An executable analytical performance evaluation approach for early performance prediction

Percolation has recently been proposed as a key component of an advanced program execution model for future generation high-end machines featuring adaptive data/code transformation and movement for effective latency tolerance. An early evaluation of the performance effect of percolation is very important in the design space exploration of future generations of supercomputers. In this paper, we develop an executable analytical performance model of a high performance multithreaded architecture that supports percolation. A novel feature of our approach is modeling interactions between software (program) and hardware (architecture) components. We solve the analytical model using a queuing simulation tool enriched with synchronization. The proposed approach is effective and facilitates obtaining performance trends quickly. Our results indicate that percolation brings in significant performance gains (by a factor of 2.7 to 11). Further, our results reveal that percolation and multithreading can complement each other.