zamanian. 2016. The End of Slow Networks: It's Time for a Redesign. Proc. VLDB Endow., Vol. 9, 7 (2016), 528--539. https://doi.org/10.14778/2904483.2904485Google ScholarDigital Library
- Mihai Budiu and Chris Dodd. 2017. The P416 Programming Language. ACM SIGOPS Oper. Syst. Rev., Vol. 51, 1 (2017), 5--14. https://doi.org/10.1145/3139645.3139648Google ScholarDigital Library
- Xinyu Chen, Yao Chen, Ronak Bajaj, Jiong He, Bingsheng He, Weng-Fai Wong, and Deming Chen. 2020. Is FPGA Useful for Hash Joins?. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Amsterdam, The Netherlands, January 12--15, 2020, Online Proceedings. www.cidrdb.org. http://cidrdb.org/cidr2020/papers/p27-chen-cidr20.pdfGoogle Scholar
- Feras Daoud, Amir Wated, and Mark Silberstein. 2016. GPUrdma: GPU-side library for high performance networking from GPU kernels. In Proceedings of the 6th International Workshop on Runtime and Operating Systems for Supercomputers, Kyoto, Japan, June 1, 2016, Kamil Iskra and Torsten Hoefler (Eds.). ACM, 6:1--6:8. https://doi.org/10.1145/2931088.2931091Google ScholarDigital Library
- Aleksandar Dragojevic, Dushyanth Narayanan, Miguel Castro, and Orion Hodson. 2014. FaRM: Fast Remote Memory. In Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2014, Seattle, WA, USA, April 2--4, 2014, Ratul Mahajan and Ion Stoica (Eds.). USENIX Association, 401--414. https://www.usenix.org/conference/nsdi14/technical-sessions/dragojevi%C4%87Google Scholar
- Kayhan Dursun, Carsten Binnig, Ugur cC etintemel, Garret Swart, and Weiwei Gong. 2019. A Morsel-Driven Query Execution Engine for Heterogeneous Multi-Cores. Proc. VLDB Endow., Vol. 12, 12 (2019), 2218--2229. https://doi.org/10.14778/3352063.3352137Google ScholarDigital Library
- Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, and H. Peter Hofstee. 2020. In-memory database acceleration on FPGAs: a survey. VLDB J., Vol. 29, 1 (2020), 33--59. https://doi.org/10.1007/s00778-019-00581-wGoogle ScholarDigital Library
- William Gropp, Torsten Hoefler, Rajeev Thakur, and Ewing Lusk. 2014. Using advanced MPI: Modern features of the message-passing interface. MIT Press.Google ScholarDigital Library
- JoAnne Holliday, Divyakant Agrawal, and Amr El Abbadi. 1999. The Performance of Database Replication with Group Multicast. In Digest of Papers: FTCS-29, The Twenty-Ninth Annual International Symposium on Fault-Tolerant Computing, Madison, Wisconsin, USA, June 15--18, 1999. IEEE Computer Society, 158--165. https://doi.org/10.1109/FTCS.1999.781046Google ScholarCross Ref
- Intel. 2023. Intel P4 Studio. https://www.intel.com/content/www/us/en/products/details/network-io/intelligent-fabric-processors/p4-studio.html.Google Scholar
- Virajith Jalaparti, Peter Bod'i k, Ishai Menache, Sriram Rao, Konstantin Makarychev, and Matthew Caesar. 2015. Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, SIGCOMM 2015, London, United Kingdom, August 17--21, 2015, Steve Uhlig, Olaf Maennel, Brad Karp, and Jitendra Padhye (Eds.). ACM, 407--420. https://doi.org/10.1145/2785956.2787488Google ScholarDigital Library
- Matthias Jasny and Lasse Thostrup. 2023. Zerosided RDMA Code. https://github.com/DataManagementLab/zerosided_rdma.Google Scholar
- Matthias Jasny, Lasse Thostrup, Tobias Ziegler, and Carsten Binnig. 2022. P4DB - The Case for In-Network OLTP. In SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022, Zachary Ives, Angela Bonifati, and Amr El Abbadi (Eds.). ACM, 1375--1389. https://doi.org/10.1145/3514221.3517825Google ScholarDigital Library
- Anuj Kalia, Michael Kaminsky, and David G. Andersen. 2016. FaSST: Fast, Scalable and Simple Distributed Transactions with Two-Sided (RDMA) Datagram RPCs. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 185--201. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/kaliaGoogle ScholarDigital Library
- Anuj Kalia, Michael Kaminsky, and David G. Andersen. 2019. Datacenter RPCs can be General and Fast. In 16th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2019, Boston, MA, February 26--28, 2019, Jay R. Lorch and Minlan Yu (Eds.). USENIX Association, 1--16. https://www.usenix.org/conference/nsdi19/presentation/kaliaGoogle Scholar
- Bettina Kemme and Gustavo Alonso. 2010. Database Replication: a Tale of Research across Communities. Proc. VLDB Endow., Vol. 3, 1 (2010), 5--12. https://doi.org/10.14778/1920841.1920847Google ScholarDigital Library
- Dario Korolija, Timothy Roscoe, and Gustavo Alonso. 2020. Do OS abstractions make sense on FPGAs?. In 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4--6, 2020. USENIX Association, 991--1010. https://www.usenix.org/conference/osdi20/presentation/roscoeGoogle Scholar
- Robert Lasch, Mehdi Moghaddamfar, Norman May, Sü leyman Sirri Demirsoy, Christian F"a rber, and Kai-Uwe Sattler. 2022. Bandwidth-optimal Relational Joins on FPGAs. In Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022, Edinburgh, UK, March 29 - April 1, 2022, Julia Stoyanovich, Jens Teubner, Paolo Guagliardo, Milos Nikolic, Andreas Pieris, Jan Mü hlig, Fatma Ö zcan, Sebastian Schelter, H. V. Jagadish, and Meihui Zhang (Eds.). OpenProceedings.org, 1:27--1:39. https://doi.org/10.5441/002/edbt.2022.03Google ScholarCross Ref
- Long Hoang Le, Mojtaba Eslahi-Kelorazi, Paulo R. Coelho, and Fernando Pedone. 2021. RamCast: RDMA-based atomic multicast. In Middleware '21: 22nd International Middleware Conference, Qué bec City, Canada, December 6 - 10, 2021, Kaiwen Zhang, Abdelouahed Gherbi, Nalini Venkatasubramanian, and Lu'i s Veiga (Eds.). ACM, 172--184. https://doi.org/10.1145/3464298.3493393Google ScholarDigital Library
- Viktor Leis, Peter A. Boncz, Alfons Kemper, and Thomas Neumann. 2014. Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age. In International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22--27, 2014, Curtis E. Dyreson, Feifei Li, and M. Tamer Ö zsu (Eds.). ACM, 743--754. https://doi.org/10.1145/2588555.2610507Google ScholarDigital Library
- Feng Li, Sudipto Das, Manoj Syamala, and Vivek R. Narasayya. 2016a. Accelerating Relational Databases by Leveraging Remote Memory and RDMA. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016, Fatma Ö zcan, Georgia Koutrika, and Sam Madden (Eds.). ACM, 355--370. https://doi.org/10.1145/2882903.2882949Google ScholarDigital Library
- Jialin Li, Ellis Michael, Naveen Kr. Sharma, Adriana Szekeres, and Dan R. K. Ports. 2016b. Just Say NO to Paxos Overhead: Replacing Consensus with Network Ordering. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 467--483. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/liGoogle Scholar
- Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, and Volker Markl. 2020. Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects. In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19, 2020, David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 1633--1649. https://doi.org/10.1145/3318464.3389705Google ScholarDigital Library
- Mailinglist. 2023. [RFC 6/7] IB/core: Peer memory client for IO memory. https://www.spinics.net/lists/linux-rdma/msg33298.html.Google Scholar
- MPICH. 2023. Manpage: MPI_Alltoall. https://www.mpich.org/static/docs/latest/www3/MPI_Alltoall.html.Google Scholar
- APS Networks. 2021. Intel Tofino APS Networks BF2556X-1T-A1F. https://www.aps-networks.com/wp-content/uploads/2021/07/210712_APS_BF2556X-1T_V04.pdf.Google Scholar
- Joel Nider and Alexandra (Sasha) Fedorova. 2021. The last CPU. In HotOS '21: Workshop on Hot Topics in Operating Systems, Ann Arbor, Michigan, USA, June, 1--3, 2021, Sebastian Angel, Baris Kasikci, and Eddie Kohler (Eds.). ACM, 1--8. https://doi.org/10.1145/3458336.3465291Google ScholarDigital Library
- NVIDIA. 2023 a. HowTo Implement PeerDirect Client using MLNX_OFED. https://enterprise-support.nvidia.com/s/article/howto-implement-peerdirect-client-using-mlnx-ofed.Google Scholar
- NVIDIA. 2023 b. Nvidia NVSHMEM. https://developer.nvidia.com/nvshmem.Google Scholar
- NVIDIA. 2023 c. RDMA Over Converged Ethernet (RoCE). https://docs.nvidia.com/networking/m/view-rendered-page.action?abstractPageId=56986516.Google Scholar
- OpenUCX. 2023. SparkUCX ShuffleManager Plugin. https://github.com/openucx/sparkucx.Google Scholar
- Nadathur Satish, Changkyu Kim, Jatin Chhugani, Anthony D. Nguyen, Victor W. Lee, Daehyun Kim, and Pradeep Dubey. 2010. Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6--10, 2010, Ahmed K. Elmagarmid and Divyakant Agrawal (Eds.). ACM, 351--362. https://doi.org/10.1145/1807167.1807207Google ScholarDigital Library
- Amazon Web Services. 2023. Elastic Fabric Adapter. https://aws.amazon.com/hpc/efa/.Google Scholar
- Anil Shanbhag, Samuel Madden, and Xiangyao Yu. 2020. A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics. In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19, 2020, David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 1617--1632. https://doi.org/10.1145/3318464.3380595Google ScholarDigital Library
- Lasse Thostrup, Gloria Doci, Nils Boeschen, Manisha Luthra, and Carsten Binnig. 2023. Distributed GPU Joins on Fast RDMA-capable Networks. Proc. ACM Manag. Data, Vol. 1, 1 (2023), 29:1--29:26. https://doi.org/10.1145/3588709Google ScholarDigital Library
- Lasse Thostrup, Daniel Failing, Tobius Ziegler, and Carsten Binnig. 2022a. A DBMS-centric Evaluation of BlueField DPUs on Fast Networks. In International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@VLDB 2022, Sydney, Australia, September 5, 2022, Rajesh Bordawekar and Tirthankar Lahiri (Eds.). 1--10. http://www.adms-conf.org/2022-camera-ready/ADMS22_thostrup.pdfGoogle Scholar
- Lasse Thostrup, Jan Skrzypczak, Matthias Jasny, Tobias Ziegler, and Carsten Binnig. 2022b. DFI: The Data Flow Interface for High-Speed Networks. SIGMOD Rec., Vol. 51, 1 (2022), 15--22. https://doi.org/10.1145/3542700.3542705Google ScholarDigital Library
- Maroun Tork, Lina Maudlej, and Mark Silberstein. 2020. Lynx: A SmartNIC-driven Accelerator-centric Architecture for Network Servers. In ASPLOS '20: Architectural Support for Programming Languages and Operating Systems, Lausanne, Switzerland, March 16--20, 2020, James R. Larus, Luis Ceze, and Karin Strauss (Eds.). ACM, 117--131. https://doi.org/10.1145/3373376.3378528Google ScholarDigital Library
- Llu'i s Vilanova, Lina Maudlej, Shai Bergman, Till Miemietz, Matthias Hille, Nils Asmussen, Michael Roitzsch, Hermann H"a rtig, and Mark Silberstein. 2022. Slashing the disaggregation tax in heterogeneous data centers with FractOS. In EuroSys '22: Seventeenth European Conference on Computer Systems, Rennes, France, April 5 - 8, 2022, Yé rom-David Bromberg, Anne-Marie Kermarrec, and Christos Kozyrakis (Eds.). ACM, 352--367. https://doi.org/10.1145/3492321.3519569Google ScholarDigital Library
- Tobias Vincc on, Christian Knö dler, Leonardo Solis-Vasquez, Arthur Bernhardt, Sajjad Tamimi, Lukas Weber, Florian Stock, Andreas Koch, and Ilia Petrov. 2022. Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads. Proc. VLDB Endow., Vol. 15, 10 (2022), 1991--2004. https://www.vldb.org/pvldb/vol15/p1991-petrov.pdfGoogle ScholarDigital Library
- Cheng Wang, Jianyu Jiang, Xusheng Chen, Ning Yi, and Heming Cui. 2017. APUS: fast and scalable paxos on RDMA. In Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, September 24--27, 2017. ACM, 94--107. https://doi.org/10.1145/3127479.3128609Google ScholarDigital Library
- Zeke Wang, Hongjing Huang, Jie Zhang, Fei Wu, and Gustavo Alonso. 2022. FpgaNIC: An FPGA-based Versatile 100Gb SmartNIC for GPUs. In 2022 USENIX Annual Technical Conference, USENIX ATC 2022, Carlsbad, CA, USA, July 11--13, 2022, Jiri Schindler and Noa Zilberman (Eds.). USENIX Association, 967--986. https://www.usenix.org/conference/atc22/presentation/wang-zekeGoogle Scholar
- Xingda Wei, Jiaxin Shi, Yanzhe Chen, Rong Chen, and Haibo Chen. 2015. Fast in-memory transaction processing using RDMA and HTM. In Proceedings of the 25th Symposium on Operating Systems Principles, SOSP 2015, Monterey, CA, USA, October 4--7, 2015, Ethan L. Miller and Steven Hand (Eds.). ACM, 87--104. https://doi.org/10.1145/2815400.2815419Google ScholarDigital Library
- Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma, Khaled Elmeleegy, Scott Shenker, and Ion Stoica. 2010. Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In European Conference on Computer Systems, Proceedings of the 5th European conference on Computer systems, EuroSys 2010, Paris, France, April 13--16, 2010, Christine Morin and Gilles Muller (Eds.). ACM, 265--278. https://doi.org/10.1145/1755913.1755940Google ScholarDigital Library
- Erfan Zamanian, Carsten Binnig, Tim Kraska, and Tim Harris. 2017. The End of a Myth: Distributed Transaction Can Scale. Proc. VLDB Endow., Vol. 10, 6 (2017), 685--696. https://doi.org/10.14778/3055330.3055335Google ScholarDigital Library
- Erfan Zamanian, Julian Shun, Carsten Binnig, and Tim Kraska. 2020. Chiller: Contention-centric Transaction Execution and Data Partitioning for Modern Networks. In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19, 2020 (SIGMOD '20), David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 511--526. https://doi.org/10.1145/3318464.3389724Google ScholarDigital Library
- Tobias Ziegler, Carsten Binnig, and Viktor Leis. 2022. ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA. In SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022, Zachary Ives, Angela Bonifati, and Amr El Abbadi (Eds.). ACM, 685--699. https://doi.org/10.1145/3514221.3526187Google ScholarDigital Library
- Tobias Ziegler, Carsten Binnig, and Uwe Röhm. 2019a. Skew-resilient Query Processing for Fast Networks. In Datenbanksysteme für Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs, ,Datenbanken und Informationssysteme" (DBIS), 4.-8. M"a rz 2019, Rostock, Germany, Workshopband (LNI, Vol. P-290 ), Holger Meyer, Norbert Ritter, Andreas Thor, Daniela Nicklas, Andreas Heuer, and Meike Klettke (Eds.). Gesellschaft fü r Informatik, Bonn, 81--85. https://doi.org/10.18420/btw2019-ws-06Google ScholarCross Ref
- Tobias Ziegler, Viktor Leis, and Carsten Binnig. 2020. RDMA Communciation Patterns. Datenbank-Spektrum, Vol. 20, 3 (2020), 199--210. https://doi.org/10.1007/s13222-020-00355--7Google ScholarCross Ref
- Tobias Ziegler, Sumukha Tumkur Vani, Carsten Binnig, Rodrigo Fonseca, and Tim Kraska. 2019b. Designing Distributed Tree-based Index Structures for Fast RDMA-capable Networks. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019, Peter A. Boncz, Stefan Manegold, Anastasia Ailamaki, Amol Deshpande, and Tim Kraska (Eds.). ACM, 741--758. https://doi.org/10.1145/3299869.3300081Google ScholarDigital Library
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- Zero-sided RDMA: Network-driven Data Shuffling for Disaggregated Heterogeneous Cloud DBMSs
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