As multiprocessor systems-on-chip become a reality, performance modeling becomes a challenge. To quickly evaluate many architectures, some type of high-level simulation is required, including high-level cache simulation. We propose to perform this cache simulation by defining a metric to represent memory behavior independently of cache structure and back-annotate this into the original application. While the annotation phase is complex, requiring time comparable to normal address trace based simulation, it need only be performed once per application set and thus enables simulation to be sped up by a factor of 20 to 50 over trace based simulation. This is important for embedded systems, as software is often evaluated against many input sets and many architectures. Our results show the technique is accurate to within 20% of miss rate for uniprocessors and was able to reduce the die area of a multiprocessor chip by a projected 14% over a naive design by accurately sizing caches for each processor.
As System On a Chip (SoC) designs become more like Programmable Heterogeneous Multiprocessors (PHMs), the highest levels of design will place emphasis on the custom design of elements that were traditionally associated with systems in the large. We motivate how schedulers that make dynamic, datadependent decisions at run-time will be key design elements in PHM SoCs. Starting from a fundamental model, the role schedulers play in PHMs is developed. Model-based scheduling is introduced as an approach to designing schedulers that optimize a PHM's performance. Due to the complexity of the PHM design space, convergence on optimal design requires high-level modeling and simulation. In model-based scheduling, high-level models of scheduling decisions result in actual design elements that appear in real systems. Experiments for a simple two-processor PHM that does a mix of image and text compression are included. Results show the effectiveness of model-based scheduling.
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