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Hardware Partitioning for Big Data Analytics

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4 Author(s)
Wu, L. ; Comput. Sci. Dept., Columbia Univ., Santa Clara, CA, USA ; Barker, R.J. ; Kim, M.A. ; Ross, K.A.

Targeted deployment of hardware accelerators can improve the throughput and energy efficiency of large-scale data processing. Data partitioning is a critical operation for manipulating large datasets and is often the limiting factor in database performance. A hardware-software streaming framework offers a seamless execution environment for streaming accelerators such as the Hardware-Accelerated Range Partitioner (HARP). Together, the streaming framework and HARP provide an order of magnitude improvement in partitioning and energy performance.

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Micro, IEEE  (Volume:34 ,  Issue: 3 )