As WWW grows at an increasing speed, a classifier targeted at hypertext has become in high demand. While document categorization is quite a mature, the issue of utilizing hypertext structure and hyperlinks has been relatively unexplored. In this paper, we propose a practical method for enhancing both the speed and the quality of hypertext categorization using hyperlinks. In comparison against a recently proposed technique that appears to be the only one of the kind, we obtained up to 18.5% of improvement in effectiveness while reducing the processing time dramatically. We attempt to explain through experiments what factors contribute to the improvement.

This paper proposes an efficient method of partitioning nested loops with non-uniform dependences for maximizing parallelism. Our approach is based on convex hull theory, and it will divide the iteration space of the loop into three regions as two parallel regions where the iterations can be fully executed in parallel and one parallelizable region where the iterations are inherently serial, but possible parallelism can be exploited. And in order to maximize parallelism from the parallelizable region, an algorithm using integer programming which partitions a loop into variable size partitions is also proposed. In comparison with some works on partitioning, the proposed method is a simple and exact partitioning method, and it gives much better speedup and extracts more parallelism than them.

The techniques of “load/store” memory reference modeling is based on deriving performance characteristics of the memory architecture of a computer by looking at the behavior of simple sequences of load, store and nop (null operation) instructions. The resulting data base can be used to match load/store templates against algorithm kernels to predict performance or as a source of data for testing analytical models of the architecture. In this paper we study the BBN GP1000 parallel processing system. We show how to build a subset of the load/store kernels needed to characterize the machine and illustrate the behavior of a simple model based on the data.