Exploring the Multitude of Real Time Multi GPU Configurations
Motivated by computational capacity and power efficiency, techniques for integrating graphics processing units (GPUs) into real time systems have become an active area of research. While much of this work has focused on single GPU systems, multiple GPUs may be used for further benefits. Similar to CPUs in multiprocessor systems, GPUs in multi GPU systems may be managed using partitioned, clustered, or global methods, independent of CPU organization. This gives rise to many combinations of CPU/GPU organizational methods that, when combined with additional GPU management options, results in thousands of "reasonable" configuration choices. In this paper, we explore real time schedulability of several categories of configurations for multiprocessor, multi GPU systems that are possible under GPUSync, a recently proposed highly configurable real time GPU management framework. Our analysis includes the careful consideration of GPU related overheads. We show system configuration strongly affects real time schedulability. We also identify which configurations offer the best schedulability in order to guide the implementation of GPU based real time systems and future research.
2014 IEEE Real Time Systems Symposium