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DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_113@linklings.com
SUMMARY:MeshTaichi: A Compiler for Efficient Mesh-based Operations
DESCRIPTION:Technical Papers\n\nMeshTaichi: A Compiler for Efficient Mesh-
 based Operations\n\nYu, Xu, Kuang, Hu, Liu\n\nMeshes are an indispensable 
 representation in many graphics applications because they provide conforma
 l spatial discretizations. However, mesh-based operations are often slow d
 ue to unstructured memory access patterns. We propose MeshTaichi, a novel 
 mesh compiler that provides an intuitive programming model for efficient m
 esh-based operations. Our programming model hides the complex indexing sys
 tem from users and allows users to write mesh-based operations using refer
 ence-style neighborhood queries. Our compiler achieves its high performanc
 e by exploiting data locality. We partition input meshes and prepare the w
 anted 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 options with computations, so t
 hat users can explore different localized data attributes and different me
 mory orderings without changing their computation code. As a result, users
  can write concise code using our programming model to generate efficient 
 mesh-based computations on both CPU and GPU backends. We test MeshTaichi o
 n a variety of physically-based simulation and geometry processing applica
 tions with both triangle and tetrahedron meshes. MeshTaichi achieves a con
 sistent speedup ranging from 1.4 times to 6 times, compared to state-of-th
 e-art mesh data structures and compilers.\n\nRegistration Category: FULL A
 CCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLangu
 age: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_113&sess=sess15
 3
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