Presentation
SIGN IN TO VIEW THIS PRESENTATION Sign In
Sensitivity of Black-Box Statistical Prediction of Lossy Compression Ratios for 3D Scientific Data
SessionPoster Reception
DescriptionCompression ratio estimation is an important optimization of I/O workflows processing terabytes of data. Applications such as compression auto-tuning or lossy compressor selection require a high-throughput, accurate estimation. Prior works that utilize sampling are fast but inaccurate, while approaches which do not use sampling are highly accurate but slow. Through sensitivity analysis we show that sampling a small number of moderately sized data blocks maintains fast data transfer and yields superior estimation accuracy compared to existing sampling approaches, and we use this to construct a new fast and accurate sampling method. In relation to non-sampling techniques, our method results in less than 10% degradation in estimation accuracy, while still maintaining the high throughput of the less accurate sampling methods.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
Research Posters
Scientific Visualization & Data Analytics Showcase
TimeTuesday, 14 November 20235:15pm - 7pm MST
LocationDEF Concourse
TP