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Rethinking SIMD Vectorization for In-Memory Databases

Rethinking SIMD Vectorization for In-Memory Databases

陈傲天
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Our evaluation on the MIC-based Xeon Phi co-processor as well as the latest mainstream CPUs shows that our vectorization designs are up to an order of magnitude faster than the state-of-the-art scalar and vector approaches. Also, we highlight the impact of efficient vectorization on the algorithmic design of in-memory database operators, as well as the architectural design and power efficiency of hardware, by making simple cores comparably fast to complex cores. This work is applicable to CPUs and co-processors with advanced SIMD capabilities, using either many simple cores or fewer complex cores.
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