Runtime verification is a monitoring technique to gain assurance about well-being of a program at run time. Most existing approaches use sequential monitors; i.e., when the state of the program with respect to an event of interest changes, the monitor interrupts the program execution, evaluates a set of logical properties, and finally resumes the program execution. In this paper, we propose a GPU-based method for design and implementation of monitors that enjoy two levels of parallelism: the monitor (1) works along with the program in parallel, and (2) evaluates a set of properties in a parallel fashion as well. Our parallel monitoring algorithms effectively exploit the many-core platform available in the GPU. In addition to parallel processing, our approach benefits from a true separation of monitoring and functional concerns, as it isolates the monitor in the GPU. Our method is fully implemented and experimental results show significant reduction in monitoring overhead, monitoring interference, and power consumption due to leveraging the GPU technology.

We introduce the tool RiTHM (Runtime Time-triggered Heterogeneous Monitoring). RiTHM takes a C program under inspection and a set of LTL properties as input and generates an instrumented C program that is verified at run time by a time-triggered monitor. RiTHM provides two techniques based on static analysis and control theory to minimize instrumentation of the input C program and monitoring intervention. The monitor's verification decision procedure is sound and complete and exploits the GPU many-core technology to speedup and encapsulate monitoring tasks.