Exploring High-Throughput Computing Paradigm for Global Routing

With aggressive technology scaling, the complexity of the global routing problem is poised to grow rapidly. Solving such a large computational problem demands a high-throughput hardware platform such as modern graphics processing units (GPUs). In this paper, we explore a hybrid GPU-CPU high-throughput computing environment as a scalable alternative to the traditional CPU-based router. We introduce net-level concurrency (NLC), which is a novel parallel model for router algorithms and aims to exploit concurrency at the level of individual nets. To efficiently uncover NLC, we design a scheduler to create groups of nets that can be routed in parallel. At its core, our scheduler employs a novel algorithm to dynamically analyze data dependencies between multiple nets. We believe such an algorithm can lay the foundation for uncovering data-level parallelism in routing, which is a necessary requirement for employing high-throughput hardware. Detailed simulation results show an average of 4× speedup over NTHU-Route 2.0 with negligible loss in solution quality. To the best of our knowledge, this is the first work on utilizing GPUs for global routing.