This paper describes the development of a fine-grained meta-heuristic for solving large strip packing problems with guillotine layouts. aCe, an architecture-adaptive environment, and the aCe C parallel programming language are used to implement a massively parallel genetic simulated annealing (GSA) algorithm. The parallel GSA combines the temperature schedule of simulated annealing with the crossover and mutation operators that are applied to chromosome populations in genetic algorithms. For our problem, chromosomes are normalized postfix expressions that represent guillotine strip packings. Preliminary results for some benchmark data sets are reported and indicate that the parallel GSA method holds promise as a technique for solving the strip packing problem.