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This work investigates GPU-resident lossy compression, using representative fields from the DYAMOND high-resolution operational dataset as input, and combining measured GPU compression throughput with SimGrid network simulation, and shows that ZFP at 16 bits per value (rate-16), about the same storage budget as float16, matches the communication-time reduction of float16 truncation.
Forget perplexity – ZipCal uses Zipf's law to curate calibration data for LLM compression, matching state-of-the-art performance at 240x the speed.
