In situ Statistics Generation within partially reconfigurable Hardware Accelerators for Query Processing

Becher A, Teich J (2019)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Event location: Amsterdam NL

DOI: 10.1145/3329785.3329936

Abstract

Hardware Accelerators for Query Processing are often optimized to filter as much data as possible before the results are stored to memory or sent to the user.
However, the static nature of such optimized accelerators limits the amount of operators they can implement to a fixed amount configured at synthesis time.
If not all operators can be pushed down to the accelerator, the decision which operators should be pushed down has therefore a big impact on the resulting data size and overall query execution time.
Statistics are therefore used to determine which operators to push onto the hardware accelerators.
Gathering these statistics is therefore of utmost importance.
In this paper we present multiple possibilities to gather statistics within an accelerator while executing a partial query.
These statistics can be gradually improved with every execution of the accelerator be of use in future queries during the query planning phase.

Authors with CRIS profile

Andreas Becher Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

Related research project(s)

None - None Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis (ReProVide) DFG-Schwerpunktprogramm (SPP) 2037 - Skalierbares Datenmanagement für zukünftige Hardware Aug. 28, 2017 - March 1, 2023

How to cite

APA:

Becher, A., & Teich, J. (2019). In situ Statistics Generation within partially reconfigurable Hardware Accelerators for Query Processing. In Proceedings of the 15th International Workshop on Data Management on New Hardware (DaMoN) Held with ACM SIGMOD/PODS 2019. Amsterdam, NL.

MLA:

Becher, Andreas, and Jürgen Teich. "In situ Statistics Generation within partially reconfigurable Hardware Accelerators for Query Processing." Proceedings of the 15th International Workshop on Data Management on New Hardware (DaMoN) Held with ACM SIGMOD/PODS 2019, Amsterdam 2019.

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