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The use of approximation is fundamental in computational science. Almost allcomputational methods adopt approximations in some form in order to obtain afavourable cost/accuracy trade-off and there are usually many approximationsthat could be used. As a result, when a researcher wishes to measure a propertyof a system with a computational technique, they are faced with an array ofoptions. Current computational workflow frameworks focus on helping researchersautomate a sequence of steps on a particular platform. The aim is often toobtain a computational measurement of a property. However these frameworks areunaware that there may be a large number of ways to do so. As such, they cannotsupport researchers in making these choices during development or atexecution-time.  We argue that computational workflow frameworks should be designed to be\textit{approximation-aware} - that is, support the fact that a given workflowdescription represents a task that \textit{could} be performed in differentways. This is key to unlocking the potential of computational workflows toaccelerate discovery tasks, particularly those involving searches of largeentity spaces. It will enable efficiently obtaining measurements of entityproperties, given a set of constraints, by directly leveraging the space ofchoices available. In this paper we describe the basic functions that anapproximation-aware workflow framework should provide, how those functions canbe realized in practice, and illustrate some of the powerful capabilities itwould enable, including approximate memoization, surrogate model support, andautomated workflow composition.

Towards an Approximation-Aware Computational Workflow Framework for Accelerating Large-Scale Discovery Tasks