Parallel Kalman Filtering on the Connection Machine

A parallel algorithm for square root Kalman filtering is developed and implemented on the Connection Machine (CM). The algorithm makes efficient use of parallel prefix or scan operations which are primitive instructions in the CM. Performance measurements show that the CM filter runs in time linear in the state vector size. This represents a great improvement over serial implementations which run in cubic time. A specific multiple target tracking application is also considered, in which several targets (e.g., satellites, aircrafts and missiles) are to be tracked simultaneously, each requiring one or more filters. A parallel algorithm is developed which, for fixed size filters, runs in constant time, independent of the number of filters simultaneously processed

A Practical Schedulability Analysis for Generalized Sporadic Tasks in Distributed Real-Time Systems

Existing off-line schedulability analysis for real-time systems can only handle periodic or sporadic tasks with known minimum inter-arrival times. Modeling sporadic tasks with fixed minimum inter-arrival times is a poor approximation for systems in which tasks arrive in bursts, but have longer intervals between the bursts. In such cases, schedulability analysis based on the existing sporadic task model is pessimistic and seriously overestimates the task\u27s time demand. In this paper, we propose a generalized sporadic task model that characterizes arrival times more precisely than the traditional sporadic task model, and we develop a corresponding schedulability analysis that computes tighter bounds on worst-case response times. Experimental results show that when arrival time jitter increases, the new analysis more effectively guarantees schedulability of sporadic tasks

Practical Schedulability Analysis for Generalized Sporadic Tasks in Distributed Real-Time Systems

Existing off-line schedulability analysis for real-time systems can only handle periodic or sporadic tasks with known minimum inter-arrival times. Modeling sporadic tasks with fixed minimum inter-arrival times is a poor approximation for systems in which tasks arrive in bursts, but have longer intervals between the bursts. In such cases, schedulability analysis based on the existing sporadic task model is pessimistic and seriously overestimates the task\u27s time demand. In this paper, we propose a generalized sporadic task model that characterizes arrival times more precisely than the traditional sporadic task model, and we develop a corresponding schedulability analysis that computes tighter bounds on worst-case response times. Experimental results show that when arrival time jitter increases, the new analysis more effectively guarantees schedulability of sporadic tasks