Zippy: A Framework for Computation and Visualization on a GPU Cluster. Due to its high performance/cost ratio, a GPU cluster is an attractive platform for large scale general-purpose computation and visualization applications. However, the programming model for high performance general- purpose computation on GPU clusters remains a complex problem. In this paper, we introduce the Zippy frame- work, a general and scalable solution to this problem. It abstracts the GPU cluster programming with a two-level parallelism hierarchy and a non-uniform memory access (NUMA) model. Zippy preserves the advantages of both message passing and shared-memory models. It employs global arrays (GA) to simplify the communication, syn- chronization, and collaboration among multiple GPUs. Moreover, it exposes data locality to the programmer for optimal performance and scalability. We present three example applications developed with Zippy: sort-last vol- ume rendering, Marching Cubes isosurface extraction and rendering, and lattice Boltzmann flow simulation with online visualization. They demonstrate that Zippy can ease the development and integration of parallel visualiza- tion, graphics, and computation modules on a GPU cluster.
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References in zbMATH (referenced in 6 articles , 1 standard article )
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- Fan, Zhe; Kuo, Yu-Chuan; Zhao, Ye; Qiu, Feng; Kaufman, Arie; Arcieri, William: Visual simulation of thermal fluid dynamics in a pressurized water reactor (2009) ioport
- Kaufman, Arie; Fan, Zhe; Petkov, Kaloian: Implementing the lattice Boltzmann model on commodity graphics hardware (2009)
- Zhao, Hanli; Jin, Xiaogang; Lu, Shufang; Mao, Xiaoyang; Shen, Jianbing: AtelierM++: a fast and accurate marbling system (2009) ioport
- Fan, Zhe; Qiu, Feng; Kaufman, Arie E.: Zippy: a framework for computation and visualization on a GPU cluster (2008) ioport