๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Parallel Sequence Mining on Shared-Memory Machines

โœ Scribed by Mohammed J. Zaki

Book ID
102974068
Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
610 KB
Volume
61
Category
Article
ISSN
0743-7315

No coin nor oath required. For personal study only.

โœฆ Synopsis

We present pSPADE, a parallel algorithm for fast discovery of frequent sequences in large databases. pSPADE decomposes the original search space into smaller suffix-based classes. Each class can be solved in main-memory using efficient search techniques and simple join operations. Furthermore, each class can be solved independently on each processor requiring no synchronization. However, dynamic interclass and intraclass load balancing must be exploited to ensure that each processor gets an equal amount of work. Experiments on a 12 processor SGI Origin 2000 shared memory system show good speedup and excellent scaleup results.

๐Ÿ“œ SIMILAR VOLUMES

Parallel MP2-energy evaluation: Simulate
Parallel MP2-energy evaluation: Simulated shared memory approach on distributed memory parallel machines
โœ Limaye, Ajay C. ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 155 KB ๐Ÿ‘ 1 views

A parallel algorithm for four-index transformation and MP2 energy evaluation, ลฝ . for distributed memory parallel MIMD machines is presented. The underlying serial algorithm for the present parallel effort is the four-index transform. The scheme works through parallelization over AO integrals and, t

Efficient support for fine-grain paralle
Efficient support for fine-grain parallelism on shared-memory machines
โœ LOWENTHAL, DAVID K.; FREEH, VINCENT W.; ANDREWS, GREGORY R. ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 134 KB ๐Ÿ‘ 2 views

A coarse-grain parallel program typically has one thread (task) per processor, whereas a finegrain program has one thread for each independent unit of work. Although there are several advantages to fine-grain parallelism, conventional wisdom is that coarse-grain parallelism is more efficient. This p