Abstract
The vast amount of processing power and memory bandwidth provided by modern graphics cards make them an interesting platform for data-intensive applications. Unsurprisingly, the database research community has identified GPUs as effective co-processors for data processing several years ago. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this paper, we summarize the major findings of the literature on GPU-accelerated data processing. Based on this survey, we present key properties, important trade-offs and typical challenges of GPU-aware database architectures, and identify major open research questions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Palo gpu accelerator. White Paper (2010)
Bakkum, P., Chakradhar, S.: Efficient data management for gpu databases (2012), http://pbbakkum.com/virginian/paper.pdf
Bakkum, P., Skadron, K.: Accelerating sql database operations on a gpu with cuda. In: GPGPU, pp. 94–103. ACM (2010)
Boncz, P.A., Kersten, M.L., Manegold, S.: Breaking the memory wall in monetdb. Commun. ACM 51(12), 77–85 (2008)
Breß, S., Beier, F., Rauhe, H., Sattler, K.-U., Schallehn, E., Saake, G.: Efficient co-processor utilization in database query processing. Information Systems (2013), http://dx.doi.org/10.1016/j.is.2013.05.004
Breß, S., Geist, I., Schallehn, E., Mory, M., Saake, G.: A framework for cost based optimization of hybrid cpu/gpu query plans in database systems. Control and Cybernetics 41(4) (2012)
Breß, S., Mohammad, S., Schallehn, E.: Self-tuning distribution of db-operations on hybrid cpu/gpu platforms. In: GvD. CEUR-WS, pp. 89–94 (2012)
Breß, S., Schallehn, E., Geist, I.: Towards optimization of hybrid CPU/GPU query plans in database systems. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases & Inform. AISC, vol. 185, pp. 27–35. Springer, Heidelberg (2012)
Diamos, G., Wu, H., Lele, A., Wang, J., Yalamanchili, S.: Efficient relational algebra algorithms and data structures for gpu. Technical report, Center for Experimental Research in Computer Systems, CERS (2012)
Fang, R., He, B., Lu, M., Yang, K., Govindaraju, N.K., Luo, Q., Sander, P.V.: Gpuqp: query co-processing using graphics processors. In: SIGMOD, pp. 1061–1063. ACM (2007)
Ghodsnia, P.: An in-gpu-memory column-oriented database for processing analytical workloads. In: The VLDB PhD Workshop. VLDB Endowment (2012)
Graefe, G.: Encapsulation of parallelism in the volcano query processing system. In: SIGMOD, pp. 102–111. ACM (1990)
Gregg, C., Hazelwood, K.: Where is the data? why you cannot debate cpu vs. gpu performance without the answer. In: ISPASS, pp. 134–144. IEEE (2011)
He, B., Lu, M., Yang, K., Fang, R., Govindaraju, N.K., Luo, Q., Sander, P.V.: Relational query co-processing on graphics processors. ACM Trans. Database Syst. 34, 21:1–21:39 (2009)
He, B., Yang, K., Fang, R., Lu, M., Govindaraju, N., Luo, Q., Sander, P.: Relational joins on graphics processors. In: SIGMOD, pp. 511–524. ACM (2008)
He, B., Yu, J.X.: High-throughput transaction executions on graphics processors. PVLDB 4(5), 314–325 (2011)
Heimel, M., Markl, V.: A first step towards gpu-assisted query optimization. In: ADMS. VLDB Endowment (2012)
Heimel, M., Saecker, M., Pirk, H., Manegold, S., Markl, V.: Hardware-oblivious parallelism for in-memory column-stores. In: VLDB. VLDB Endowment (2013)
Kossmann, D.: The state of the art in distributed query processing. ACM Computing Surveys 32(4), 422–469 (2000)
Manegold, S., Boncz, P.A., Kersten, M.L.: Optimizing database architecture for the new bottleneck: Memory access. The VLDB Journal 9(3), 231–246 (2000)
Manegold, S., Kersten, M.L., Boncz, P.: Database architecture evolution: Mammals flourished long before dinosaurs became extinct. PVLDB 2(2), 1648–1653 (2009)
NVIDIA. Nvidia cuda c programming guide, pp. 30–34 (2012), http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf (accessed February 16, 2013)
Pirk, H.: Efficient cross-device query processing. In: The VLDB PhD Workshop. VLDB Endowment (2012)
Pirk, H., Manegold, S., Kersten, M.: Accelerating foreign-key joins using asymmetric memory channels. In: ADMS, pp. 585–597. VLDB Endowment (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Breß, S., Heimel, M., Siegmund, N., Bellatreche, L., Saake, G. (2014). Exploring the Design Space of a GPU-Aware Database Architecture. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-01863-8_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01862-1
Online ISBN: 978-3-319-01863-8
eBook Packages: EngineeringEngineering (R0)