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Establishing Best Practices for Applying Inline Compressed Arrays to Improve Performance in HPC
DescriptionHPC applications require massive amounts of memory to process large datasets. While data compression is used to avoid bottlenecks in transmission and storage, it is still necessary to decompress this data into memory to use it. Inline compressed arrays (ICA) is a method which keeps the data compressed in application memory, decompressing blocks of data as needed. The goal is to reduce the memory footprint of big-data applications, allowing them to run on more abundant HPC nodes with less DRAM. This research uses matrix multiplication as a lens for analyzing the effects of various ICA parameters on runtime and memory usage. We construct a model for a minimum number of compressor calls needed to complete the computation, and show how careful tuning of ICA parameters achieves this minimum. Finally, we briefly discuss how our lessons learned impact other computational kernels.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX