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Syst."],"published-print":{"date-parts":[[2022,2,24]]},"abstract":"<jats:p>Stacked DRAMs have been studied, evaluated in multiple scenarios, and even productized in the last decade. The large available bandwidth they offer make them an attractive choice, particularly, in high-performance computing (HPC) environments. Consequently, many prior research efforts have studied and evaluated 3D stacked DRAM-based designs. Despite offering high bandwidth, stacked DRAMs are severely constrained by the overall memory capacity offered. In this paper, we study and evaluate integrating stacked DRAM on top of a GPU in a 3D manner which in tandem with the 2.5D stacked DRAM increases the capacity and the bandwidth without increasing the package size. This integration of 3D stacked DRAMs aids in satisfying the capacity requirements of emerging workloads like deep learning. Though this vertical 3D integration of stacked DRAMs also increases the total available bandwidth, we observe that the bandwidth offered by these 3D stacked DRAMs is severely limited by the heat generated on the GPU. Based on our experiments on a cycle-level simulator, we make a key observation that the sections of the 3D stacked DRAM that are closer to the GPU have lower retention-times compared to the farther layers of stacked DRAM. This thermal-induced variable retention-times causes certain sections of 3D stacked DRAM to be refreshed more frequently compared to the others, thereby resulting in thermal-induced NUMA paradigms. To alleviate such thermal-induced NUMA behavior, we propose and experimentally evaluate three different incarnations of Data Convection, i.e., Intra-layer, Inter-layer, and Intra + Inter-layer, that aim at placing the most-frequently accessed data in a thermal-induced retention-aware fashion, taking into account both bank-level and channel-level parallelism. Our evaluations on a cycle-level GPU simulator indicate that, in a multi-application scenario, our Intra-layer, Inter-layer and Intra + Inter-layer algorithms improve the overall performance by 1.8%, 11.7%, and 14.4%, respectively, over a baseline that already encompasses 3D+2.5D stacked DRAMs.<\/jats:p>","DOI":"10.1145\/3508027","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T23:44:29Z","timestamp":1646091869000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Data Convection"],"prefix":"10.1145","volume":"6","author":[{"given":"Soheil","family":"Khadirsharbiyani","sequence":"first","affiliation":[{"name":"Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jagadish","family":"Kotra","sequence":"additional","affiliation":[{"name":"AMD Research, Austin, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karthik","family":"Rao","sequence":"additional","affiliation":[{"name":"Advanced Micro Devices Inc., Austin, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmut","family":"Kandemir","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, University Park, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123939.3124547"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2593069.2593128"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2009.5413147"},{"key":"e_1_2_1_4_1","unstructured":"AMD Inc. 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