Synthesis of reconfigurable high-performance multicore systems

Reconfigurable high-performance computing systems (RHPC) have been attracting more and more attention over the past few years. RHPC systems are a promising solution for accelerating system performance, lowering power consumption and minimizing operation cost. In order to achieve high performance on this hybrid system, it is important to effectively explore the design space, which includes accelerator synthesis, resource allocation and job scheduling. In this paper we propose novel algorithms for reconfigurable resource allocation and job scheduling to optimize performance of multicore RHPC systems. Specifically, we first propose an interesting approximation algorithm to assign jobs to processors with consideration of coprocessors at the global optimization step. Then we present an optimal solution for coprocessor selection in the local optimization step. In this paper we also demonstrate that designers can quickly explore a large number of accelerator design choices with the help of high-level synthesis tools. Experiments show that our proposed techniques provide efficient solutions for real-life benchmarks and generate higher quality of results. When compared to other heuristic algorithms, our results can achieve up to 47% performance improvement.