AI

Runtime adaptation: a case for reactive code alignment

Abstract

Static alignment techniques are well studied and have been incorporated into compilers in order to optimize code locality for the instruction fetch unit in modern processors. However, current static alignment techniques have several limitations that cannot be overcome. In the exascale era, it becomes even more important to break from static techniques and develop adaptive algorithms in order to maximize the utilization of every processor cycle. In this paper, we explore those limitations and show that reactive realignment, a method where we dynamically monitor running applications, react to symptoms of poor alignment, and adapt alignment to the current execution environment and program input, is more scalable than static alignment. We present fetches-per-instruction as a runtime indicator of poor alignment. Additionally, we discuss three main opportunities that static alignment techniques cannot leverage, but which are increasingly important in large scale computing systems: microarchitectural differences of cores, dynamic program inputs that exercise different and sometimes alternating code paths, and dynamic branch behavior, including indirect branch behavior and phase changes. Finally, we will present several instances where our trigger for reactive realignment may be incorporated in practice, and discuss the limitations of dynamic alignment.