Iterative Probabilistic Performance Prediction for Multi-Application Multiprocessor Systems
Modern embedded devices are increasingly becoming multiprocessor with the need to support a large number of applications to satisfy the demands of users. Due to a huge number of possible combinations of these multiple applications, it becomes a challenge to predict their performance. This becomes even more important when applications may be dynamically started and stopped in the system. Since modern embedded systems allow users to download and add applications at run-time, a complete design-time analysis is not always possible. This paper presents a new technique to accurately predict the performance of multiple applications mapped on a multiprocessor platform.