Current GPU programming systems automatically distribute the work on all GPU processors based on a set of fixed assumptions, e. g. that all tasks are independent from each other. We show that automatic distribution limits algorithmic design, and demonstrate that manual work distribution hardly adds any overhead. Our Scan + algorithm is an improved scan relying on manual work distribution. It uses global barriers and task interleaving to provides almost twice the performance of Apple's reference implementation [1].

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System-level development has been dominated by programming languages like C / C++ for decades. These languages are inherently unsafe, error-prone, and a major reason for vulnerabilities. High-level programming languages with a secure memory model and strong type system are able to improve the quality of the system software. In this paper, we explore the programming language Rust for kernel development and present RustyHermit, which is a unikernel completely written in Rust without any C / C++. We show that the support for RustyHermit can be transparently integratable in the Rust toolchain and common Rust applications are build-able on top of RustyHermit. Previously, we developed the C-based unikernel HermitCore with a similar design to RustyHermit and we are able to compare both kernels. We show that the performance of both kernels is similar and only~3.27 % of RustyHermit relies on unsafe code, that cannot be checked by the compiler in detail.

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