The constrained 2D cutting stock problem is an irregular problem with dynamic data structures, highly variable amounts of computation per task, and unpredictable amounts and patterns of communication. This paper describes the design and implementation of a parallel solution to this problem on a cluster of workstations and a distributed memory multicomputer. The key element of our parallel solution is the replication of an important data structure on all processors. By exploiting properties of the cutting stock problem which allow the use of relaxed consistency mechanisms, our approach is able to reduce the overheads for communication and synchronization in comparison to approaches that partition the data structure among processors. A token-based lazy release consistency protocol is used to ensure mutual exclusion and maintain consistency, and a randomized work-stealing protocol is employed to dynamically balance work among processors. Good speedups are reported for three benchmark problems executed on two distributed memory platforms: a cluster of workstations interconnected by a 10 Mbit/s Ethernet and an Intel Paragon. © 1998 John Wiley & Sons, Ltd.

The paper describes the design and implementation of a solution to the constrained 2D cutting stock problem on a cluster of workstations. The constrained 2D cutting stock problem is an irregular problem with a dynamically modified global data set and irregular amounts and patterns of communication. A replicated data structure is used for the parallel solution since the ratio of reads to writes is known to be large. Mutual exclusion and consistency are maintained using a token based lazy consistency mechanism, and a randomized protocol for dynamically balancing the distributed work queue is employed. Speedups are reported for three benchmark problems executed on a cluster of workstations interconnected by a 10 Mbps Ethernet.