ReCSA: a dedicated sort accelerator using ReRAM-based content addressable memory |
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Authors: | Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO |
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Affiliation: | National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China |
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Abstract: | With the increasing amount of data, there is an urgent need for efficient sorting algorithms to process large data sets. Hardware sorting algorithms have attracted much attention because they can take advantage of different hardware’s parallelism. But the traditional hardware sort accelerators suffer “memory wall” problems since their multiple rounds of data transmission between the memory and the processor. In this paper, we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array simultaneously. Using this designed ReCAM array, we present ReCSA, which is the first dedicated ReCAM-based sort accelerator. Besides hardware designs, we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting performance. The sorting algorithm in ReCSA can process various data types, such as integer, float, double, and strings. We also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort accelerators. The experimental results show that ReCSA has , , , , and speedups against CPU-, GPU-, FPGA-, NDP-, and PIM-based platforms when processing numeric data sets. ReCSA also has , , and performance improvement when processing string data sets compared with CPU-, GPU-, and FPGA-based platforms. |
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Keywords: | ReCAM parallel sorting architecture design processing-in-memory |
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