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With the rapid growth of multicore processors, memory optimisation is necessary in improving the usage of cache memories. This includes efforts in improving the performance of data fetching from memory. Current prefetching algorithms depend on usage, such that items, which are not frequently used, will be removed. This can cause potential delay when infrequent items are removed, even when they are needed by many other processors in the near future. To alleviate this limitation, the hierarchical analytical scheduling (HAS) model is proposed in this paper. The model works by determining the relative importance of data during cache replacement policy. HAS is based on the concept of hierarchical temporal memory (HTM), in which the scheduling is derived on the priority and similarity of data from the aspect of space and time. Implemented with OCTAVE, a simulation of the model is implemented to analyse its behaviour in a hypothetical cache management scenario.

With the rapid growth of multicore processors, memory optimisation is necessary in improving the usage of cache memories. This includes efforts in improving the performance of data fetching from memory. Current prefetching algorithms depend on usage, such that items, which are not frequently used, will be removed. This can cause potential delay when infrequent items are removed, even when they are needed by many other processors in the near future. To alleviate this limitation, the hierarchical analytical scheduling (HAS) model is proposed in this paper. The model works by determining the relative importance of data during cache replacement policy. HAS is based on the concept of hierarchical temporal memory (HTM), in which the scheduling is derived on the priority and similarity of data from the aspect of space and time. Implemented with OCTAVE, a simulation of the model is implemented to analyse its behaviour in a hypothetical cache management scenario.