Hardware-Assisted Online Data Race Detection

Abstract

Dynamic data race detection techniques usually involve invasive instrumentation that makes it impossible to deploy an executable with such checking in the field, hence making errors difficult to debug and reproduce. This paper shows how to detect data races using the COEMS technology through continuous online monitoring with low-impact instrumentation on a novel FPGA-based external platform for embedded multicore systems. It is used in combination with formal specifications in the high-level stream-based temporal specification language TeSSLa, in which we encode a lockset-based algorithm to indicate potential race conditions. We show how to instantiate a TeSSLa template that is based on the Eraser algorithm, and present a corresponding light-weight instrumentation mechanism that emits necessary observations to the FPGA with low overhead. We illustrate the feasibility of our approach with experimental results on detection of data races on a sample application.