A job scheduling design for visualization services using GPU clusters

Wei Hsien Hsu, Chun Fu Wang, Kwan-Liu Ma, Hongfeng Yu, Jacqueline H. Chen

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations


Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.

Original languageEnglish (US)
Number of pages11
StatePublished - Dec 12 2012
Event2012 IEEE International Conference on Cluster Computing, CLUSTER 2012 - Beijing, China
Duration: Sep 24 2012Sep 28 2012


Other2012 IEEE International Conference on Cluster Computing, CLUSTER 2012


  • GPU clusters
  • job scheduling
  • multi-user volume rendering
  • parallel volume visualizatoin

ASJC Scopus subject areas

  • Software


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