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Integration of FPGAs in Database Management Systems: Challenges and Opportunities

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Abstract

In the presence of exponential growth of the data produced every day in volume, velocity, and variety, online analytical processing (OLAP) is becoming increasingly challenging. FPGAs offer hardware reconfiguration to enable query-specific pipelined and parallel data processing with the potential of maximizing throughput, speedup as well as energy and resource efficiency. However, dynamically configuring hardware accelerators to match a given OLAP query is a complex task. Furthermore, resource limitations restrict the coverage of OLAP operators. As a consequence, query optimization through partitioning the processing onto components of heterogeneous hardware/software systems seems a promising direction. While there exists work on operator placement for heterogeneous systems, it mainly targets systems combining multi-core CPUs with GPUs. However, an inclusion of FPGAs, which uniquely offer efficient and high-throughput pipelined processing at the expense of potential reconfiguration overheads, is still an open problem. We postulate that this challenge can only be met in a scalable fashion when providing a cooperative optimization between global and FPGA-specific optimizers. We demonstrate how this is addressed in two current research projects on FPGA-based query processing.

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References

  1. Barthels C, Alonso G, Hoefler T, Schneider T, Müller I (2017) Distributed join algorithms on thousands of cores. Proceedings VLDB Endowment 10(5):517–528

    Article  Google Scholar 

  2. Becher A, Ziener D, Meyer-Wegener K, Teich J (2015) A co-design approach for accelerated SQL query processing via FPGA-based data filtering. In: FPT, pp 192–195

    Google Scholar 

  3. Becher A, Pirkl J, Herrmann A, Teich J, Wildermann S (2016a) Hybrid energy-aware reconfiguration management on Xilinx Zynq SoCs. In: ReConFig, pp 1–7

    Google Scholar 

  4. Becher A, Wildermann S, Mühlenthaler M, Teich J (2016b) Reorder: Runtime datapath generation for high-throughput multi-stream processing. In: ReConFig, pp 1–8

    Google Scholar 

  5. Becher A, Wildermann S, Teich J (2018) Optimistic regular expression matching on FPGAs for near-data processing. Proc. 14th Int. Workshop on Data Management on New Hardware, Houston, 11.6.2018, pp 1–4

    Google Scholar 

  6. Breß S (2013) Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS. Proceedings VLDB Endowment 6(12):1398–1403

    Article  Google Scholar 

  7. Breß S (2014) The design and implementation of CoGaDB: a column-oriented GPU-accelerated DBMS. Datenbank Spektrum 14(3):199–209. https://doi.org/10.1007/s13222-014-0164-z

    Article  Google Scholar 

  8. Breß S, Beier F, Rauhe H, Sattler KU, Schallehn E, Saake G (2013) Efficient co-processor utilization in database query processing. Inf Syst 38(8):1084–1096. https://doi.org/10.1016/j.is.2013.05.004

    Article  Google Scholar 

  9. Breß S, Funke H, Teubner J (2016) Robust query processing in co-processor-accelerated databases. In: SIGMOD, pp 1891–1906 https://doi.org/10.1145/2882903.2882936

    Chapter  Google Scholar 

  10. Breß S, Köcher B, Funke H, Rabl T, Markl V (2018) Generating custom code for efficient query execution on heterogeneous processors. VLDB J. https://doi.org/10.1007/s00778-018-0512-y

    Article  Google Scholar 

  11. Casper J, Olukotun K (2014) Hardware acceleration of database operations. In: FPGA, pp 151–160

    Google Scholar 

  12. Funke H, Breß S, Noll S, Markl V, Teubner J (2018) Pipelined query processing in coprocessor environments. In: SIGMOD, pp 1603–1618 https://doi.org/10.1145/3183713.3183734

    Chapter  Google Scholar 

  13. Halstead RJ, Sukhwani B, Min H, Thoennes M, Dube P, Asaad SW, Iyer B (2013) Accelerating join operation for relational databases with FPGAs. In: FCCM, pp 17–20

    Google Scholar 

  14. Hampel V, Pionteck T, Maehle E (2012) An approach for performance estimation of hybrid systems with FPGAs and GPUs as coprocessors. In: ARCS, pp 160–171

    Google Scholar 

  15. He B, Lu M, Yang K, Fang R, Govindaraju NK, Luo Q, Sander PV (2009) Relational query coprocessing on graphics processors. ACM Trans Database Syst 34(4):Art. No 21. https://doi.org/10.1145/1620585.1620588

    Article  Google Scholar 

  16. Heimel M, Saecker M, Pirk H, Manegold S, Markl V (2013) Hardware-oblivious parallelism for in-memory column-stores. Proceedings VLDB Endowment 6(9):709–720

    Article  Google Scholar 

  17. Hemsoth N (2016) Baidu takes FPGA approach to accelerating SQL at scale. The next platform. https://www.nextplatform.com/2016/08/24/baidu-takes-fpga-approach-accelerating-big-sql/. Accessed 24 Aug 2018

    Google Scholar 

  18. István Z, Sidler D, Alonso G (2016) Runtime parameterizable regular expression operators for databases. In: FCCM, pp 204–211

    Google Scholar 

  19. Kaldewey T, Lohman GM, Müller R, Volk PB (2012) GPU join processing revisited. In: DaMoN, pp 55–62

    Chapter  Google Scholar 

  20. Kara K, Giceva J, Alonso G (2017) FPGA-based data partitioning. In: SIGMOD, pp 433–445

    Google Scholar 

  21. Karnagel T, Habich D, Schlegel B, Lehner W (2014) Heterogeneity-aware operator placement in column-store DBMS. Datenbank Spektrum 14(3):211–221. https://doi.org/10.1007/s13222-014-0167-9

    Article  Google Scholar 

  22. Karnagel T, Habich D, Lehner W (2015) Local vs. global optimization: operator placement strategies in heterogeneous environments. In: Proceedings of the Workshops of the EDBT/ICDT 2015 Joint Conference, Brussels, Belgium, 27 March 2015, pp 48–55

  23. Karnagel T, Habich D, Lehner W (2017) Adaptive work placement for query processing on heterogeneous computing resources. Proceedings VLDB Endowment 10(7):733–744

    Article  Google Scholar 

  24. Kim C, Sedlar E, Chhugani J, Kaldewey T, Nguyen AD, Blas AD, Lee VW, Satish N, Dubey P (2009) Sort vs. hash revisited: fast join implementation on modern multi-core CPUs. Proceedings VLDB Endowment 2(2):1378–1389

    Article  Google Scholar 

  25. Kim C, Chhugani J, Satish N, Sedlar E, Nguyen AD, Kaldewey T, Lee VW, Brandt SA, Dubey P (2010) FAST: fast architecture sensitive tree search on modern CPUs and GPUs. In: SIGMOD, pp 339–350

    Google Scholar 

  26. Koch D, Tørresen J (2011) FPGASort: a high performance sorting architecture exploiting run-time reconfiguration on FPGAs for large problem sorting. In: FPGA, pp 45–54

    Google Scholar 

  27. Lang W, Kandhan R, Patel JM (2011) Rethinking query processing for energy efficiency: slowing down to win the race. IEEE Data Eng Bull 34(1):12–23

    Google Scholar 

  28. Manegold S, Boncz P, Kersten ML (2002) Generic database cost models for hierarchical memory systems. In: Proceedings VLDB Endowment  pp 191–202

    Google Scholar 

  29. Müller R, Teubner J (2009) FPGA: what’s in it for a database? In: SIGMOD, pp 999–1004

    Google Scholar 

  30. Müller R, Teubner J, Alonso G (2009) Streams on wires – A query compiler for FPGAs. Proceedings VLDB Endowment 2(1):229–240

    Article  Google Scholar 

  31. Müller R, Teubner J, Alonso G (2010) Glacier: a query-to-hardware compiler. In: SIGMOD, pp 1159–1162

    Google Scholar 

  32. Müller R, Teubner J, Alonso G (2012) Sorting networks on FPGAs. VLDB J 21(1):1–23.  http://dx.doi.org/10.1007/s00778-011-0232-z

    Article  Google Scholar 

  33. Neumann T, Leis V (2014) Compiling database queries into machine code. IEEE Data Eng Bull 37(1):3–11

    Google Scholar 

  34. Owaida M, Sidler D, Kara K, Alonso G (2017) Centaur: a framework for hybrid CPU-FPGA databases. In: FCCM, pp 211–218

    Google Scholar 

  35. Pirk H, Moll O, Zaharia M, Madden S (2016) Voodoo – a vector algebra for portable database performance on modern hardware. Proceedings VLDB Endowment 9(14):1707–1718

    Article  Google Scholar 

  36. Pohl C (2017) Exploiting manycore architectures for parallel data stream processing. In: GvDB, pp 66–71 (http://ceur-ws.org/Vol-1858/paper13.pdf)

    Google Scholar 

  37. Polychroniou O, Raghavan A, Ross KA (2015) Rethinking SIMD vectorization for in-memory databases. In: SIGMOD, pp 1493–1508

    Google Scholar 

  38. Putnam A, Caulfield A, Chung E, Chiou D, Constantinides K, Demme J, Esmaeilzadeh H, Fowers J, Gray J, Haselman M, Hauck S, Heil S, Hormati A, Kim JY, Lanka S, Peterson E, Smith A, Thong J, Xiao PY, Burger D, Larus J, Gopal GP, Pope S (2014) A reconfigurable fabric for accelerating large-scale datacenter services. In: ISCA, pp 13–24

    Google Scholar 

  39. Raducanu B, Boncz PA, Zukowski M (2013) Micro adaptivity in vectorwise. In: SIGMOD, pp 1231–1242 https://doi.org/10.1145/2463676.2465292

    Chapter  Google Scholar 

  40. Rosenfeld V, Heimel M, Viebig C, Markl V (2015) The operator variant selection problem on heterogeneous hardware. In: ADMS, pp 1–12

    Google Scholar 

  41. Sidhu RPS, Prasanna VK (2001) Fast regular expression matching using FPGAs. In: FCCM, pp 227–238

    Google Scholar 

  42. Sidler D, István Z, Owaida M, Alonso G (2017) Accelerating pattern matching queries in hybrid CPU-FPGA architectures. In: SIGMOD, pp 403–415

    Chapter  Google Scholar 

  43. Sitaridi E, Ross K (2013) Optimizing select conditions on GPUs. In: DaMoN, ACM. pp, vol 4, pp 1–4

    Google Scholar 

  44. Sukhwani B, Thoennes M, Min H, Dube P, Brezzo B, Asaad SW, Dillenberger D (2013) Large payload streaming database sort and projection on FPGAs. In: SBAC-PAD, pp 25–32

    Google Scholar 

  45. Sukhwani B, Thoennes M, Min H, Dube P, Brezzo B, Asaad SW, Dillenberger D (2015) A hardware/software approach for database query acceleration with FPGAs. Int J Parallel Program 43(6):1129–1159

    Article  Google Scholar 

  46. Ueda T, Ito M, Ohara M (2015) A dynamically reconfigurable equi-joiner on FPGA. IBM Technical Report RT0969

    Google Scholar 

  47. Ungethüm A, Habich D, Karnagel T, Lehner W, Asmussen N, Völp M, Noethen B, Fettweis GP (2015) Query processing on low-energy many-core processors. In: ICDE, pp 155–160

    Chapter  Google Scholar 

  48. Wang Z, Paul J, Cheah HY, He B, Zhang W (2016) Relational query processing on OpenCL-based FPGAs. In: FPL, pp 1–10

    Google Scholar 

  49. Xu Z, Tu Y, Wang X (2012) PET: reducing database energy cost via query optimization. Proceedings VLDB Endowment 5(12):1954–1957

    Article  Google Scholar 

  50. Yuan Y, Lee R, Zhang X (2013) The yin and yang of processing data warehousing queries on GPU devices. Proceedings VLDB Endowment 6(10):817–828

    Article  Google Scholar 

  51. Zhang S, He J, He B, Lu M (2013) OmniDB: Towards portable and efficient query processing on parallel CPU/GPU architectures. Proceedings VLDB Endowment 6(12):1374–1377

    Article  Google Scholar 

  52. Ziener D, Bauer F, Becher A, Dennl C, Meyer-Wegener K, Schürfeld U, Teich J, Vogt J, Weber H (2016) FPGA-based dynamically reconfigurable SQL query processing. ACM Trans Reconfigurable Technol Syst 9(4):Article No. 25

    Article  Google Scholar 

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Authors and Affiliations

  1. Friedrich-Alexander-Universität Erlangen Nürnberg, Nuremberg, Germany

    Andreas Becher, Lekshmi B.G., Klaus Meyer-Wegener, Jürgen Teich & Stefan Wildermann

  2. Otto-von-Guericke-University, Magdeburg, Germany

    David Broneske, Tobias Drewes, Bala Gurumurthy, Thilo Pionteck & Gunter Saake

Authors
  1. Andreas Becher
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  2. Lekshmi B.G.
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  6. Klaus Meyer-Wegener
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  9. Jürgen Teich
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  10. Stefan Wildermann
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Correspondence to Andreas Becher.

Additional information

This work has been supported by the German Research Foundation (DFG) as part of the Priority Programm 2037 (Grant No.: ME 943/9, PI447/9, SA 465/51, TE163/21, WI4415/1).

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Becher, A., B.G., L., Broneske, D. et al. Integration of FPGAs in Database Management Systems: Challenges and Opportunities. Datenbank Spektrum 18, 145–156 (2018). https://doi.org/10.1007/s13222-018-0294-9

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