Spacetime

Project Dates: 
January 2015
Research Area(s): 
Funding: 

Funding has been received from the National Science Foundation.

Project Description: 

Spacetime is a framework for developing time-stepped, multi-worker applications based on the tuplespace model. Workers compute within spacetimed frames -- a fixed portion of the shared data during a fixed period of time. The locally modified data may be pushed back to the shared store at the end of each step. Pulling and pushing data from/to the store is done declaratively using two small DSLs: (1) a DSL for predicate collection classes (PCC) used for specifying algebraic operations on data sets, and (2) a DSL for controlling data flow in terms of direction (to/from store) and, eventually, permissions.

The first implementation of spacetime is in Python. Follow this link to it for instructions on how to use the Python implementation.

This work was featured in the ISR Connector newsletter, Fall/Winter 2016, in Hot Research.

Publications: 
Lopes, C., R. Achar, and A. Valadares, "Predicate Collection Classes", Journal of Object Technology, In Press.
Valadares, A., C. V. Lopes, R. Achar, and M. Bowman, "CADIS: Aspect-Oriented Architecture for Collaborative Modeling and Simulation", 2016 Winter Simulation Conference, Washington, D.C., IEEE, pp. 1024-1035, 12/2016.