Database Management Systems
Database Architecture Optimized for the New Bottleneck: Memory Access
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Laws of Software Evolution Revisited
EWSPT '96 Proceedings of the 5th European Workshop on Software Process Technology
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Query co-processing on commodity processors
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Improving hash join performance through prefetching
ACM Transactions on Database Systems (TODS)
Communications of the ACM
Relational query coprocessing on graphics processors
ACM Transactions on Database Systems (TODS)
Sort vs. Hash revisited: fast join implementation on modern multi-core CPUs
Proceedings of the VLDB Endowment
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Design and evaluation of main memory hash join algorithms for multi-core CPUs
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
SAAHPC '11 Proceedings of the 2011 Symposium on Application Accelerators in High-Performance Computing
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
GPGPU for real-time data analytics
ICPADS '12 Proceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems
Red Fox: An Execution Environment for Relational Query Processing on GPUs
Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization
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Driven by the rapid hardware development of parallel CPU/GPU architectures, we have witnessed emerging relational query processing techniques and implementations on those parallel architectures. However, most of those implementations are not portable across different architectures, because they are usually developed from scratch and target at a specific architecture. This paper proposes a kernel-adapter based design (OmniDB), a portable yet efficient query processor on parallel CPU/GPU architectures. OmniDB attempts to develop an extensible query processing kernel (qKernel) based on an abstract model for parallel architectures, and to leverage an architecture-specific layer (adapter) to make qKernel be aware of the target architecture. The goal of OmniDB is to maximize the common functionality in qKernel so that the development and maintenance efforts for adapters are minimized across different architectures. In this demo, we demonstrate our initial efforts in implementing OmniDB, and present the preliminary results on the portability and efficiency.