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.

Clusters of workstations are increasingly being viewed as a cost effective alternative to parallel supercomputers. However, resource management and scheduling on workstations clusters is complicated by the fact that the number of idle workstations available for executing parallel applications is constantly fluctuating. We present a case for scheduling parallel applications on non dedicated workstation clusters using dynamic space sharing, a policy under which the number of processors allocated to an application can be changed during its execution. We describe an approach that uses application level checkpointing and data repartitioning for supporting dynamic space sharing and for handling the dynamic reconfiguration triggered when failure or owner activity is detected on a workstation being used by a parallel application. The performance advantages of dynamic space sharing are quantified through a simulation study, and experimental results are presented for the overhead of dynamic reconfiguration of a grid oriented data parallel application using our approach.

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.