Abstract
In this paper, we present new methods for load balancing of unstructured tree computations on large-scale SIMD machines, and analyze the scalability of these and other existing schemes. An efficient formulation of tree search on an SIMD machine consists of two major components: a triggering mechanism, which determines when the search space redistribution must occur to balance the search space over processors, and a scheme to redistribute the search space. We have devised a new redistribution mechanism and a new triggering mechanism. Either of these can be used in conjunction with triggering and redistribution mechanisms developed by other researchers. We analyze the scalability of these mechanisms, and verify the results experimentally. The analysis and experiments show that our new load-balancing methods are highly scalable on SIMD architectures. Their scalability is shown to be no worse than that of the best load-balancing schemes on MIMD architectures. We verify our theoretical results by implementing the 15-puzzle problem on a CM-2 SIMD parallel computer.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1057-1072 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Parallel and Distributed Systems |
| Volume | 5 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1994 |
Bibliographical note
Funding Information:Manuscript received April 22, 1993; revised October 27, 1993, and November 2, 1993. This work was supported by IST/SDIO through the Army Research Office under Grant 28408-MA-SDI, and by the Army High Per- formance Computing . -Research Center at the University of Minnesota.
Keywords
- Artificial intelligence
- computing
- heuristic search
- load balancing
- parallel
- unstructured computations