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ABSTRACT Whether a microbe is free-living or associated with a host from across the tree of life, its existence depends on a limited number of elements and electron donors and acceptors. Yet divergent approaches have been used by investigators from different fields. The “environment first” research tradition emphasizes thermodynamics and biogeochemical principles, including the quantification of redox environments and elemental stoichiometry to identify transformations and thus an underlying microbe. The increasingly common “microbe first” research approach benefits from culturing and/or DNA sequencing methods to first identify a microbe and encoded metabolic functions. Here, the microbe itself serves as an indicator for environmental conditions and transformations. We illustrate the application of both approaches to the study of microbiomes and emphasize how both can reveal the selection of microbial metabolisms across diverse environments, anticipate alterations to microbiomes in host health, and understand the implications of a changing climate for microbial function. 

We present a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable C++ parallel programming model that is embodied in the HPX library and being incorporated into the ISO C++ standard. The model represents a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center’s Cori system (658,784 Intel Knight’s Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.