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The increasing complexity of modern configurable systems makes it critical to improve the level of automation in the process of system configuration. Such automation can also improve the agility of the development cycle, allowing for rapid and automated integration of decoupled workflows. In this paper, we present a new framework for automated configuration of systems representable as state machines. The framework leverages model checking and satisfiability modulo theories (SMT) and can be applied to any application domain representable using SMT formulas. Our approach can also be applied modularly, improving its scalability. Furthermore, we show how optimization can be used to produce configurations that are best according to some metric and also more likely to be understandable to humans. We showcase this framework and its flexibility by using it to configure a CGRA memory tile for various image processing applications. 

In recent years, cloud service providers have sold computation in increasingly granular units. Most recently, "serverless" executors run a single executable with restricted network access and for a limited time. The beneft of these restrictions is scale: thousand-way parallelism can be allocated in seconds, and CPU time is billed with sub-second granularity. To exploit these executors, we introduce gg-SAT: an implementation of divide-and-conquer SAT solving. Infrastructurally, gg-SAT departs substantially from previous implementations: rather than handling process or server management itself, gg-SAT builds on the gg framework, allowing computations to be executed on a confgurable backend, including serverless offerings such as AWS Lambda. Our experiments suggest that when run on the same hardware, gg-SAT performs competitively with other D&C solvers, and that the 1000-way parallelism it offers (through AWS Lambda) is useful for some challenging SAT instances.