Dynamic optimization has been proposed to overcome many limitations of static optimization, such as inaccurate assumptions about the underlying processor architecture and lack of adaption to the program’s runtime behavior. However, existing dynamic optimization systems often impose high runtime overhead (software systems) or great hardware complexity (hardware systems), and only have limited runtime adaptability. This thesis proposes a new model of optimization, where optimization is triggered by hardware optimization events and is performed concurrently on an application while it is running. We introduce an event-driven multithreaded dynamic optimization architecture, called Trident. Trident is a software/hardware solution which strives to...