Silicon photonics is an emerging technology that delivers higher ratios of bandwidth to power than today's electrical interconnects. This paper explores whether graph-based analytics, increasingly important for high performance computing, can benefit from photonics' energy-efficient bandwidth. We select two contrasting photonically-enhanced systems projected for 2020. We sketch optical and electrical interconnect variants at different points along similar performance-to-power curves. We model applications and graphs that exhibit four distinct workload characteristics: compute- bound, bandwidth-bound all-to-alls, bandwidth-bound neighbor exchange, and latency-bound. We present quantitative results that project execution time and energy on large graphs (1 trillion edges). Our results show that for these workloads, interconnects with efficient optical interconnects can be over-provisioned if their bandwidth is too high. However, interconnects with similar efficiency but lower power present an opportunity for energy savings. We also show that even though optical interconnects do not improve on electrical link latencies, they can substantially increase the performance of latency-bound applications.
['Nathan R. Tallent', 'Kevin J. Barker', 'Daniel G. Chavarría-Miranda', 'Antonino Tumeo', 'Mahantesh Halappanavar', 'Andres Marquez', 'Darren J. Kerbyson', 'Adolfy Hoisie']
Modeling the Impact of Silicon Photonics on Graph Analytics