HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
Modeling GPU-CPU workloads and systems
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Accelerating SQL database operations on a GPU with CUDA
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Exploring graphics processing units as parallel coprocessors for online aggregation
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
CUDA by Example: An Introduction to General-Purpose GPU Programming
CUDA by Example: An Introduction to General-Purpose GPU Programming
Comparing GPU and CPU in OLAP cubes creation
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures
Concurrency and Computation: Practice & Experience - Euro-Par 2009
High-throughput transaction executions on graphics processors
Proceedings of the VLDB Endowment
Parallel k-Nearest Neighbor Search on Graphics Hardware
PAAP '10 Proceedings of the 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
X-device query processing by bitwise distribution
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
GiST scan acceleration using coprocessors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
Automatic selection of processing units for coprocessing in databases
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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GPU acceleration is a promising approach to speed up query processing of database systems by using low cost graphic processors as coprocessors. Two major trends have emerged in this area: (1) The development of frameworks for scheduling tasks in heterogeneous CPU/GPU platforms, which is mainly in the context of coprocessing for applications and does not consider specifics of database-query processing and optimization. (2) The acceleration of database operations using efficient GPU algorithms, which typically cannot be applied easily on other database systems, because of their analytical-algorithm-specific cost models. One major challenge is how to combine traditional database query processing with GPU coprocessing techniques and efficient database operation scheduling in a GPU-aware query optimizer. In this thesis, we develop a hybrid query processing engine, which extends the traditional physical optimization process to generate hybrid query plans and to perform a cost-based optimization in a way that the advantages of CPUs and GPUs are combined. Furthermore, we aim at a portable solution between different GPU-accelerated database management systems to maximize applicability. Preliminary results indicate great potential.