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Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            High-resolution simulations can deliver great visual quality, but they are often limited by available memory, especially on GPUs. We present a compiler for physical simulation that can achieve both high performance and significantly reduced memory costs, by enabling flexible and aggressive\n            <jats:italic>quantization.<\/jats:italic>\n            Low-precision (\"quantized\") numerical data types are used and packed to represent simulation states, leading to reduced memory space and bandwidth consumption. Quantized simulation allows higher resolution simulation with less memory, which is especially attractive on GPUs. Implementing a quantized simulator that has high performance and packs the data tightly for aggressive storage reduction would be extremely labor-intensive and error-prone using a traditional programming language. To make the creation of quantized simulation practical, we have developed a new set of language abstractions and a compilation system. A suite of tailored domain-specific optimizations ensure quantized simulators often run as fast as the full-precision simulators, despite the overhead of encoding-decoding the packed quantized data types. Our programming language and compiler, based on\n            <jats:italic>Taichi<\/jats:italic>\n            , allow developers to effortlessly switch between different full-precision and quantized simulators, to explore the full design space of quantization schemes, and ultimately to achieve a good balance between space and precision. The creation of quantized simulation with our system has large benefits in terms of memory consumption and performance, on a variety of hardware, from mobile devices to workstations with high-end GPUs. We can simulate with levels of resolution that were previously only achievable on systems with much more memory, such as multiple GPUs. For example, on a\n            <jats:italic>single<\/jats:italic>\n            GPU, we can simulate a Game of Life with 20 billion cells (8\u00d7 compression per pixel), an Eulerian fluid system with 421 million active voxels (1.6\u00d7 compression per voxel), and a hybrid Eulerian-Lagrangian elastic object simulation with 235 million particles (1.7\u00d7 compression per particle). At the same time, quantized simulations create physically plausible results. Our quantization techniques are\n            <jats:italic>complementary<\/jats:italic>\n            to existing acceleration approaches of physical simulation: they can be used in combination with these existing approaches, such as sparse data structures, for even higher scalability and performance.\n          <\/jats:p>","DOI":"10.1145\/3450626.3459671","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:27Z","timestamp":1626739467000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["QuanTaichi"],"prefix":"10.1145","volume":"40","author":[{"given":"Yuanming","family":"Hu","sequence":"first","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiafeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanda","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingkuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Kuang","sequence":"additional","affiliation":[{"name":"Taichi Graphics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Dai","sequence":"additional","affiliation":[{"name":"Kuaishou Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William T.","family":"Freeman","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fr\u00e9do","family":"Durand","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073625"},{"key":"e_1_2_2_2_1","unstructured":"Ahmad Abdelfattah Hartwig Anzt Erik G Boman Erin Carson Terry Cojean Jack Dongarra Mark Gates Thomas Gr\u00fctzmacher Nicholas J Higham Sherry Li etal 2020. A survey of numerical methods utilizing mixed precision arithmetic. arXiv preprint arXiv:2007.06674 (2020).  Ahmad Abdelfattah Hartwig Anzt Erik G Boman Erin Carson Terry Cojean Jack Dongarra Mark Gates Thomas Gr\u00fctzmacher Nicholas J Higham Sherry Li et al. 2020. A survey of numerical methods utilizing mixed precision arithmetic. arXiv preprint arXiv:2007.06674 (2020)."},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2930661","article-title":"Perspectives: Why New Programming Languages for Simulation","volume":"35","author":"Bernstein Gilbert Louis","year":"2016","unstructured":"Gilbert Louis Bernstein and Fredrik Kjolstad . 2016 . Perspectives: Why New Programming Languages for Simulation ? ACM Transactions on Graphics (TOG) 35 , 2 (2016), 1 -- 3 . Gilbert Louis Bernstein and Fredrik Kjolstad. 2016. Perspectives: Why New Programming Languages for Simulation? 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A survey on methods and theories of quantized neural networks. arXiv preprint arXiv:1808.04752 ( 2018 ). Yunhui Guo. 2018. A survey on methods and theories of quantized neural networks. arXiv preprint arXiv:1808.04752 (2018)."},{"key":"e_1_2_2_11_1","volume-title":"Proceedings of High Performance Graphics. Eurographics Association, 109--117","author":"Hoetzlein Rama Karl","year":"2016","unstructured":"Rama Karl Hoetzlein . 2016 . GVDB: Raytracing sparse voxel database structures on the GPU . In Proceedings of High Performance Graphics. Eurographics Association, 109--117 . Rama Karl Hoetzlein. 2016. GVDB: Raytracing sparse voxel database structures on the GPU. In Proceedings of High Performance Graphics. 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