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exanauts
SparseDirectSolver.jl
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Numeric: division-free Cholesky column step in the front kernels
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Ordering: expose METIS nseps as nd_nseps
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symbolic: device construction of the pattern and assembly maps
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bench: GV100 comparison record
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Numeric: division-free Cholesky column step in the front kernels
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Numeric: division-free Cholesky column step in the front kernels
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T22: ordering chooser scores the supernodal schedule depth (issue #108)
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T24: ND partition-tree export/import and canonical supernode postorder (WIP)
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T23 remainder: oneAPI and Metal extensions, allocation-free KA dense fallbacks (#53)
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Regime-C Cholesky reportedly host-synchronizes per front through its info check (blocks stream overlap of wide fronts); verify
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Update-stack placement assumes level-synchronous execution: out-of-level-order kernels (experiment 5) need private contribution blocks or DAG lifetimes
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Ordering chooser picks AMD on large KKT systems: cost model weighs column-etree depth, not supernodal schedule depth (2× schedule depth vs ND)
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Matching-based 2×2 pairs (algo5) cost ~2.1× nnz(L) on MadNLP K2 systems
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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Performance prototypes: partitioned-inverse solve, split/fused factorization, ordering study (experiments 0/5/6)
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