  Control parallelism and data parallelism is mostly reasoned and optimized as
separate functions. Because of this, workloads that are irregular, fine-grain
and dynamic such as dynamic graph processing become very hard to scale. An
experimental research approach to computer architecture that synthesizes prior
techniques of parallel computing along with new innovations is proposed in this
paper. We establish the background and motivation of the research undertaking
and provide a detailed description of the proposed omputing system that is
highly parallel non-von Neumann, memory-centric and memory-driven. We also
present a message-driven (or even-driven) programming model called "diffusive
computation" and provide insights into its properties using SSSP and Triangle
Counting problems as examples.
