Tomas Karnagel
Wolfgang Lehner
Abstract
The hardware landscape is currently changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. This trend is a great opportunity for data-base systems to increase the overall performance if the heterogeneous resources can be utilized efficiently. To achieve this, the main challenge is to place the right work on the right computing unit. Current approaches tackling this placement for query processing assume that data cardinalities of intermediate results can be correctly estimated. However, this assumption does not hold for complex queries. To overcome this problem, we propose an adaptive placement approach being independent of cardinality estimation of intermediate results. Our approach is incorporated in a novel adaptive placement sequence. Additionally, we implement our approach as an extensible virtualization layer, to demonstrate the broad applicability with multiple database systems. In our evaluation, we clearly show that our approach significantly improves OLAP query processing on heterogeneous hardware, while being adaptive enough to react to changing cardinalities of intermediate query results.
- D. Abadi, P. Boncz, S. Harizopoulos, S. Idreos, and S. Madden. The Design and Implementation of Modern Column-Oriented Database Systems. In Foundations and Trends in Databases, volume 5, pages 197--280, 2013. Google Scholar
Digital Library
- J. Antony, P. P. Janes, and A. P. Rendell. Exploring Thread and Memory Placement on NUMA Architectures: Solaris and Linux, UltraSPARC/FirePlane and Opteron/Hypertransport. In Proceedings of HiPC, pages 338--352, 2006. Google ScholarDigital Library
- P. A. Boncz, M. L. Kersten, and S. Manegold. Breaking the Memory Wall in MonetDB. Communications ACM, 51(12):77--85, Dec. 2008. Google ScholarDigital Library
- P. A. Boncz, M. Zukowski, and N. Nes. MonetDB/X100: Hyper-Pipelining Query Execution. In CIDR:225--237, 2005.Google Scholar
- S. Breß. The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS. Datenbank-Spektrum, 14(3):199--209, 2014.Google ScholarCross Ref
- S. Breß and G. Saake. Why It is Time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS. Proc. VLDB Endow., 6(12):1398--1403, Aug. 2013. Google ScholarDigital Library
- A. Brinkmann, K. Salzwedel, and C. Scheideler. Compact, Adaptive Placement Schemes for Non-uniform Requirements. In Proceedings of SPAA, pages 53--62. ACM, 2002. Google ScholarDigital Library
- S. Christodoulakis. Implications of Certain Assumptions in Database Performance Evaluation. ACM Trans. Database Syst., 9(2):163--186, June 1984. Google ScholarDigital Library
- A. Deshpande, Z. Ives, and V. Raman. Adaptive Query Processing. Found. Trends databases, 1(1):1--140, Jan. 2007. Google ScholarDigital Library
- H. Esmaeilzadeh, E. Blem, R. St. Amant, K. Sankaralingam, and D. Burger. Dark silicon and the end of multicore scaling. In Proceedings of ISCA, pages 365--376. ACM, 2011. Google ScholarDigital Library
- B. He, M. Lu, K. Yang, R. Fang, N. K. Govindaraju, Q. Luo, and P. V. Sander. Relational Query Coprocessing on Graphics Processors. ACM Trans. Database Syst., 34(4):21:1--21:39, Dec. 2009. Google ScholarDigital Library
- B. He, K. Yang, R. Fang, M. Lu, N. Govindaraju, Q. Luo, and P. Sander. Relational Joins on Graphics Processors. In Proceedings of the 2008 ACM SIGMOD, SIGMOD '08, pages 511--524, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- J. He, S. Zhang, and B. He. In-cache Query Co-processing on Coupled CPU-GPU Architectures. Proc. VLDB Endow., 8(4):329--340, Dec. 2014. Google ScholarDigital Library
- M. Heimel, M. Saecker, H. Pirk, S. Manegold, and V. Markl. Hardware-oblivious Parallelism for In-memory Column-stores. Proc. VLDB Endow., 6(9):709--720, July 2013. Google ScholarDigital Library
- Y. E. Ioannidis and S. Christodoulakis. On the propagation of errors in the size of join results. In Proceedings of ACM SIGMOD, pages 268--277. ACM, 1991. Google ScholarDigital Library
- 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. Proc. VLDB Endow.:642--653, 2015. Google ScholarDigital Library
- T. Karnagel, D. Habich, and W. Lehner. Local vs. Global Optimization: Operator Placement Strategies in Heterogeneous Environments. In Proceedings of the Workshops of the EDBT/ICDT, pages 48--55, 2015.Google Scholar
- T. Karnagel, D. Habich, B. Schlegel, and W. Lehner. Heterogeneity-Aware Operator Placement in Column-Store DBMS. Datenbank-Spektrum, 14(3):211--221, 2014.Google ScholarCross Ref
- V. Leis, P. Boncz, A. Kemper, and T. Neumann. Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age. In Proceedings of the 2014 ACM SIGMOD, pages 743--754. ACM, 2014. Google ScholarDigital Library
- V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann. How Good Are Query Optimizers, Really? Proc. VLDB Endow., 9(3):204--215, Nov. 2015. Google ScholarDigital Library
- B. Lepers, V. Quéma, and A. Fedorova. Thread and Memory Placement on NUMA Systems: Asymmetry Matters. In Proceedings of the 2015 USENIX, pages 277--289, 2015. Google ScholarDigital Library
- S. Meraji, B. Schiefer, L. Pham, L. Chu, P. Kokosielis, A. Storm, W. Young, C. Ge, G. Ng, and K. Kanagaratnam. Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration. In Proceedings of SIGMOD, pages 1951--1960. ACM, 2016. Google ScholarDigital Library
- R. Mueller, J. Teubner, and G. Alonso. Data Processing on FPGAs. Proc. VLDB Endow., 2(1):910--921, Aug. 2009. Google ScholarDigital Library
- T. Neumann. Efficiently compiling efficient query plans for modern hardware. Proc. VLDB Endow., 4(9):539--550, 2011. Google ScholarDigital Library
- P. ONeil, E. ONeil, X. Chen, and S. Revilak. The star schema benchmark and augmented fact table indexing. In Technology Conference on Performance Evaluation and Benchmarking, pages 237--252. Springer, 2009. Google ScholarDigital Library
- K. Wang, K. Zhang, Y. Yuan, S. Ma, R. Lee, X. Ding, and X. Zhang. Concurrent Analytical Query Processing with GPUs. Proc. VLDB Endow., 7(11):1011--1022, July 2014. Google ScholarDigital Library
- Y.-P. You, H.-J. Wu, Y.-N. Tsai, and Y.-T. Chao. VirtCL: A Framework for OpenCL Device Abstraction and Management. In Proceedings of PPoPP, pages 161--172. ACM, 2015. Google ScholarDigital Library
- Y. Yuan, R. Lee, and X. Zhang. The Yin and Yang of Processing Data Warehousing Queries on GPU Devices. Proc. VLDB Endow., 6(10):817--828, Aug. 2013. Google ScholarDigital Library
Index Terms
- Adaptive work placement for query processing on heterogeneous computing resources
Recommendations
View-based query processing: On the relationship between rewriting, answering and losslessness
As a result of the extensive research in view-based query processing, three notions have been identified as fundamental, namely rewriting, answering, and losslessness. Answering amounts to computing the tuples satisfying the query in all databases ...
Adaptive Multi-join Query Processing in PDBMS
ICDE '09: Proceedings of the 2009 IEEE International Conference on Data EngineeringTraditionally, distributed databases assume that the small) set of nodes participating in a query is known apriori, the data is well placed, and the statistics are readily available. However, these assumptions are no longer valid in a Peer-based ...
Comments