Extreme scale parallel computing systems will have tens of thousands of optionally accelerator-equipped nodes with hundreds of cores each, as well as deep memory hierarchies and complex interconnect topologies. Such exascale systems will provide hardware parallelism at multiple levels and will be energy constrained. Their extreme scale and the rapidly deteriorating reliability of their hardware components means that exascale systems will exhibit low meantime -betweenfailure values. Furthermore, existing programming models already require heroic programming and optimization efforts to achieve high efficiency on current supercomputers. Invariably, these efforts are platform-specific and non-portable. In this article, we explore the shortcomings of existing programming models and runtimes for large-scale computing systems. We propose and discuss important features of programming paradigms and runtimes to deal with exascale computing systems with a special focus on data-intensive applications and resilience. Finally, we discuss code sustainability issues and propose several software metrics that are of paramount importance for code development for ultrascale computing systems.

In this paper, we introduce a novel approach to optimizing the control of systems that can be modeled as Markov decision processes (MDPs) with a threshold-based optimal policy. Our method is based on a specific type of genetic program known as symbolic regression (SR). We present how the performance of this program can be greatly improved by taking into account the corresponding MDP framework in which we apply it.The proposed method has two main advantages: (1) it results in near-optimal decision policies, and (2) in contrast to other algorithms, it generates closed-form approximations. Obtaining an explicit expression for the decision policy gives the opportunity to conduct sensitivity analysis, and allows instant calculation of a new threshold function for any change in the parameters. We emphasize that the introduced technique is highly general and applicable to MDPs that have a threshold-based policy. Extensive experimentation demonstrates the usefulness of the method. CCS CONCEPTS• Mathematics of computing → Stochastic processes.

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