DIRECT - a multiprocessor organization for supporting relational data base management systems
ISCA '78 Proceedings of the 5th annual symposium on Computer architecture
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Accelerating database operators using a network processor
DaMoN '05 Proceedings of the 1st international workshop on Data management on new hardware
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Data parallel Haskell: a status report
Proceedings of the 2007 workshop on Declarative aspects of multicore programming
Scan primitives for GPU computing
Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Efficient gather and scatter operations on graphics processors
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Vectorized data processing on the cell broadband engine
DaMoN '07 Proceedings of the 3rd international workshop on Data management on new hardware
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Breaking the memory wall in MonetDB
Communications of the ACM - Surviving the data deluge
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Real-time parallel hashing on the GPU
ACM SIGGRAPH Asia 2009 papers
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
Proceedings of the VLDB Endowment
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing
Proceedings of the 1st ACM symposium on Cloud computing
Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
iNFAnt: NFA pattern matching on GPGPU devices
ACM SIGCOMM Computer Communication Review
GPU-accelerated predicate evaluation on column store
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Communications of the ACM
Proceedings of the 2011 SIGGRAPH Asia Conference
Efficient hash tables on the gpu
Efficient hash tables on the gpu
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
Just-in-time data distribution for analytical query processing
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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The multi-core architectures of today's computer systems make parallelism a necessity for performance critical applications. Writing such applications in a generic, hardware-oblivious manner is a challenging problem: Current database systems thus rely on labor-intensive and error-prone manual tuning to exploit the full potential of modern parallel hardware architectures like multi-core CPUs and graphics cards. We propose an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime. This design reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems. We provide a proof-of-concept for this design by integrating operators written using the parallel programming framework OpenCL into the open-source database MonetDB. Following this approach, we achieve efficient, yet highly portable parallel code without the need for optimization by hand. We evaluated our implementation against MonetDB using TPC-H derived queries and observed a performance that rivals that of MonetDB's query execution on the CPU and surpasses it on the GPU. In addition, we show that the same set of operators runs nearly unchanged on a GPU, demonstrating the feasibility of our approach.