{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T22:28:17Z","timestamp":1791412097478,"version":"4.3.3"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p>CXL-based disaggregated memory systems offer options to expand the memory beyond the limits of a single server via cache-coherent memory expansion cards or memory pools. Especially, In-Memory Database Management Systems (IMDBMSs) can benefit from alleviating two critical constraints: (1) limited memory capacity in a server and (2) long restart time during failover to reload data to memory. However, the usage and effectiveness of CXL memory in enterprise-scale IMDBMSs has yet to be validated.<\/jats:p>\n          <jats:p>In this work---for the first time---we investigate dynamic memory expansion employing commercial CXL memory devices for IMDBMSs. Our detailed performance analysis reveals that the performance impact of higher latency and lower memory bandwidth impact depends on the memory access patterns of data structures (cf. (1)). Additionally, we present the feasibility of CXL shared memory between servers to improve restart times during failover (cf. (2)).<\/jats:p>\n          <jats:p>Our evaluation shows the effectiveness of CXL memory integrated into the SAP HANA Cloud IMDBMS. OLTP workloads have a negligible performance degradation while OLAP workloads have a wide range of performance degradation. CXL shared memory shows a 40% reduction of the restart time for TPC-H SF10 and 84% potential reduction for TPC-H SF100.<\/jats:p>","DOI":"10.14778\/3685800.3685809","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"3827-3840","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["An Examination of CXL Memory Use Cases for In-Memory Database Management Systems Using SAP HANA"],"prefix":"10.14778","volume":"17","author":[{"given":"Minseon","family":"Ahn","sequence":"first","affiliation":[{"name":"SAP Labs Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Willhalm","sequence":"additional","affiliation":[{"name":"Intel Deutschland GmbH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norman","family":"May","sequence":"additional","affiliation":[{"name":"SAP SE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghun","family":"Lee","sequence":"additional","affiliation":[{"name":"SAP Labs Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suprasad Mutalik","family":"Desai","sequence":"additional","affiliation":[{"name":"Intel Technology India Pvt. Ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Booss","sequence":"additional","affiliation":[{"name":"SAP SE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungmin","family":"Kim","sequence":"additional","affiliation":[{"name":"SAP Labs Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navneet","family":"Singh","sequence":"additional","affiliation":[{"name":"Intel Technology India Pvt. Ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Ritter","sequence":"additional","affiliation":[{"name":"SAP SE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Rebholz","sequence":"additional","affiliation":[{"name":"SAP SE"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3606557.3606563"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3533737.3535090"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137780"},{"key":"e_1_2_1_4_1","unstructured":"AsteraLabs. 2024. Breaking Through the Memory Wall. https:\/\/www.asteralabs.com\/general\/breaking-through-the-memory-wall\/"},{"key":"e_1_2_1_5_1","volume-title":"BTW 2023","author":"Benson Lawrence","year":"2023","unstructured":"Lawrence Benson, Marcel Weisgut, and Tilmann Rabl. 2023. What We Can Learn from Persistent Memory for CXL. BTW 2023 (2023)."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2023.3241586"},{"key":"e_1_2_1_7_1","unstructured":"Prakash Chauhan and Mahesh Wagh. 2022. CXL Memory Challenges. https:\/\/hc34.hotchips.org\/assets\/program\/tutorials\/CXL\/Hot%20Chips%202022%20CXL%20MemoryChallenges.pdf"},{"key":"e_1_2_1_8_1","unstructured":"Compute Express Link Consortium. 2019. CXL. https:\/\/www.computeexpresslink.org\/"},{"key":"e_1_2_1_9_1","unstructured":"CXL. 2024. CXL 3.1 Specification. https:\/\/computeexpresslink.org\/cxl-specification\/"},{"key":"e_1_2_1_10_1","unstructured":"CXL. 2024. Past CXL Specifications. https:\/\/computeexpresslink.org\/past-cxl-specifications\/"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3361682"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000058"},{"key":"e_1_2_1_13_1","first-page":"28","article-title":"The SAP HANA Database-An Architecture Overview","volume":"35","author":"F\u00e4rber Franz","year":"2012","unstructured":"Franz F\u00e4rber, Norman May, Wolfgang Lehner, Philipp Gro\u00dfe, Ingo M\u00fcller, Hannes Rauhe, and Jonathan Dees. 2012. The SAP HANA Database-An Architecture Overview. IEEE Data Eng. Bull. 35, 1 (2012), 28--33.","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_14_1","unstructured":"The LINUX Foundation. 2021. Direct Access for files. https:\/\/www.kernel.org\/doc\/Documentation\/filesystems\/dax.txt"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18420\/BTW2023-47"},{"key":"e_1_2_1_16_1","unstructured":"Donghyun Gouk Sangwon Lee Miryeong Kwon and Myoungsoo Jung. 2022. Direct access High-Performance memory disaggregation with DirectCXL. In USENIX ATC. 287--294. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/gouk"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3457390.3457402"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2023.3237548"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3399666.3399931"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551837"},{"key":"e_1_2_1_21_1","unstructured":"Donghun Lee Andrew Chang Minseon Ahn Jongmin Gim Jungmin Kim Jaemin Jung Kang-Woo Choi Vincent Pham Oliver Rebholz Krishna Malladi et al. 2020. Optimizing Data Movement with Near-Memory Acceleration of In-memory DBMS.. In EDBT. 371--374."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592980.3595311"},{"key":"e_1_2_1_23_1","volume-title":"Database Kernels: Seamless Integration of Database Systems and Fast Storage via CXL. In CIDR.","author":"Lee Sangjin","year":"2024","unstructured":"Sangjin Lee, Alberto Lerner, Philippe Bonnet, and Philippe Cudr\u00e9-Mauroux. 2024. Database Kernels: Seamless Integration of Database Systems and Fast Storage via CXL. In CIDR."},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Alberto Lerner and Gustavo Alonso. 2024. CXL and the Return of Scale-Up Database Engines. arXiv:2401.01150 [cs.DB]","DOI":"10.14778\/3675034.3675047"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3578835"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2012.6168955"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Clemens Lutz Sebastian Bre\u00df Steffen Zeuch Tilmann Rabl and Volker Markl. 2020. Pump up the volume: Processing large data on GPUs with fast interconnects. In SIGMOD. 1633--1649.","DOI":"10.1145\/3318464.3389705"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-155860869-6\/50025-1"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.02878"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592980.3595314"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","unstructured":"J. McGlone P. Palazzari and J. B. Leclere. 2018. Accelerating Key In-memory Database Functionality with FPGA Technology. In ReConFig. 1--8. 10.1109\/RECONFIG.2018.8641722","DOI":"10.1109\/RECONFIG.2018.8641722"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/HCS55958.2022.9895633"},{"key":"e_1_2_1_33_1","unstructured":"Intel\u00ae PCM. 2023. Intel\u00ae Performance Counter Monitor. https:\/\/github.com\/intel\/pcm"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","unstructured":"Hasso Plattner. 2009. A common database approach for OLTP and OLAP using an in-memory column database. In SIGMOD. ACM 1--2. 10.1145\/1559845.1559846","DOI":"10.1145\/1559845.1559846"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733074"},{"key":"e_1_2_1_36_1","unstructured":"PMDK. 2004. Persistent Memory Development Kit. https:\/\/pmem.io\/pmdk\/"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15350-6_7"},{"key":"e_1_2_1_38_1","unstructured":"SAP. 2012. translytical-data-platforms-forrester-wave-sap-a-leader. https:\/\/news.sap.com\/2022\/12\/translytical-data-platforms-forrester-wave-sap-a-leader\/"},{"key":"e_1_2_1_39_1","unstructured":"SAP. 2024. Persistent Memory in SAP HANA. https:\/\/help.sap.com\/docs\/SAP_HANA_PLATFORM\/6b94445c94ae495c83a19646e7c3fd56\/1f61b13e096d4ef98e62c676debf117e.html"},{"key":"e_1_2_1_40_1","unstructured":"SAP. 2024. SAP HANA Fast Restart Option. https:\/\/help.sap.com\/docs\/SAP_HANA_PLATFORM\/6b94445c94ae495c83a19646e7c3fd56\/ce158d28135147f099b761f8b1ee43fc.html"},{"key":"e_1_2_1_41_1","unstructured":"SAP. 2024. SAP Profitability and Performance Management. https:\/\/www.sap.com\/products\/financial-management\/profitability-and-performance-management.html"},{"key":"e_1_2_1_42_1","unstructured":"SAP. 2024. Why SAP data and analytics? https:\/\/www.sap.com\/products\/technology-platform\/analytics.html"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352123"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213946"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2022.3226482"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3613424.3614256"},{"key":"e_1_2_1_47_1","volume-title":"the Linux Kernel Organization","author":"Inc.","year":"2024","unstructured":"Inc. the Linux Kernel Organization. 2024. NUMA memory policy. https:\/\/www.kernel.org\/doc\/Documentation\/vm\/numa_memory_policy.txt"},{"key":"e_1_2_1_48_1","unstructured":"TPC-C. 2023. TPC-C. https:\/\/www.tpc.org\/tpcc\/"},{"key":"e_1_2_1_49_1","unstructured":"TPC-DS. 2023. TPC-DS. https:\/\/www.tpc.org\/tpcds\/"},{"key":"e_1_2_1_50_1","unstructured":"TPC-H. 2023. TPC-H. https:\/\/www.tpc.org\/tpch\/"},{"key":"e_1_2_1_51_1","volume-title":"Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems. In 2022 IEEE\/ACM Workshop on Memory Centric High Performance Computing (MCHPC). IEEE, 11--20","author":"Wahlgren Jacob","year":"2022","unstructured":"Jacob Wahlgren, Maya Gokhale, and Ivy B Peng. 2022. Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems. In 2022 IEEE\/ACM Workshop on Memory Centric High Performance Computing (MCHPC). IEEE, 11--20."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.14778\/3561261.3561263"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW55747.2022.00207"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/NAS55553.2022.9925356"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2014.6844459"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536206.2536210"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3533044"},{"key":"e_1_2_1_58_1","volume-title":"Managing Memory Tiers with CXL in Virtualized Environments. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)","author":"Zhong Yuhong","year":"2024","unstructured":"Yuhong Zhong, Daniel S. Berger, Carl Waldspurger, Ishwar Agarwal, Rajat Agarwal, Frank Hady, Karthik Kumar, Mark D. Hill, Mosharaf Chowdhury, and Asaf Cidon. 2024. Managing Memory Tiers with CXL in Virtualized Environments. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24). USENIX Association, Santa Clara, CA, 37--56. https:\/\/www.usenix.org\/conference\/osdi24\/presentation\/zhong-yuhong"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685809","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:33:24Z","timestamp":1735623204000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":58,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685809"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685809","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}