Fault-Tolerance for High Performance and Big Data Applications: Theory and Practice
TimeMonday, 18 November 20198:30am - 5pm
DescriptionResilience 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. This tutorial is organized along four main topics:
(i) An overview of failure types (software/hardware, transient/fail-stop), and typical probability distributions (Exponential, Weibull, Log-Normal);
(ii) General-purpose techniques, which include several checkpoint and rollback recovery protocols, replication, prediction, silent error detection and correction;
(iii) Application-specific techniques, such as user-level in-memory checkpointing, data replication (map-reduce) or fixed-point convergence for iterative applications (backpropagation); and
(iv) Practical deployment of fault tolerance techniques with User Level Fault Mitigation (a proposed MPI standard extension).
Relevant examples will include widely used routines such as map-reduce and backpropagation in neural networks. A step-by-step approach will show how to protect these routines and make them fault-tolerant, using a variety of techniques, in a hands-on session.
The tutorial is open to all SC19 attendees who are interested in the current status and expected promise of fault-tolerant approaches for scientific and big data applications. There are no audience prerequisites: background will be provided for all protocols and probabilistic models. However, basic knowledge of MPI will be helpful for the hands-on session.