  Computer systems that have been successfully deployed for dense regular
workloads fall short of achieving scalability and efficiency when applied to
irregular and dynamic graph applications. Conventional computing systems rely
heavily on static, regular, numeric intensive computations while High
Performance Computing systems executing parallel graph applications exhibit
little locality, spatial or temporal, and are fine-grained and memory
intensive. With the strong interest in AI which depend on these very different
use cases combined with the end of Moore's Law at nanoscale, dramatic
alternatives in architecture and underlying execution models are required. This
paper identifies an innovative non-von Neumann architecture, Continuum Computer
Architecture (CCA), that redefines the nature of computing structures to yield
powerful innovations in computational methods to deliver a new generation of
highly parallel hardware architecture. CCA reflects a genus of highly parallel
architectures that while varying in specific quantities (e.g., memory blocks),
share a multiple of attributes not found in typical von Neumann machines. Among
these are memory-centric components, message-driven asynchronous flow control,
and lightweight out-of-order execution across a global name space. Together
these innovative non-von Neumann architectural properties guided by a new
original execution model will deliver the new future path for extending beyond
the von Neumann model. This paper documents a series of interrelated
experiments that together establish future directions for next generation
non-von Neumann architectures, especially for graph processing.
