Title
Load Balanced Parallel GPU Out-of-Core for Continuous LOD Model Visualization
Abstract
Rendering massive 3D models has been recognized as a challenging task. Due to the limited size of GPU memory, a massive model with hundreds of millions of primitives cannot fit into most of modern GPUs. By applying parallel Level-Of-Detail (LOD), as proposed in [1], transferring only a portion of primitives rather than the whole to the GPU is sufficient for generating a desired simplified version of the model. However, the low bandwidth in CPU-GPU communication make data-transferring a very time-consuming process that prevents users from achieving high-performance rendering of massive 3D models on a single-GPU system. This paper explores a device-level parallel design that distributes the workloads in a multi-GPU multi-display system. Our multi-GPU out-of-core uses a load-balancing method and seamlessly integrates with the parallel LOD algorithm. Our experiments show highly interactive frame rates of the “Boeing 777” airplane model that consists of over 332 million triangles and over 223 million vertices.
Year
DOI
Venue
2012
10.1109/SC.Companion.2012.37
SC Companion
Keywords
Field
DocType
million vertex,continuous lod model visualization,graphics processing unit,massive model,gpu memory,multi-gpu multi-display system,parallel gpu out-of-core,parallel level-of,airplane model,massive 3d model,device-level parallel design,parallel lod algorithm,level-of-detail,graphics processing units,high-performance rendering,resource allocation,rendering (computer graphics),boeing 777 airplane model,parallel algorithms,data visualisation,parallel gpu,rendering,solid modelling,million triangle,load balancing,data transfer,cpu-gpu communication
Data visualization,Visualization,CUDA,Parallel algorithm,Computer science,Load balancing (computing),Parallel computing,Out-of-core algorithm,Rendering (computer graphics),OpenGL
Conference
ISBN
Citations 
PageRank 
978-1-4673-6218-4
3
0.41
References 
Authors
16
3
Name
Order
Citations
PageRank
Chao Peng1294.43
Mi Peng291.24
Yong Cao36810.33