Resilience is a critical issue for large-scale platforms. This tutorial provides a comprehensive survey of fault-tolerant techniques for high-performance and big-data applications, with a fair balance between theory and practice.
The tutorial will include an overview of failure types and typical probability distributions, general-purpose techniques: checkpoint and rollback recovery protocols, replication, prediction and silent error detection, application-specific techniques: user-level in-memory checkpointing, data replication (map-reduce) or fixed-point convergence for iterative applications (back-propagation): practical deployment of fault tolerance techniques with User Level Fault Mitigation (a proposed MPI standard extension). Examples: Monte-Carlo methods, SPMD stencil, map-reduce and back-propagation in neural networks. A step-by-step hands-on approach shows how to protect these routines.
The tutorial is open to all SC20 attendees who are interested in the current status and expected promise of fault-tolerant approaches for scientific and big data applications. No audience prerequisites: background will be provided for all protocols and probabilistic models.