Extreme scale computing: Modeling the impact of system noise in multicore clustered systems

System noise or Jitter is the activity of hardware, firmware, operating system, runtime system, and management software events. It is shown to disproportionately impact application performance in current generation large-scale clustered systems running general-purpose operating systems (GPOS). Jitter mitigation techniques such as co-scheduling jitter events across operating systems improve application performance but their effectiveness on future petascale systems is unknown. To understand if existing co-scheduling solutions enable scalable petascale performance, we construct two complementary jitter models based on detailed analysis of system noise from the nodes of a large-scale system running a GPOS. We validate these two models using experimental data from a system consisting of 128 GPOS instances with 4096 CPUs. Based on our models, we project a minimum slowdown of 2.1%, 5.9%, and 11.5% for applications executing on a similar one petaflop system running 1024 GPOS instances and having global synchronization operations once every 1000 msec, 100 msec, and 10 msec, respectively. Our projections indicate that additional system noise mitigation techniques are required to contain the impact of jitter on multi-petaflop systems, especially for tightly synchronized applications.