Automated software testing of memory performance in embedded GPUs

Embedded and real-time software is often constrained by several temporal requirements. Therefore, it is important to design embedded software that meets the required performance goal. The inception of embedded graphics processing units (GPUs) brings fresh hope in developing high-performance embedded software which were previously not suitable for embedded platforms. Whereas GPUs use massive parallelism to obtain high throughput, the overall performance of an application running on embedded GPUs is often limited by memory performance. Therefore, a crucial problem lies in automatically detecting the inefficiency of such software developed for embedded GPUs. In this paper, we propose GUPT, a novel test generation framework that systematically explores and detects poor memory performance of applications running on embedded GPUs. In particular, we systematically combine static analysis with dynamic test generation to expose likely execution scenarios with poor memory performance. Each test case in our generated test suite reports a potential memory-performance issue, along with the detailed information to reproduce the same. We have implemented our test generation framework using GPGPU-Sim, a cycle-accurate simulator and the LLVM compiler infrastructure. We have evaluated our framework for several open-source programs. Our experiments suggest the efficacy of our framework by exposing numerous memory-performance issues in a reasonable time. We also show the usage of our framework in improving the performance of programs for embedded GPUs.