CuPit-2 - a parallel language for neural algorithms: language reference and tutorial
Holger Hopp, Lutz Prechelt · Repository KITopen (Karlsruhe Institute of Technology) · 1997
CuPit-2 is a parallel programming language with two main design goals: 1. to allow the simple, problem-adequate formulation of learning algorithms for neural networks with focus on algorithms that change the topology of the underlying neural network during the learning process and 2. to allow the generation of efficient code for massively parallel machines from a completely machine-independent program description, in particular to maximize both data locality and load balancing even for irregular neural networks. The idea to achieve these goals lies in the programming model: CuPit-2 programs are object-centered, with connections and nodes of a graph (which is the neural network) being the objects. Algorithms are based on parallel local computations in the nodes and connections and communication along the connections (plus broadcast and reduction operations). This report describes the design considerations and the resulting language definition and discusses in detail a tutorial example program. This CuPit-2 language manual and tutorial is an updated version of the original CuPit language manual [Technical Report 1994-04]. The new language CuPit-2 differs from the original CuPit in several ways. All language changes from CuPit to CuPit-2 are listed in the appendix.