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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T170000
DTEND;TZID=Asia/Seoul:20221208T183000
UID:siggraphasia_SIGGRAPH Asia 2022_sess171_papers_113@linklings.com
SUMMARY:MeshTaichi: A Compiler for Efficient Mesh-based Operations
DESCRIPTION:Technical Communications, Technical Papers\n\nMeshTaichi: A Co
 mpiler for Efficient Mesh-based Operations\n\nYu, Xu, Kuang, Hu, Liu\n\nMe
 shes are an indispensable representation in many graphics applications bec
 ause they provide conformal spatial discretizations. However, mesh-based o
 perations are often slow due to unstructured memory access patterns. We pr
 opose MeshTaichi, a novel mesh compiler that provides an intuitive program
 ming model for efficient mesh-based operations. Our programming model hide
 s the complex indexing system from users and allows users to write mesh-ba
 sed operations using reference-style neighborhood queries. Our compiler ac
 hieves its high performance by exploiting data locality. We partition inpu
 t meshes and prepare the wanted relations by inspecting users' code during
  compile time. During run time, we further utilize on-chip memory (shared 
 memory on GPU and L1 cache on CPU) to access the wanted attributes of mesh
  elements efficiently. Our compiler decouples low-level optimization optio
 ns with computations, so that users can explore different localized data a
 ttributes and different memory orderings without changing their computatio
 n code. As a result, users can write concise code using our programming mo
 del to generate efficient mesh-based computations on both CPU and GPU back
 ends. We test MeshTaichi on a variety of physically-based simulation and g
 eometry processing applications with both triangle and tetrahedron meshes.
  MeshTaichi achieves a consistent speedup ranging from 1.4 times to 6 time
 s, compared to state-of-the-art mesh data structures and compilers.\n\nReg
 istration Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\n
 Format: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_113&sess=sess17
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