GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications
- University of St. Thomas
- ORNL
- Argonne National Laboratory (ANL)
Visualizations of large-scale data sets are often created on graphics clusters that distribute the rendering task amongst many processes. When using real-time GPU-based graphics algorithms, the most time-consuming aspect of distributed rendering is typically the com-positing phase - combining all partial images from each rendering process into the final visualization. Compo siting requires image data to be copied off the GPU and sent over a network to other processes. While compression has been utilized in existing distributed rendering compositors to reduce the data being sent over the network, this compression tends to occur after the raw images are transferred from the GPU to main memory. In this paper, we present work that leverages OpenGL / CUDA interoperability to compress raw images on the GPU prior to transferring the data to main memory. This approach can significantly reduce the device-to-host data transfer time, thus enabling more efficient compositing of images generated by distributed rendering applications.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1845790
- Resource Relation:
- Conference: IEEE Symposium on Large Data Analysis and Visualization (LDAV) - New Orleans, Louisiana, United States of America - 10/25/2021 6:00:00 AM-
- Country of Publication:
- United States
- Language:
- English
Similar Records
Sort-First, Distributed Memory Parallel Visualization and Rendering
Computational Benefit of GPU Optimization for the Atmospheric Chemistry Modeling