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The constant increase in parallelism available on large-scale distributedcomputers poses major scalability challenges to many scientific applications. Acommon strategy to improve scalability is to express the algorithm in terms ofindependent tasks that can be executed concurrently on a runtime system. Inthis manuscript, we consider a generalization of this approach where task-levelspeculation is allowed. In this context, a probability is attached to each taskwhich corresponds to the likelihood that the product of the task will beconsumed as part of the calculation. We consider the problem of optimalresource allocation to each of the possible tasks so as too maximize theexpected overall computational throughput. The power of this approach isdemonstrated by analyzing its application to Parallel Trajectory Splicing, amassively-parallel long-time-dynamics method for atomistic simulations.

Resource allocation for task-level speculative scientific applications: a proof of concept using Parallel Trajectory Splicing