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However, the high memory requirements limit their use, especially on low-end mobile platforms. Compression techniques have been widely adopted to reduce memory consumption but face two primary issues when applied to mobile GPUs: (1)\n            <jats:italic>low repetition ratio<\/jats:italic>\n            caused by small raw data sizes and concurrency, and (2)\n            <jats:italic>low locality<\/jats:italic>\n            caused by unpredictable rendering behaviors. These two limitations result in a low compression ratio when compressors are applied to low-end mobile devices.\n          <\/jats:p>\n          <jats:p>\n            This article introduces\n            <jats:italic>gCom<\/jats:italic>\n            , a fine-grained rendering compressor accelerated by GPUs. To improve the compression ratio,\n            <jats:italic>gCom<\/jats:italic>\n            incorporates the following innovations. First, unlike other compression techniques that use frames or tiles as basic processing units,\n            <jats:italic>gCom<\/jats:italic>\n            is the first to employ a fine-grained processing unit (i.e., the color channel), enhancing repetition amplification without increasing raw data. Second,\n            <jats:italic>gCom<\/jats:italic>\n            introduces two key features\u2014\n            <jats:italic>Hierarchical Delta<\/jats:italic>\n            and\n            <jats:italic>Channel Decorrelator<\/jats:italic>\n            \u2014which maximize the locality of adjacent channels and reduce raw data size. Third, to maintain the original GPU throughput,\n            <jats:italic>gCom<\/jats:italic>\n            revolutionizes the Golomb-Rice algorithm and proposes a new compression approach, the Parallel-Oriented Golomb-Rice algorithm, enabling parallel execution of both decompression and compression processes. The entire design of\n            <jats:italic>gCom<\/jats:italic>\n            utilizes only idle resources and existing commands on mobile GPUs, thus keeping purchasing costs low.\n          <\/jats:p>\n          <jats:p>\n            To date,\n            <jats:italic>gCom<\/jats:italic>\n            has improved the channel locality by nearly 50%. The best compression achievement received by\n            <jats:italic>gCom<\/jats:italic>\n            has reached around 20%.\n          <\/jats:p>","DOI":"10.1145\/3711819","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T03:44:37Z","timestamp":1736394277000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["gCom: Fine-grained Compressors in Graphics Memory of Mobile GPU"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7178-5097","authenticated-orcid":false,"given":"Dongjie","family":"Tang","sequence":"first","affiliation":[{"name":"Shanghai Custom College, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9552-9357","authenticated-orcid":false,"given":"Zijun","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Custom College, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7969-6891","authenticated-orcid":false,"given":"Yun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7819-5667","authenticated-orcid":false,"given":"Yicheng","family":"Gu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-293X","authenticated-orcid":false,"given":"Fangxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2730-2319","authenticated-orcid":false,"given":"Zhengwei","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Software, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142548"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2019.00015"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2019.00014"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378498"},{"key":"e_1_3_2_6_2","first-page":"499","volume-title":"Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201920)","author":"Bai Zhihao","year":"2020","unstructured":"Zhihao Bai, Zhen Zhang, Yibo Zhu, and Xin Jin. 2020. 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