A clustering-aware correction algorithm using spatial partitioning and projected gradient descent preserves single-linkage clusters in lossy-compressed particle data while keeping competitive compression ratios.
HACC: Simulating sky surveys on state-of-the-art supercomputing architectures
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cuRAMSES replaces Hilbert-curve domain decomposition with recursive k-section partitioning and adds Morton-key hashing plus spatial binning to cut communication volume and accelerate feedback routines by up to 260x while preserving conservation to 0.5%.
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Preserving Clusters in Error-Bounded Lossy Compression of Particle Data
A clustering-aware correction algorithm using spatial partitioning and projected gradient descent preserves single-linkage clusters in lossy-compressed particle data while keeping competitive compression ratios.
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cuRAMSES: Scalable AMR Optimizations for Large-Scale Cosmological Simulations
cuRAMSES replaces Hilbert-curve domain decomposition with recursive k-section partitioning and adds Morton-key hashing plus spatial binning to cut communication volume and accelerate feedback routines by up to 260x while preserving conservation to 0.5%.