Accurate, scalable and informative design space exploration for large and sophisticated multi-core oriented architectures

As microprocessors become more complex, early design space exploration plays an essential role in reducing the time to market and post-silicon surprises. The trend toward multi-/many- core processors will result in sophisticated large-scale architecture substrates (e.g. non-uniformly accessed caches interconnected by network-on-chip) that exhibit increasingly complex and heterogeneous behavior. While conventional analytical modeling techniques can be used to efficiently explore the characteristics (e.g. IPC and power) of monolithic architecture design, existing methods lack the ability to accurately and informatively forecast the complex behavior of large and distributed architecture substrates across the design space. This limitation will only be exacerbated with the rapidly increased integration scale (e.g. number of cores per chip). In this paper, we propose novel, multi-scale 2D predictive models which can efficiently reason the characteristics of large and sophisticated multi-core oriented architectures during the design space exploration stage without using detailed cycle-level simulations. Our proposed techniques employ 2D wavelet multiresolution analysis and neural network regression modeling. We extensively evaluate the efficiency of our predictive models in forecasting the complex and heterogeneous characteristics of large and distributed shared cache interconnected by a network on chip in multi-core designs using both multi-programmed and multithreaded workloads. Experimental results show that the models achieve high accuracy while maintaining low complexity and computation overhead. Through case studies, we demonstrate that the proposed techniques can be used to informatively explore and accurately evaluate global, cooperative multi-core resource allocation and thermal-aware designs that cannot be achieved using conventional design exploration methods.