SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

ACM Student Research Competition Poster Archive

Quantify the Effect of Histogram Intersection in Spatio-Temporal Data Sampling

Student: Changfeng Zou (Clemson University)
Supervisor: Jon Calhoun (Clemson University)

Abstract: The computational advance in high-performance computing leads to increased data generation by applications, resulting in a bottleneck within the system due to I/O limitations. One solution is the Spatio-temporal sampling method, which takes advantage of both spatial and temporal data reduction methods to produce higher post-reconstruction quality. Various user input parameters such as the number of bins or histogram intersection limit the performance for Spatio-temporal sampling. This poster focuses on determining the effect of the histogram intersection threshold in the Spatio-temporal sampling method. Results indicate that as long as a data set is not identical across adjacent time-steps, reducing the histogram intersection percentage increases the sampling bandwidth until blocks reused become static. The ExaAM data set shows an increase of 100-130% in sampling bandwidth, with only about a 5% decrease in PSNR value at 60% histogram intersection or lower.

ACM-SRC Semi-Finalist: no

Poster: PDF
Poster Summary: PDF

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