Throughput optimization via cache partitioning for embedded multiprocessors

In embedded multiprocessors cache partitioning is a known technique to eliminate inter-task cache conflicts, so to increase predictability. On such systems, the partitioning ratio is a parameter that should be tuned to optimize performance. In this paper we propose a simulated annealing (SA) based heuristic to determine the cache partitioning ratio that maximizes an application's throughput. In its core, the SA method iterates many times over many partitioning ratios, checking the resulted throughput. Hence the throughput of the system has to be estimated very fast, so we utilize a light simulation strategy. The light simulation derives the throughput from tasks' timings gathered off-line. This is possible because in an environment where tasks don't interfere with each other, their performance figures can be used in any possible combination. An application of industrial relevance (H.264 decoder) running on a parallel homogeneous platform is used to demonstrate the proposed method. For the H.264 application 9% throughput improvement is achieved when compared to the throughput obtained using methods of partitioning for the least number of misses. This is a significant improvement as it represents 45% from the theoretical throughput improvement achievable when assuming an infinite cache