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Tango: A Cross-layer Approach to Managing I/O Interference over Local Ephemeral Storage
DescriptionAs simulation-based scientific discovery advances to exascale, a major question that the community is striving to answer is how to co-design data storage and complex physics-rich analytics in a way that the time to knowledge can be minimized for post-processing. This paper aims to address the issue of I/O interference for data analytics over local ephemeral storage, which is shared by multiple applications in a non-exclusive node usage scenario. At the core of this work is a coordinated cross-layer approach that reacts to storage interference from both storage and application layers. We evaluated three real-world data analytics, XGC, GenASiS, and CFD, on Chameleon, and quantitatively demonstrated that the I/O performance can be vastly improved, e.g., by 52% versus no adaptivity and 36% versus single layer adaptivity, while maintaining acceptable outcomes of data analysis.
Event Type
Paper
TimeTuesday, 19 November 202411am - 11:30am EST
LocationB308
Tags
Accelerators
Algorithms
Data Compression
I/O, Storage, Archive
Performance Optimization
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