{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T20:35:58Z","timestamp":1780346158706,"version":"3.54.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,12,18]]},"abstract":"<jats:p>In this paper, we suggest a novel GPU-in-data-path architecture that leverages a GPU to accelerate the I\/O path and thus can achieve almost in-memory bandwidth using SSDs. In this architecture, the main idea is to stream data in heavy-weight compressed blocks from SSDs directly into the GPU and decompress it on-the-fly as part of the table scan to inflate data before processing it by downstream query operators. Furthermore, we employ novel GPU-optimized pruning techniques that help us further inflate the perceived read bandwidth. In our evaluation, we show that the GPU-in-data-path architecture can achieve an effective bandwidth of up to 100 GiB\/s, surpassing existing in-memory systems' capabilities.<\/jats:p>","DOI":"10.1145\/3698812","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T16:40:35Z","timestamp":1734712835000},"page":"1-26","source":"Crossref","is-referenced-by-count":7,"title":["GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8654-5738","authenticated-orcid":false,"given":"Nils","family":"Boeschen","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt &amp; hessian.AI, Darmstadt, DE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1602-4512","authenticated-orcid":false,"given":"Tobias","family":"Ziegler","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, DE"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2744-7836","authenticated-orcid":false,"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt &amp; DFKI Darmstadt, Darmstadt, DE"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3662010.3663450"},{"key":"e_1_2_2_2_1","unstructured":"Jens Axboe. 2022. Flexible I\/O Tester. https:\/\/github.com\/axboe\/fio"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536274.2536325"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/375663.375692"},{"key":"e_1_2_2_5_1","volume-title":"9th Biennial Conference on Innovative Data Systems Research, CIDR","author":"Chrysogelos Periklis","year":"2019","unstructured":"Periklis Chrysogelos, Panagiotis Sioulas, and Anastasia Ailamaki. 2019. Hardware-conscious Query Processing in GPU-accelerated Analytical Engines. In 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, January 13--16, 2019, Online Proceedings. www.cidrdb.org. http:\/\/cidrdb.org\/cidr2019\/papers\/p127-chrysogelos-cidr19.pdf"},{"key":"e_1_2_2_6_1","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 1061--1063","author":"Fang Rui","unstructured":"Rui Fang, Bingsheng He, Mian Lu, Ke Yang, Naga K. Govindaraju, Qiong Luo, and Pedro V. Sander. 2007. GPUQP: query co-processing using graphics processors. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 1061--1063."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920927"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/SOAC.1991.143840"},{"key":"e_1_2_2_9_1","volume-title":"10th Conference on Innovative Data Systems Research, CIDR","author":"Haas Gabriel","year":"2020","unstructured":"Gabriel Haas, Michael Haubenschild, and Viktor Leis. 2020. Exploiting Directly-Attached NVMe Arrays in DBMS. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Amsterdam, The Netherlands, January 12--15, 2020, Online Proceedings. www.cidrdb.org. http:\/\/cidrdb.org\/cidr2020\/papers\/p16-haas-cidr20.pdf"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","unstructured":"Juliana Hildebrandt Dirk Habich Patrick Damme and Wolfgang Lehner. 2016. Compression-Aware In-Memory Query Processing: Vision System Design and Beyond. In Data Management on New Hardware - 7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics IMDM 2016 New Delhi India September 1 2016 Revised Selected Papers (Lecture Notes in Computer Science Vol. 10195) Spyros Blanas Rajesh Bordawekar Tirthankar Lahiri Justin J. Levandoski and Andrew Pavlo (Eds.). Springer 40--56. https:\/\/doi.org\/10.1007\/978--3--319--56111-0_3","DOI":"10.1007\/978--3--319--56111-0_3"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2236584.2236592"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00026"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457540"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007328.3007331"},{"key":"e_1_2_2_15_1","volume-title":"Freitag","author":"Neumann Thomas","year":"2020","unstructured":"Thomas Neumann and Michael J. Freitag. 2020. Umbra: A Disk-Based System with In-Memory Performance. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Amsterdam, The Netherlands, January 12--15, 2020, Online Proceedings. www.cidrdb.org. http:\/\/cidrdb.org\/cidr2020\/papers\/p29-neumann-cidr20.pdf"},{"key":"e_1_2_2_16_1","volume-title":"13th Conference on Innovative Data Systems Research, CIDR 2023","author":"Nicholson Hamish","year":"2023","unstructured":"Hamish Nicholson, Aunn Raza, Periklis Chrysogelos, and Anastasia Ailamaki. 2023. HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics. In 13th Conference on Innovative Data Systems Research, CIDR 2023, Amsterdam, The Netherlands, January 8--11, 2023. www.cidrdb.org. https:\/\/www.cidrdb.org\/cidr2023\/papers\/p84-nicholson.pdf"},{"key":"e_1_2_2_17_1","unstructured":"NVIDIA. 2024. cuDF. https:\/\/github.com\/rapidsai\/cudf"},{"key":"e_1_2_2_18_1","unstructured":"NVIDIA. 2024. nvcomp Library. https:\/\/developer.nvidia.com\/nvcomp"},{"key":"e_1_2_2_19_1","unstructured":"NVIDIA. 2024. NVIDIA GPU Direct Storage. https:\/\/docs.nvidia.com\/gpudirect-storage\/"},{"key":"e_1_2_2_20_1","unstructured":"NVIDIA. 2024 d. NVIDIA Unified Memory. https:\/\/developer.nvidia.com\/blog\/improving-gpu-memory-oversubscription-performance\/"},{"key":"e_1_2_2_21_1","unstructured":"Patrick E O'Neil Elizabeth J O'Neil and Xuedong Chen. 2007. The star schema benchmark (SSB). https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=d500381f36e3599400db896173c21bd395dd090d"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461538"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575748"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380595"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526132"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00068"},{"key":"e_1_2_2_27_1","unstructured":"TLC. 2024. Trip Record Data. https:\/\/www.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page"},{"key":"e_1_2_2_28_1","volume-title":"International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@VLDB 2018","author":"Tom\u00e9 Diego G.","year":"2018","unstructured":"Diego G. Tom\u00e9, Tim Gubner, Mark Raasveldt, Eyal Rozenberg, and Peter A. Boncz. 2018. Optimizing Group-By and Aggregation using GPU-CPU Co-Processing. In International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@VLDB 2018, Rio de Janeiro, Brazil, August 27, 2018, Rajesh Bordawekar and Tirthankar Lahiri (Eds.). 1--10. http:\/\/www.adms-conf.org\/2018-camera-ready\/tome_groupby.pdf"},{"key":"e_1_2_2_29_1","unstructured":"Transaction Processing Performance Council (TPC). 1993. TPC BENCHMARK H (Decision Support) Standard Specification Revision 3.0.1. http:\/\/www.tpc.org"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687671"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.48786\/EDBT.2024.13"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551809"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536206.2536210"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137769"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698812","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3698812","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T17:45:47Z","timestamp":1774979147000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3698812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12,18]]}},"alternative-id":["10.1145\/3698812"],"URL":"https:\/\/doi.org\/10.1145\/3698812","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]}}}