UC Davis

Data movement has become a limiting factor in terms of performance, power consumption, and scalability of high-performance compute nodes with increasing numbers of processor and memory systems. Optical interconnects enabled by Silicon Photonics could not only overcome this limitation but also change the way we think about system architectures and memory hierarchies. This dissertation aims to introduce and evaluate scalable high performance computing architectures based on optical interconnects. This dissertation presents the motivation and background, architecture design, and evaluation results for the following case studies:Investigating the design challenges in large-scale many-core processors, the impact of interconnection fabric on the overall system performance and power consumption, and how Silicon Photonics can alleviate system constraints. Studying on-chip memory networks capable of providing HPC compute nodes with terabytes of memory capacity by interconnecting several 3D stacked DRAM modules through a packet-switched network interface. Replacing legacy interconnects with sophisticated optical networks could significantly reduce memory access time and energy - a largely unexplored research area. Addressing the scaling limitations in chiplet-based systems, in particular, large inter-chiplet non-uniform latencies, distance-related energy overheads, and limited Input-Output (IO) bandwidth, and exploiting the properties of optical interconnects to propose a scalable uniform memory architecture. Rethinking the architecture of state-of-the-art high-throughput accelerators, the impact of memory access latency variations on the overall performance and system design, and the key challenges in scaling memory and compute capacity in these systems. A new architecture is proposed to reduce the contention within the memory system with the help of a partitioned memory controller and an all-to-all passive optical interconnect that is amenable for a 2.5D based implementation using off-the-shelf memory modules.