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Compilers are generally not aware of the semantics of library-based parallel programming models such as MPI and OpenSHMEM, and hence are unable to detect programming errors related to their use. To alleviate this issue, we developed a custom static checker for OpenSHMEM programs based on LLVM’s Clang Static Analyzer framework (CSA). We leverage the Symbolic Execution engine of the core Static Analyzer framework and its path-sensitive analysis to check for bugs on all OpenSHMEM program paths. We have identified common programming mistakes in OpenSHMEM programs that are detectable at compile-time and provided checks for them in the analyzer. They cover: utilization of the right type of mem- ory (private vs. symmetric memory); safe/synchronized access to program data in the presence of asynchronous, one-sided communication; and double-free of memories allocated using OpenSHMEM memory allocation routines. Our experimental analysis showed that the static checker successfully detects bugs in OpenSHMEM code. 

Microfluidic gradient generators are used to study the movement of living cells, lipid vesicles, and colloidal particles in response to spatial variations in their local chemical environment. Such gradient driven motions are often slow (less than 1 μm s −1 ) and therefore influenced or disrupted by fluid flows accompanying the formation and maintenance of the applied gradient. Even when external flows are carefully eliminated, the solute gradient itself can drive fluid motions due to combinations of gravitational body forces and diffusioosmotic surface forces. Here, we develop a microfluid gradient generator based on the in situ formation of biopolymer membranes and quantify the fluid flows induced by steady solute gradients. The measured velocity profiles agree quantitatively with those predicted by analytical approximations of relevant hydrodynamic models. We discuss how the speed of gradient-driven flows depends on system parameters such as the gradient magnitude, the fluid viscosity, the channel dimensions, and the solute type. These results are useful in identifying and mitigating undesired flows within microfluidic gradient systems.