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- W2557830883 abstract "While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics. Recent studies propose scale-in clusters with in-storage processing devices to process big data analytics with Spark However the proposal is based solely on the memory bandwidth characterization of in-memory data analytics and also does not shed light on the specification of host CPU and memory. Through empirical evaluation of in-memory data analytics with Apache Spark on an Ivy Bridge dual socket server, we have found that (i) simultaneous multi-threading is effective up to 6 cores (ii) data locality on NUMA nodes can improve the performance by 10% on average, (iii) disabling next-line L1-D prefetchers can reduce the execution time by up to 14%, (iv) DDR3 operating at 1333 MT/s is sufficient and (v) multiple small executors can provide up to 36% speedup over single large executor." @default.
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- W2557830883 date "2016-12-06" @default.
- W2557830883 modified "2023-10-14" @default.
- W2557830883 title "Node architecture implications for in-memory data analytics on scale-in clusters" @default.
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- W2557830883 doi "https://doi.org/10.1145/3006299.3006319" @default.
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