Using Trace Data for Run-Time Optimization of Parallel Execution in Real-Time Multi-Core Systems

In recent years, multi-core processors are becoming more and more common in embedded systems, offering higher performance than single-core processors and thereby enabling both computationally intensive embedded applications as well as the space-, weight-, and energy-efficient integration of software components. However, real-time applications, for which meeting certain deadlines must be guaranteed, do not profit as much from this transition. This is mainly due to interference between the processing cores of commercial-off-the-shelf multi-core processors at shared resources, hampering the predictability of task execution times. An effective approach to avoid this is running the critical tasks exclusively on one core while pausing execution on all other cores. This, however, reduces the overall system efficiency since parallel execution potential remains unused. In this work we present a novel approach to managing shared and exclusive execution in such systems. By on-line observation of the critical task progress via the on-chip trace infrastructure, we reduce the time of exclusive execution when it is safely possible and thereby increase the overall system efficiency. Using trace information allows for early detection of parallelization potential and does not require modifications to the critical application, which helps avoiding re-certification of the critical application. We present an implementation on a heterogeneous multi-processor system-on-chip using a state-of-the-art hypervisor for critical systems and evaluate its performance. Our results indicate that a performance gain of 37 % to 41 % over approaches focused on exclusive execution can be reached in low-interference situations.