GPUOPT: Power-efficient Photonic Network-on-Chip for a Scalable GPU

On-chip photonics is a disruptive technology, and such NoCs are superior to traditional electrical NoCs in terms of latency, power, and bandwidth. Hence, researchers have proposed a wide variety of optical networks for multicore processors. The high bandwidth and low latency features of photonic NoCs have led to the overall improvement in the system performance. However, there are very few proposals that discuss the usage of optical interconnects in Graphics Processor Units (GPUs). GPUs can also substantially gain from such novel technologies, because they need to provide significant computational throughput without further stressing their power budgets. The main shortcoming of optical networks is their high static power usage, because the lasers are turned on all the time by default, even when there is no traffic inside the chip, and thus sophisticated laser modulation schemes are required. Such modulation schemes base their decisions on an accurate prediction of network traffic in the future. In this article, we propose an energy-efficient and scalable optical interconnect for modern GPUs called GPUOPT that smartly creates an overlay network by dividing the symmetric multiprocessors (SMs) into clusters. It furthermore has separate sub-networks for coherence and non-coherence traffic. To further increase the throughput, we connect the off-chip memory with optical links as well. Subsequently, we show that traditional laser modulation schemes (for reducing static power consumption) that were designed for multicore processors are not that effective for GPUs. Hence, there was a need to create a bespoke scheme for predicting the laser power usage in GPUs. Using this set of techniques, we were able to improve the performance of a modern GPU by 45% as compared to a state-of-the-art electrical NoC. Moreover, as compared to competing optical NoCs for GPUs, our scheme reduces the laser power consumption by 67%, resulting in a net 65% reduction in ED2 for a suite of Rodinia benchmarks.