Space Computing using COTS Heterogeneous Platforms : Intelligent On-Board Data Processing in Space Systems

Abstract: Space computing enriches space activities such as deep-space explorations and in-orbit intelligent decision making. The awareness of space computing is growing due to the technological advances of high-performance commercial off-the-shelf (COTS) computing platforms. Space offers a complex, constrained and challengeable environment to the developers, researchers, as well as human beings. The challenges are size, weight and power (SWaP) constraints, real-time requirements, communication limitations as well as radiation effects. The research conducted in this thesis aims at investigating and supporting intelligent on-board data processing using COTS heterogeneous computing platforms in space systems. These platforms embed at least one Central Processing Unit (CPU) and one Graphics Processing Unit (GPU) on the same chip. The main goal of the research presented in this thesis is twofold. First, to investigate the heterogeneous computing platforms for the purpose of proposing a solution to tackle the above-mentioned challenges in space systems. Second, to complement the proposed solution with novel scheduling techniques for real-time applications that run on COTS heterogeneous platforms under harsh environments like space.The proposed techniques are based on the system model that considers the use of alternative executions of parallel segments of tasks. Although offloading a parallel segment to a parallel computation unit (such as GPU) improves the best-case execution times of most applications, it can increase the response times of tasks in some applications due to the overuse of GPU. Hence, the use of the proposed task model can be a key to decrease the response times of tasks and improve schedulability of the system. The server-based proposed scheduling techniques support the proposed task model by guaranteeing the execution slot for parallel segments on CPU(s). The experimental evaluation conducted in this thesis shows that the proposed task model can improve the schedulability of the real-time systems up to 90% with the static allocation of applications. Moreover, the dynamic allocation method using the server-based scheduling with the proposed task model can improve the schedulability up to 16%. Finally, the thesis presents a simulation tool that simulates real-time applications using the proposed task model while considering the different levels of radiation tolerance to different processing units.