SiGAMMA

Abstract

In embedded systems, CPUs and GPUs typically share main memory. The resulting memory contention may significantly inflate the duration of CPU tasks in a hard-to-predict way. Despite initial solutions have been devised to control this undesired inflation, these approaches do not consider the interference due to memory-intensive components in COTS embedded systems like integrated Graphical Processing Units. Dealing with this kind of interference might require custom-made hardware components that are not integrated in off-the-shelf platforms. We address these important issues by proposing a memory-arbitration mechanism, SiGAMMA (SiΓ), for eliminating the interference on CPU tasks caused by conflicting memory requests from the GPU. Tasks on the CPU are assumed to comply with a prefetch-based execution model (PREM) proposed in the real-time literature, while memory accesses from the GPU are arbitrated through a predictable mechanism that avoids contention. Our experiments show that SiΓ proves to be very effective in guaranteeing almost null inflation to memory phases of CPU tasks, while at the same time avoiding excessive starvation of GPU tasks.