research-article

Robust Query Processing in Co-Processor-accelerated Databases

Authors:

Sebastian Breß,

Jens TeubnerAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 1891 - 1906

https://doi.org/10.1145/2882903.2882936

Published: 26 June 2016 Publication History

Abstract

Technology limitations are making the use of heterogeneous computing devices much more than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the only promising way to achieve application-speedups that users urgently need and expect. However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge.

In this paper, we identify two effects that limit performance in case co-processor resources become scarce. Cache thrashing occurs when the working set of queries does not fit into the co-processor's data cache, resulting in performance degradations up to a factor of 24. Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution by up to a factor of six.

We propose solutions for both effects. Data-driven operator placement avoids data movements when they might be harmful; query chopping limits co-processor memory usage and thus avoids contention. The combined approach-data-driven query chopping-achieves robust and scalable performance on co-processors. We validate our proposal with our open-source GPU-accelerated database engine CoGaDB and the popular star schema and TPC-H benchmarks.

References

[1]

CUDA C programming guide, CUDA version 6.5, 77--78. NVIDIA, 2014. http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf.

[2]

D. J. Abadi, S. R. Madden, and N. Hachem. Column-stores vs. row-stores: How different are they really? In SIGMOD, pages 967--980. ACM, 2008.

Digital Library

[3]

S. Arumugam, A. Dobra, C. M. Jermaine, N. Pansare, and L. Perez. The DataPath system: A data-centric analytic processing engine for large data warehouses. In SIGMOD, pages 519--530. ACM, 2010.

Digital Library

[4]

P. A. Boncz and M. L. Kersten. MIL primitives for querying a fragmented world. The VLDB Journal, 8(2):101--119, 1999.

Digital Library

[5]

P. A. Boncz, M. Zukowski, and N. Nes. MonetDB/X100: Hyper-pipelining query execution. In CIDR, pages 225--237, 2005.

[6]

S. Borkar and A. A. Chien. The future of microprocessors. Communications of the ACM, 54(5):67--77, 2011.

Digital Library

[7]

S. Breß, F. Beier, H. Rauhe, K.-U. Sattler, E. Schallehn, and G. Saake. Efficient co-processor utilization in database query processing. Information Systems, 38(8):1084--1096, 2013.

Digital Library

[8]

S. Breß, M. Heimel, M. Saecker, B. Kocher, V. Markl, and G. Saake. Ocelot/HyPE: Optimized data processing on heterogeneous hardware. PVLDB, 7(13):1609--1612, 2014.

Digital Library

[9]

S. Breß, N. Siegmund, M. Heimel, M. Saecker, T. Lauer, L. Bellatreche, and G. Saake. Load-aware inter-co-processor parallelism in database query processing. Data & Knowledge Engineering, 93(0):60--79, 2014.

Digital Library

[10]

L. Chen, X. Huo, and G. Agrawal. Accelerating mapreduce on a coupled cpu-gpu architecture. In SC, pages 25:1--25:11. IEEE, 2012.

Digital Library

[11]

C. Gregg and K. Hazelwood. Where is the data? why you cannot debate CPU vs. GPU performance without the answer. In ISPASS, pages 134--144. IEEE, 2011.

Digital Library

[12]

S. Harizopoulos, V. Shkapenyuk, and A. Ailamaki. QPipe: A simultaneously pipelined relational query engine. In SIGMOD, pages 383--394. ACM, 2005.

Digital Library

[13]

B. He, M. Lu, K. Yang, R. Fang, N. K. Govindaraju, Q. Luo, and P. V. Sander. Relational query co-processing on graphics processors. In ACM Trans. Database Syst., volume 34. ACM, 2009.

Digital Library

[14]

J. He, M. Lu, and B. He. Revisiting co-processing for hash joins on the coupled CPU-GPU architecture. Proc. VLDB Endow., 6(10):889--900, 2013.

Digital Library

[15]

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

Digital Library

[16]

S. Héman, N. Nes, M. Zukowski, and P. Boncz. Vectorized data processing on the Cell broadband engine. In DaMoN, pages 4:1--4:6. ACM, 2007.

Digital Library

[17]

S. Idreos, F. Groffen, N. Nes, S. Manegold, K. S. Mullender, and M. L. Kersten. MonetDB: Two decades of research in column-oriented database architectures. IEEE Data Eng. Bull., 35(1):40--45, 2012.

[18]

S. Jha, B. He, M. Lu, X. Cheng, and H. P. Huynh. Improving main memory hash joins on Intel Xeon Phi processors: An experimental approach. PVLDB, 8(6):642--653, 2015.

Digital Library

[19]

T. Karnagel et al. Demonstrating efficient query processing in heterogeneous environments. In SIGMOD, pages 693--696. ACM, 2014.

Digital Library

[20]

T. Karnagel, D. Habich, and W. Lehner. Local vs. global optimization: Operator placement strategies in heterogeneous environments. In DAPHNE, EDBT/ICDT Workshops, pages 48--55, 2015.

[21]

K. Krikellas, S. Viglas, and M. Cintra. Generating code for holistic query evaluation. In ICDE, pages 613--624. IEEE, 2010.

[22]

V. Leis, P. Boncz, A. Kemper, and T. Neumann. Morsel-driven parallelism: A NUMA-aware query evaluation framework for the many-core age. In SIGMOD, pages 743--754. ACM, 2014.

Digital Library

[23]

M. Lu, L. Zhang, H. P. Huynh, Z. Ong, Y. Liang, B. He, R. Goh, and R. Huynh. Optimizing the mapreduce framework on Intel Xeon Phi coprocessor. In Big Data, pages 125--130. IEEE, 2013.

[24]

T. Mostak. An overview of MapD (massively parallel database). White Paper, MIT, April 2013. http://geops.csail.mit.edu/docs/mapd_overview.pdf.

[25]

R. Mueller, J. Teubner, and G. Alonso. Data processing on FPGAs. PVLDB, 2(1):910--921, 2009.

Digital Library

[26]

T. Mühlbauer, W. Rödiger, R. Seilbeck, A. Kemper, and T. Neumann. Heterogeneity-conscious parallel query execution: Getting a better mileage while driving faster! In DaMoN, pages 2:1--2:10. ACM, 2014.

Digital Library

[27]

T. Neumann. Efficiently compiling efficient query plans for modern hardware. PVLDB, 4(9):539--550, 2011.

Digital Library

[28]

P. O'Neil, E. J. O'Neil, and X. Chen. The star schema benchmark (SSB), 2009. Revision 3, http://www.cs.umb.edu/poneil/StarSchemaB.pdf.

[29]

I. Pandis, R. Johnson, N. Hardavellas, and A. Ailamaki. Data-oriented transaction execution. PVLDB, 3(1--2):928--939, 2010.

Digital Library

[30]

H. Pirk, S. Manegold, and M. Kersten. Waste not... efficient co-processing of relational data. In ICDE. IEEE, 2014.

[31]

H. Pirk, T. Sellam, S. Manegold, and M. Kersten. X-Device Query Processing by Bitwise Distribution. In DaMoN, pages 48--54. ACM, 2012.

Digital Library

[32]

I. Psaroudakis, M. Athanassoulis, and A. Ailamaki. Sharing data and work across concurrent analytical queries. PVLDB, 6(9):637--648, 2013.

Digital Library

[33]

I. Psaroudakis, T. Scheuer, N. May, and A. Ailamaki. Task scheduling for highly concurrent analytical and transactional main-memory workloads. In ADMS, pages 36--45. VLDB Endowment, 2013.

[34]

J. Sanders and E. Kandrot. CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional, 1st edition, 2010.

Digital Library

[35]

K. Wang, K. Zhang, Y. Yuan, S. Ma, R. Lee, X. Ding, and X. Zhang. Concurrent analytical query processing with GPUs. PVLDB, 7(11):1011--1022, 2014.

Digital Library

[36]

H. Wu, G. Diamos, T. Sheard, M. Aref, S. Baxter, M. Garland, and S. Yalamanchili. Red Fox: An execution environment for relational query processing on GPUs. In CGO, pages 44:44--44:54. ACM, 2014.

Digital Library

[37]

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

Digital Library

[38]

S. Zhang et al. OmniDB: Towards portable and efficient query processing on parallel CPU/GPU architectures. PVLDB, 6(12):1374--1377, 2013.

Digital Library

Cited By

Kroviakov AKurapov PAnneser CGiceva J(2024)Heterogeneous Intra-Pipeline Device-Parallel AggregationsProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663441(1-10)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3662010.3663441
Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654694
Berthold ASchmidt LObersteiner AHabich DLehner WSchirmeier H(2024)On-The-Fly Data Distribution to Accelerate Query Processing in Heterogeneous Memory SystemsAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_12(170-183)Online publication date: 1-Sep-2024
https://doi.org/10.1007/978-3-031-70626-4_12
Show More Cited By

Index Terms

Robust Query Processing in Co-Processor-accelerated Databases
1. Information systems
  1. Data management systems
    1. Database management system engines

Recommendations

Efficient Query Processing on Many-core Architectures: A Case Study with Intel Xeon Phi Processor
SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Recently, Intel Xeon Phi is emerging as a many-core processor with up to 61 x86 cores. In this demonstration, we present PhiDB, an OLAP query processor with simultaneous multi-threading (SMT) capabilities on Xeon Phi as a case study for parallel ...
Load-aware inter-co-processor parallelism in database query processing

For a decade, the database community has been exploring graphics processing units and other co-processors to accelerate query processing. While the developed algorithms often outperform their CPU counterparts, it is not beneficial to keep processing ...
Preliminary experiences with the uintah framework on Intel Xeon Phi and stampede
XSEDE '13: Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery

In this work, we describe our preliminary experiences on the Stampede system in the context of the Uintah Computational Framework. Uintah was developed to provide an environment for solving a broad class of fluid-structure interaction problems on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

European Union's Horizon2020 Research & Innovation Program
Deutsche Forschungsgemeinschaft

Conference

SIGMOD/PODS'16

Sponsor:

SIGMOD

SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

Acceptance Rates

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
807
Total Downloads

Downloads (Last 12 months)65
Downloads (Last 6 weeks)5

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kroviakov AKurapov PAnneser CGiceva J(2024)Heterogeneous Intra-Pipeline Device-Parallel AggregationsProceedings of the 20th International Workshop on Data Management on New Hardware10.1145/3662010.3663441(1-10)Online publication date: 10-Jun-2024
Habich DPietrzyk JBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIMDified Data Processing - Foundations, Abstraction, and Advanced TechniquesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654694(613-621)Online publication date: 9-Jun-2024
Berthold ASchmidt LObersteiner AHabich DLehner WSchirmeier H(2024)On-The-Fly Data Distribution to Accelerate Query Processing in Heterogeneous Memory SystemsAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_12(170-183)Online publication date: 1-Sep-2024
Thostrup LDoci GBoeschen NLuthra MBinnig C(2023)Distributed GPU Joins on Fast RDMA-capable NetworksProceedings of the ACM on Management of Data10.1145/35887091:1(1-26)Online publication date: 30-May-2023
Gurumurthy BBroneske DSchäler MPionteck TSaake G(2023)Novel insights on atomic synchronization for sort-based group-by on GPUsDistributed and Parallel Databases10.1007/s10619-023-07424-241:3(387-409)Online publication date: 24-Apr-2023
Yogatama BGong WYu X(2022)Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMSProceedings of the VLDB Endowment10.14778/3551793.355180915:11(2491-2503)Online publication date: 29-Sep-2022
Lutz CBreß SZeuch SRabl TMarkl VIves ZBonifati AEl Abbadi A(2022)Triton Join: Efficiently Scaling to a Large Join State on GPUs with Fast InterconnectsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517911(1017-1032)Online publication date: 10-Jun-2022
Ailamaki ALiarou ETozun PPorobic DPsaroudakis I(2022)Databases on Modern HardwareundefinedOnline publication date: 25-Feb-2022
Beena Gopalakrishnan LBecher AWildermann SMeyer-Wegener KTeich J(2021)Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing SystemDatenbank-Spektrum10.1007/s13222-020-00363-721:1(55-64)Online publication date: 4-Jan-2021
Lai ZSun XLuo QXie X(2021)Accelerating multi-way joins on the GPUThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00708-y31:3(529-553)Online publication date: 2-Nov-2021
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents