Introduction

HyPE is a library build for automatic selection of processing units for co-processing in database systems. The long-term goal of the project is to implement a fully fledged query processing engine, which is able to automatically generate and optimize a hybrid CPU/GPU physical query plan from a logical query plan. It is a research prototype developed by the Otto-von-Guericke University Magdeburg in collaboration with Ilmenau University of Technology.

See the publications listed below for more details.

Features

Currently, HyPE supports the following features:

Entirely written in C++
Decides on the (likely) optimal algorithm w.r.t. to a user specified optimization criterion for an operation
Unrestricted use in parallel applications due to thread-safety
Easily extensible by utilizing a plug-in architecture
New: Runs under Linux and Windows
New: Supports the following compilers: g++ (>=4.5), clang, and Visual C++
Requires (almost) no knowledge about executed algorithms, just the relevant features of the datasets for an algorithms execution time
Collects statistical information to help the user to fine tune HyPE's parameters for their use case
Provides a parallel execution engine for operators

Detailed Documentation

Project Members

Project Members:

Sebastian Breß (University of Magdeburg)
Klaus Baumann (University of Magdeburg)
Robin Haberkorn (University of Magdeburg)
Steven Ladewig (University of Magdeburg)
Tobias Lauer (Jedox AG)
Gunter Saake (University of Magdeburg)
Norbert Siegmund (University of Passau)

Project Partners:

Felix Beier (Ilmenau University of Technology)
Ladjel Bellatreche (LIAS/ISEA-ENSMA, Futuroscope, France)
Hannes Rauhe (Ilmenau University of Technology)
Kai-Uwe Sattler (Ilmenau University of Technology)

Former Project Members:

Ingolf Geist (University of Magdeburg)

Publications

Sebastian Breß. Ein selbstlernendes Entscheidungsmodell für die Verteilung von Datenbankoperationen auf CPU/GPU-Systemen. Master thesis, University of Magdeburg, Germany, March 2012. In German.
Sebastian Breß, Siba Mohammad, and Eike Schallehn. Self-Tuning Distribution of DB-Operations on Hybrid CPU/GPU Platforms. In Proceedings of the 24st Workshop Grundlagen von Datenbanken (GvD), pages 89–94. CEUR-WS, 2012.
Sebastian Breß, Eike Schallehn, and Ingolf Geist. Towards Optimization of Hybrid CPU/GPU Query Plans in Database Systems. In Second ADBIS workshop on GPUs In Databases (GID), pages 27–35. Springer, 2012.
Sebastian Breß, Felix Beier, Hannes Rauhe, Eike Schallehn, Kai-Uwe Sattler, and Gunter Saake. Automatic Selection of Processing Units for Coprocessing in Databases. In 16th East-European Conference on Advances in Databases and Information Systems (ADBIS), pages 57–70. Springer, 2012.
Sebastian Breß, Ingolf Geist, Eike Schallehn, Maik Mory, and Gunter Saake. A Framework for Cost based Optimization of Hybrid CPU/GPU Query Plans in Database Systems. Control and Cybernetics, 41(4):715–742, 2012.
Sebastian Breß, Felix Beier, Hannes Rauhe, Kai-Uwe Sattler, Eike Schallehn, and Gunter Saake. Efficient Co-Processor Utilization in Database Query Processing. Information Systems, 38(8):1084–1096, 2013. http://dx.doi.org/10.1016/j.is.2013.05.004.
Sebastian Breß. Why it is Time for a HyPE: A Hybrid Query Processing Engine for Efficient GPU Coprocessing in DBMS. In The VLDB PhD workshop. VLDB Endowment, 2013.
Sebastian Breß, Norbert Siegmund, Ladjel Bellatreche, and Gunter Saake. An Operator-Stream-based Scheduling Engine for Effective GPU Coprocessing. In 17th East-European Conference on Advances in Databases and Information Systems (ADBIS), pages 288–301. Springer, 2013.
Sebastian Breß, Max Heimel, Norbert Siegmund, Ladjel Bellatreche, and Gunter Saake. Exploring the Design Space of a GPU-aware Database Architecture. In ADBIS workshop on GPUs In Databases (GID), pages 225–234. Springer, 2013.