Building a full system simulator for a CPU-GPU heterogeneous architecture recently draws keen attention of mobile device developers who want to run full software stacks without modification. A common practice is to integrate a GPU simulator with a CPU simulator, which runs very slow or is not applicable when the GPU simulator does not exist. To overcome these drawbacks, a HIL (Hardware-in-the Loop) simulation approach that integrates a real GPU with a CPU simulator has been proposed recently, in which HIL simulation is performed at the device driver level, supporting a specific GPU only. For design space exploration of CPU-GPU heterogeneous architecture, it is necessary to support various GPUs. To this end, we propose a GPU-HIL simulation technique that integrates a real GPU at the OpenGL ES API level, aiming to make a good compromise between speed and timing accuracy. Technical challenges and proposed solutions are presented in detail. Using three OpenGL ES Android benchmarks, preliminary experiments show some use cases of the proposed simulation framework for design space exploration and API-level dynamic behavior monitoring.

Multi-core CPU/GPU heterogeneous platforms became popular in embedded systems. A full system simulator is typically used to observe the internal system behavior by running complete software stacks without modification on simulation models of CPUs and other devices in the system. However, there are few known full system simulators for CPU/GPU heterogeneous platforms and existent GPU simulators are prohibitively slow for running application software. In this paper, we propose a hardware-in-the-loop simulation technique that integrates GPU hardware into a full system simulator. A novel interfacing mechanism between CPU simulator and the development board, where GPU hardware is integrated, is devised. In the experiments, we took Exynos 4412 as a case study, where gem5 simulator is used to simulate mainly a quad-core ARM CPU in the platform and an Exynos development board is used to run the Mali GPU hardware. We could successfully run Android apps on the proposed hardware-in-the-loop simulation framework with up to 1.5 M cycles per second performance.