R-DSGD and R-DSGD-M under (δ,c)-robust aggregation have tight Byzantine error floors under (B,ζ)-bounded dissimilarity; local momentum eliminates the stochastic-noise term but not the heterogeneity term.
In IEEE International Symposium on Workload Characterization (IISWC 2009)
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PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
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Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
R-DSGD and R-DSGD-M under (δ,c)-robust aggregation have tight Byzantine error floors under (B,ζ)-bounded dissimilarity; local momentum eliminates the stochastic-noise term but not the heterogeneity term.
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Floating-point autotuning with customized precisions
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
- The EDGE Language: Extended General Einsums for Graph Algorithms