  Background: Large-scale biological jobs on high-performance computing systems
require manual intervention if one or more computing cores on which they
execute fail. This places not only a cost on the maintenance of the job, but
also a cost on the time taken for reinstating the job and the risk of losing
data and execution accomplished by the job before it failed. Approaches which
can proactively detect computing core failures and take action to relocate the
computing core's job onto reliable cores can make a significant step towards
automating fault tolerance.
  Method: This paper describes an experimental investigation into the use of
multi-agent approaches for fault tolerance. Two approaches are studied, the
first at the job level and the second at the core level. The approaches are
investigated for single core failure scenarios that can occur in the execution
of parallel reduction algorithms on computer clusters. A third approach is
proposed that incorporates multi-agent technology both at the job and core
level. Experiments are pursued in the context of genome searching, a popular
computational biology application.
  Result: The key conclusion is that the approaches proposed are feasible for
automating fault tolerance in high-performance computing systems with minimal
human intervention. In a typical experiment in which the fault tolerance is
studied, centralised and decentralised checkpointing approaches on an average
add 90% to the actual time for executing the job. On the other hand, in the
same experiment the multi-agent approaches add only 10% to the overall
execution time.
