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Lightweight memory tracing for hot data identification

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Abstract

The low capacity of main memory has become a critical issue in the performance of systems. Several memory schemes, utilizing multiple classes of memory devices, are used to mitigate the problem; hiding the small capacity by placing data in proper memory devices based on the hotness of the data. Memory tracers can provide such hotness information, but existing tracing tools incur extremely high overhead and the overhead increases as the problem size of a workload grows. In this paper, we propose Daptrace built for tracing memory access with bounded and light overhead. The two main techniques, region-based sampling and adaptive region construction, are utilized to maintain a low overhead regardless of the program size. For evaluation, we trace a wide range of 20 workloads and compared with baseline. The results show that Daptrace has a very small amount of runtime overhead and storage space overhead (1.95% and 5.38 MB on average) while maintaining the tracing quality regardless of the working set size of a workload. Also, a case study on out-of-core memory management exhibits a high potential of Daptrace for optimal data management. From the evaluation results, we can conclude that Daptrace shows great performance on identifying hot memory objects.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF2015M3C4A7065646 and No. 2017R1A2B4005681).

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  1. Seoul National University, Seoul, South Korea

    Yunjae Lee & Heon Y. Yeom

  2. Sookmyung Women’s University, Seoul, South Korea

    Yoonhee Kim

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  1. Yunjae Lee
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  2. Yoonhee Kim
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Lee, Y., Kim, Y. & Yeom, H.Y. Lightweight memory tracing for hot data identification. Cluster Comput 23, 2273–2285 (2020). https://doi.org/10.1007/s10586-020-03130-1

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