Abstract

KARYON, a kernel-based architecture for safety-critical control, is a European project that proposes a new perspective to improve performance of smart vehicle coordination focusing on Advanced Driver Assistance Systems (ADASs) and Unmanned Aerial Systems (UAS). The key objective is to provide system solutions for predictable and safe coordination of smart vehicles that autonomously cooperate and interact in an open and inherently uncertain environment. Currently, these systems are not allowed to operate on the public roads or in the air space, as the risk of causing severe damage cannot be excluded with sufficient certainty. The impact of the project is two-fold; it will provide improved vehicle density without driver involvement and increased traffic throughput to maintain mobility without a need to build new traffic infrastructures. The results will improve interaction in cooperation scenarios while preserving safety and assessing it according to standards. The prospective project results include self-stabilizing algorithms for vehicle coordination, communication and synchronization. In addition, we aim at showing that the safety kernel can be designed to be a self-stabilizing one. Document type: Part of book or chapter of book

Abstract

We conducted a feasibility study to research modifications to data-flow architectures to enable data-flow to be distributed across multiple machines automatically. Distributed data-flow is a crucial technology to ensure that tools like the VisIt visualization application can provide in-situ data analysis and post-processing for simulations on peta-scale machines. We modified a version of VisIt to study load-balancing trade-offs between light-weight kernel compute environments and dedicated post-processing cluster nodes. Our research focused on memory overheads for contouring operations, which involves variable amounts of generated geometry on each node and computation of normal vectors for all generated vertices. Each compute node independently decided whether to send data to dedicated post-processing nodes at each stage of pipeline execution, depending on available memory. We instrumented the code to allow user settable available memory amounts to test extremely low-overhead compute environments. We performed initial testing of this prototype distributed streaming framework, but did not have time to perform scaling studies at and beyond 1000 compute-nodes. Document type: Report