Taming extreme heterogeneity via machine learning based design of autonomous manycore systems

Abstract

To avoid rewriting software code for new computer architectures and to take advantage of the extreme heterogeneous processing, communication and storage technologies, there is an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. To enable both programmability and flexibility in the heterogeneous computing era, we propose a novel complex network inspired model of computation and efficient optimization algorithms for determining the optimal degree of parallelization from old software code. This mathematical framework allows us to determine the required number and type of processing elements, the amount and type of deep memory hierarchy, and the degree of reconfiguration for the communication infrastructure, thus opening new avenues to performance and energy efficiency. Our framework enables heterogeneous manycore systems to autonomously adapt from traditional switching techniques to network coding strategies in order to sustain on-chip communication in the order of terabytes. While this new programming model enables the design of selfprogrammable autonomous heterogeneous manycore systems, a number of open challenges will be discussed.

An Efficient Task Mapping for Manycore Systems

“…As a promising interconnect infrastructure, NoC has a significant impact on mitigating the challenges related to onchip communication and heterogeneity of cores. However, the full potential of MCS can be realized only if the applications are suitably parallelized considering task dependencies as well as the timing and power constraints [2]- [6]. Applications can be either converted from an existing sequential form or written to be executed in parallel from scratch.…”