We study algorithmic approaches for recovering from the failure of several compute nodes in the parallel preconditioned conjugate gradient (PCG) solver on large-scale parallel computers. In particular, we analyze and extend an exact state reconstruction (ESR) approach, which is based on a method proposed by Chen (2011). In the ESR approach, the solver keeps redundant information from previous search directions, so that the solver state can be fully reconstructed if a node fails unexpectedly. ESR does not require checkpointing or external storage for saving dynamic solver data and has low overhead compared to the failure-free situation. In this paper, we improve the fault tolerance of the PCG algorithm based on the ESR approach. In particular, we support recovery from simultaneous or overlapping failures of several nodes for general sparsity patterns of the system matrix, which cannot be handled by Chen's method. For this purpose, we refine the strategy for how to store redundant information across nodes. We analyze and implement our new method and perform numerical experiments with large sparse matrices from real-world applications on 128 nodes of the Vienna Scientific Cluster (VSC). For recovering from three simultaneous node failures we observe average runtime overheads between only 2.8% and 55.0%. The overhead of the improved resilience depends on the sparsity pattern of the system matrix.

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