Understanding Error Sensitivity in Checkpointing for Linear System Solvers

Fault tolerance in large-scale iterative solvers is critical, yet traditional checkpointing methods often impose significant storage overhead. In this study, we evaluate the compression error introduced from lossy compression in the Conjugate Gradient method. We systematically investigate how various compressor configurations such as numerical error bounds, error modes, and prediction algorithms influence compressed checkpoint size, compression error, and extra iterations after recovery. Our analysis reveals key trade-offs between storage efficiency and recovery overhead.