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Polaris: Enhancing CXL-based Memory Expanders with Memory-side Prefetching

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Advanced Parallel Processing Technologies (APPT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14103))

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Abstract

The use of CXL-based memory expanders introduces increased latency compared to local memory due to control and transmission overheads. This latency difference negatively impacts tasks that are sensitive to latency. While cache prefetching has traditionally been used to mitigate memory latency, addressing this performance gap requires improved CPU prefetch coverage. However, tuning a CPU prefetcher for CXL memory necessitates costly CPU modifications and can result in cache pollution and wasted memory bandwidth. To address these challenges, we propose a solution called Polaris, a novel CXL memory expander that integrates a hardware prefetcher in the CXL memory controller chip. Polaris analyzes incoming memory requests and prefetches cachelines to a dedicated SRAM buffer without requiring modifications to CPUs or software. In cases where prefetch hits occur, Polaris establishes a “shortcut” for rapid memory access, significantly reducing the performance gap between CXL and local DDR memory. Furthermore, if small CPU changes are allowed, such as extending Intel’s DDIO, Polaris can further minimize CXL memory access overheads by actively pushing high-confidence prefetches to the CPU’s last-level cache (LLC). Extensive experiments demonstrate that, in conjunction with various CPU-side prefetchers, Polaris enables up to 85% of common workloads (on average, 43%) to effectively tolerate CXL memory’s longer latency.

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Notes

  1. 1.

    We say the CXL latency is “effectively tolerated” if the performance gap between CXL and local memory is within 5%.

References

  1. Parsec 2.1, 2022.9. https://parsec.cs.princeton.edu/

  2. Pythia’s github repo, 2022.9. https://github.com/CMU-SAFARI/Pythia

  3. Aguilera, M.K., et al.: Remote regions: a simple abstraction for remote memory. In: 2018 USENIX Annual Technical Conference (USENIX ATC 18), pp. 775–787 (2018)

    Google Scholar 

  4. Al Maruf, H., Chowdhury, M.: Effectively prefetching remote memory with leap. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), pp. 843–857 (2020)

    Google Scholar 

  5. Amaro, E., et al.: Can far memory improve job throughput? In: Proceedings of the Fifteenth European Conference on Computer Systems, pp. 1–16 (2020)

    Google Scholar 

  6. Bakhshalipour, M., Lotfi-Kamran, P., Sarbazi-Azad, H.: Domino temporal data prefetcher. In: 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 131–142. IEEE (2018)

    Google Scholar 

  7. Bakhshalipour, M., Shakerinava, M., Lotfi-Kamran, P., Sarbazi-Azad, H.: Bingo spatial data prefetcher. In: 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 399–411. IEEE (2019)

    Google Scholar 

  8. Beamer, S., Asanović, K., Patterson, D.: The gap benchmark suite, arXiv preprint arXiv:1508.03619 (2015)

  9. Bera, R., et al.: Hermes: accelerating long-latency load requests via perceptron-based off-chip load prediction. In: 55th IEEE/ACM International Symposium on Microarchitecture, MICRO 2022, Chicago, IL, USA, 1–5 October 2022. IEEE, 2022, pp. 1–18 (2022). https://doi.org/10.1109/MICRO56248.2022.00015

  10. Bera, R., Kanellopoulos, K., Nori, A., Shahroodi, T., Subramoney, S., Mutlu, O.: Pythia: a customizable hardware prefetching framework using online reinforcement learning. In: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, 2021, pp. 1121–1137 (2021)

    Google Scholar 

  11. Bera, R., Nori, A.V., Mutlu, O., Subramoney, S.: Dspatch: dual spatial pattern prefetcher. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, pp. 531–544 (2019)

    Google Scholar 

  12. Bhatia, E., Chacon, G., Pugsley, S., Teran, E., Gratz, P.V., Jiménez, D.A.: Perceptron-based prefetch filtering. In: 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA), pp. 1–13. IEEE (2019)

    Google Scholar 

  13. Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). https://doi.org/10.1145/362686.362692

  14. Bucek, J., Lange, K.-D., Kistowski, J.V.: SPEC CPU2017: next-generation compute benchmark. In: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, pp. 41–42 (2018)

    Google Scholar 

  15. Calciu, I., et al.: Rethinking software runtimes for disaggregated memory. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 79–92 (2021)

    Google Scholar 

  16. ChampSim, Champsim simulator, 2022.9. https://github.com/ChampSim/ChampSim

  17. C. foundation, Cxl 3.0 specification, 2022.9. https://www.computeexpresslink.org/download-the-specification

  18. Gao, Y., et al.: When cloud storage meets \(\{RDMA\}\). In: 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21), pp. 519–533 (2021)

    Google Scholar 

  19. Gouk, D., Lee, S., Kwon, M., Jung, M.: Direct access,\(\{High-Performance\}\) memory disaggregation with \(\{DirectCXL\}\). In: 2022 USENIX Annual Technical Conference (USENIX ATC 22), pp. 287–294 (2022)

    Google Scholar 

  20. Gu, J., Lee, Y., Zhang, Y., Chowdhury, M., Shin, K.G.: Efficient memory disaggregation with infiniswap. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pp. 649–667 (2017)

    Google Scholar 

  21. Henning, J.L.: SPEC CPU2006 benchmark descriptions. ACM SIGARCH Comput. Archit. News 34(4), 1–17 (2006)

    Article  Google Scholar 

  22. Huggahalli, R., Iyer, R., Tetrick, S.: Direct cache access for high bandwidth network i/o. In: 32nd International Symposium on Computer Architecture (ISCA’05), pp. 50–59. IEEE (2005)

    Google Scholar 

  23. Hynix, S.: Sk hynix cxl memory expander, 2022.9. https://news.skhynix.com/sk-hynix-develops-ddr5-dram-cxltm-memory-to-expand-the-cxl-memory-ecosystem/

  24. Intel, Intel data-direct io, 2022.9. https://www.intel.cn/content/www/cn/zh/io/data-direct-i-o-technology.html

  25. Kim, J., Pugsley, S.H., Gratz, P.V., Reddy, A.N., Wilkerson, C., Chishti, Z.: Path confidence based lookahead prefetching. In: 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 1–12. IEEE (2016)

    Google Scholar 

  26. Kumar, A., Huggahalli, R., Makineni, S.: Characterization of direct cache access on multi-core systems and 10gbe. In: 2009 IEEE 15th International Symposium on High Performance Computer Architecture, pp. 341–352. IEEE (2009)

    Google Scholar 

  27. León, E.A., Ferreira, K.B., Maccabe, A.B.: Reducing the impact of the memorywall for I/O using cache injection. In: 2007 15th Annual IEEE Symposium on High-Performance Interconnects (HOTI), pp. 143–150. IEEE (2007)

    Google Scholar 

  28. Li, H., et al.: First-generation memory disaggregation for cloud platforms, arXiv preprint arXiv:2203.00241 (2022)

  29. Lim, K., Chang, J., Mudge, T., Ranganathan, P., Reinhardt, S.K., Wenisch, T.F.: Disaggregated memory for expansion and sharing in blade servers. ACM SIGARCH Comput. Archit. News 37(3), 267–278 (2009)

    Article  Google Scholar 

  30. Maruf, H.A., et al.: TPP: transparent page placement for cxl-enabled tiered memory, arXiv preprint arXiv:2206.02878 (2022)

  31. Michaud, P.: Best-offset hardware prefetching. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 469–480 (2016)

    Google Scholar 

  32. Muralimanohar, N., Balasubramonian, R., Jouppi, N.P.: Cacti 6.0: a tool to model large caches. HP Lab. 27, 28 (2009)

    Google Scholar 

  33. Nassif, N., et al.: Sapphire rapids: the next-generation intel Xeon scalable processor. In: 2022 IEEE International Solid-State Circuits Conference (ISSCC), vol. 65, pp. 44–46. IEEE (2022)

    Google Scholar 

  34. NCSU, Freepdk45. https://www.eda.ncsu.edu/wiki/FreePDK45:Contents

  35. OpenXiangShan, Xiangshan riscv cpu, 2022.9. https://github.com/OpenXiangShan/XiangShan

  36. Pugsley, S.H., et al.: Sandbox prefetching: safe run-time evaluation of aggressive prefetchers. In: IEEE 20th International Symposium on High Performance Computer Architecture (HPCA), pp. 626–637. IEEE (2014)

    Google Scholar 

  37. Ruan, Z., Schwarzkopf, M., Aguilera, M.K., Belay, A.: \(\{AIFM\}\):\(\{High-Performance\}\),\(\{Application-Integrated\}\) far memory. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 315–332 (2020)

    Google Scholar 

  38. Samsung, Smdk, 2022.9. https://github.com/OpenMPDK/SMDK.git

  39. Shakerinava, M., Bakhshalipour, M., Lotfi-Kamran, P., Sarbazi-Azad, H.: Multi-lookahead offset prefetching. The Third Data Prefetching Championship (2019)

    Google Scholar 

  40. Shan, Y., Huang, Y., Chen, Y., Zhang, Y.: \(\{LegoOS\}\): a disseminated, distributed \(\{OS\}\) for hardware resource disaggregation. In: 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18), pp. 69–87 (2018)

    Google Scholar 

  41. Shevgoor, M., Koladiya, S., Balasubramonian, R., Wilkerson, C., Pugsley, S.H., Chishti, Z.: Efficiently prefetching complex address patterns. In: 2015 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 41–152. IEEE (2015)

    Google Scholar 

  42. Sumsung, Expanding the limits of memory bandwidth and density: Samsung’s cxl dram memory expander, 2022.9. https://semiconductor.samsung.com/newsroom/tech-blog/expanding-the-limits-of-memory-bandwidth-and-density-samsungs-cxl-dram-memory-expander/

  43. Tang, D., Bao, Y., Hu, W., Chen, M.: DMA cache: using on-chip storage to architecturally separate I/O data from CPU data for improving I/O performance. In: HPCA-16: The Sixteenth International Symposium on High-Performance Computer Architecture, pp. 1–12. IEEE (2010)

    Google Scholar 

  44. Viswanathan, V.: Disclosure of H/W prefetcher control on some intel processors. Intel SW Developer Zone (2014)

    Google Scholar 

  45. Wang, C., et al.: Semeru: a \(\{Memory-Disaggregated\}\) managed runtime. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 261–280 (2020)

    Google Scholar 

  46. Wiki, Pcie 5.0, 2022.9. https://en.wikipedia.org/wiki/PCI_Express

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Acknowledgment

This work is supported by Key-Area Research and Development Program of Guangdong Province (2021B0101310002), NSFC (61832020, 62032001, 92064006) and 111 Project (B18001).

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Correspondence to Guangyu Sun .

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Zhou, Z. et al. (2024). Polaris: Enhancing CXL-based Memory Expanders with Memory-side Prefetching. In: Li, C., Li, Z., Shen, L., Wu, F., Gong, X. (eds) Advanced Parallel Processing Technologies. APPT 2023. Lecture Notes in Computer Science, vol 14103. Springer, Singapore. https://doi.org/10.1007/978-981-99-7872-4_2

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