This paper describes a parallelization of the sequential dynamic programming method for solving a 2D knapsack problem where multiples of n rectangular objects are optimally packed into a knapsack of size L × W and are only obtainable with guillotine-type (side to side) cuts. The parallel algorithm is described and analyzed for the associative model. The associative model (ASC) for parallel computation supports a generalized version of an associative style of computing that has been used since the introduction of associative SIMD computers in the early 1970's. In particular, this model supports data parallelism, constant time maximum and minimum operations, one or more instruction streams (ISs) which are sent to an equal number of partition sets of processors, and assignment of tasks to the ISs using control parallelism. This algorithm runs in O(W(n+L+W)) time using O(L) processors, where L ≥ W for a 2D knapsack problem with a capacity of L × W. This result is cost optimal with respect to the best sequential implementation. Moreover, an efficient ASC algorithm for this well-known problem should give insight to how the associative model compares to other parallel models.