Scalable Optical Nodes for Networked Edge Traversal (SONNET)
Abstract
Graph analytics on large data sets is currently performed on leadership-class supercomputers that are designed for other purposes. These machines are required because they have the memory capacity required for large graph problems, but the demand on the processors is low, resulting in extremely low compute efficiency. Computationally, graph analysis is characterized by many short, random accesses to memory which is inefficient on current systems that are optimized for regular, predictable access. The SONNET program will build a silicon photonics-based graph processor that will perform graph analysis on Terabytes (TBs) of data with performance comparable to peta-scale supercomputers in a significantly smaller size, weight, and power (SWAP) envelope. SONNET will optimize the design of the graph processor by co-designing processor and photonic hardware, and the computer and network architectures to exploit the high bandwidth provided by silicon photonics. SONNET will demonstrate a scalable, power efficient prototype of such a graph processor and quantify performance for DoD-relevant applications. The performance, efficiency, and size will be transformational for big data analytics and enable real-time analysis on dynamic graphs in the fields of cyber security, threat detection, and numerous others. This program will explore the efficient processing of local information using stacked memory and integrated circuits specially made for specific tasks, as well as the efficient transfer of data between local information processors. The SONNET program will optimize the design of a graph processor and design and demonstrate high performance processor cores to accelerate graph primitives and photonic hardware required for high bandwidth, low diameter photonic networks. The program will design and evaluate a Graph processor capable of analyzing large data sets relevant to future DoD requirements. This program has advanced technology development efforts funded in PE 0603760E, CCC-02.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2016
- Source ID
- e74534312b94619d8bc1e0883da3e0ca