Implementing database operations using SIMD instructions
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
DBMSs on a Modern Processor: Where Does Time Go?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient Progressive Skyline Computation
Proceedings of the 27th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Maximal vector computation in large data sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approaching the skyline in Z order
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Angle-based space partitioning for efficient parallel skyline computation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Computer Organization and Design, Fourth Edition, Fourth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)
Parallel Skyline Computation on Multicore Architectures
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Scalable skyline computation using object-based space partitioning
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
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A dominance test, which decides the dominance relationship between tuples, is a core operation in skyline computation. Optimizing dominance tests can thus improve the performance of all existing skyline algorithms. Towards this goal, this paper proposes a vectorization of dominance tests in SIMD architectures. Specifically, our vectorization can perform the dominance test of multiple consecutive dimensions in parallel, thereby achieving a speedup of SIMD parallelism degree in theory. However, achieving such performance gain is non-trivial due to complex control dependencies within the dominance test. To address this problem, we devise an efficient vectorization, called VSkyline, which performs the dominance test with SIMD instructions by determining incomparability in a block of four dimensional values. Experimental results using a performance monitor show that VSkyline considerably reduces the numbers of both executed instructions and branch mispredictions.