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- W2616403344 abstract "Advances in hardware strongly influence the database system design. The flattening speed of CPU cores makes manycore accelerators a vital alternative to explore for processing the ever-increasing amounts of data. Accelerators typically employ more and simpler cores with reduced instruction control overhead. For example, in GPUs, multiple execution units share the same instruction sequencing logic. As a result, GPU processors do not face the power constraints limiting the parallelism of CPUs. Use of GPUs yields significant speed-ups for database processing [1], [2]. The increased availability of GPUs in the cloud opens new acceleration possibilities. To harness the power of GPUs, we adapt and redesign data analytics operators to exploit better their special memory and threading model. Due to the increasing memory capacities and also the users' need for fast interaction with data, as in the case of visual analytics, we focus on in-memory data processing. We identify two key applications for hardware acceleration because of their impact on end-to-end system performance: a) Substring matching and b) Lossless data compression. Our techniques cover data preprocessing and algorithmic operator optimization taking the GPU control flow into consideration. We also discuss to what extent we can adapt our techniques for CPUs with SIMD extensions." @default.
- W2616403344 created "2017-05-26" @default.
- W2616403344 creator A5063629406 @default.
- W2616403344 date "2017-04-01" @default.
- W2616403344 modified "2023-09-25" @default.
- W2616403344 title "Hardware acceleration of database analytics" @default.
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- W2616403344 doi "https://doi.org/10.1109/icde.2017.239" @default.
- W2616403344 hasPublicationYear "2017" @default.
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