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Analytic Rendering and Hardware-Accelerated Simulation for Scientific Applications.

, , , and . Comput. Sci. Eng., 24 (2): 4-6 (2022)

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Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data., , , and . DRBSD@SC, page 11-20. IEEE, (2022)Evaluation of PyTorch as a Data-Parallel Programming API for GPU Volume Rendering., , , , and . EGPGV@EuroVis, page 31-35. Eurographics Association, (2021)AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications., , , , , , , , , and 4 other author(s). SC, page 44:1-44:15. ACM, (2023)Adaptive Configuration of In Situ Lossy Compression for Cosmology Simulations via Fine-Grained Rate-Quality Modeling., , , , , and . HPDC, page 45-56. ACM, (2021)Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations., , , , , , and . IPDPS, page 105-115. IEEE, (2020)Analytic Rendering and Hardware-Accelerated Simulation for Scientific Applications., , , and . Comput. Sci. Eng., 24 (2): 4-6 (2022)Investigating Depth of Field in Volume Rendering and Distributed Volume Rendering on High Performance Computing Systems.. University of Utah, USA, (2016)TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations., , , , , , and . HPDC, page 135-147. ACM, (2022)TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations., , , , , , , and . IEEE Trans. Parallel Distributed Syst., 35 (3): 421-438 (March 2024)TAC+: Drastically Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations., , , , , , , and . CoRR, (2023)