This presentation proposes an approach to coupling based on source level and runtime code analysis, in addition to a robust runtime support framework for the execution of the coupled models. By analyzing the behavior of the code in both a static and runtime context, with some minor assistance from the programmer, we can generate simple finite state automata representing the essential behavior of individual models. Given these representations, couplings between states can be defined between the models by the user. Petri nets with simple graph grammars provide a mechanism to map these state machines and couplings into a meaningful model that can be implemented in the runtime system to control the coupled model behavior. For applications that can be recompiled, however, this intelligent memory, with its hundreds of simple processors, appears as a superb accelerator of applications.
Energy consumption presents another challenge.
Many processors on a chip, all possibly accessing on-chip memory, consume significant energy. Bob Malone Abstract Times N Systems, a systems company in Austin Texas, has a unique interconnect strategy. Multiple Intel nodes are connected via a large Shared Memory using fiber optics, the hardware runs either W2K or Linux. Individual nodes can access shared memory by directly addressing data off their local memory. This collection of hardware, nodes and interconnect is refer to as a team.