The performance of parallel applications on large scale systems is shown to disproportionately degrade due to interference from system services. This interference from system services is also known as jitter. However, there is limited understanding of sources and patterns of jitter on multi-core systems. In this paper, we identify and characterize jitter sources in terms of their amplitude and execution interval distributions on multi-core IBM Power systems with UNIX-based general purpose operating systems: AIX and Linux. Our analysis shows that there are various kinds of jitter sources and their execution varies drastically between different cores and between hardware threads within each core for practical reasons. This in-depth knowledge of jitter events is leveraged to devise effective approaches to mitigate the jitter impact on application performance in large scale systems. Moreover, such knowledge would provide useful insights to a new generation of operating system designs such as multikernel or satellite kernel for multicore 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.

A performance analysis of Manufacturing Message Specification (MMS) software has been conducted by using the generalized stochastic Petri net (GSPN). The criteria used in the evaluation are average service waiting, processing, and transporting times, and system utilization. The feasibility of using MMS for a robotic platform system is discussed based on the performance evaluation. It is shown that the service arriving rate has a significant effect on several important performance indices, such as the number of waiting services and system utilization. This indicates that MMS performance is quite sensitive to the application environments. Therefore, for real-time control systems with high communication intensity, the time delay through MMS might be unacceptable and some service requests may have to be balked. To solve the problem, one has either to reduce the intensity of message transferring in the system or to simplify MMS communication protocol, or do both.>