Showing papers by "Thomas H. Cormen published in 2004"

FG: A Framework Generator for Hiding Latency in Parallel Programs Running on Clusters

Abstract: FG is a programming environment for asynchronous programs that run on clusters and t into a pipeline framework. It enables the programmer to write a series of synchronous functions and represents them as stages of an asynchronous pipeline. FG mitigates the high latency inherent in interprocessor communication and accessing the outer levels of the memory hierarchy. It overlaps separate pipeline stages that perform communication, computation, and I/O by running the stages asynchronously. Each stage maps to a thread. Buffers, whose sizes correspond to block sizes in the memory hierarchy, traverse the pipeline. FG makes such pipeline-structured parallel programs easier to write, smaller, and faster. FG offers several advantages over statically scheduled overlapping and dynamically scheduled overlapping via explicit calls to thread functions. First, it reduces coding and debugging time. Second, we nd that it reduces code size by approximately 15n26%. Third, according to experimental results, it improves performance. Compared with programs that use static scheduling, FG-generated programs run approximately 61n69% faster on a 16-node Beowulf cluster. Compared with programs that make explicit calls for dynamically scheduled threads, FG-generated programs run slightly faster. Fourth, FG offers various design options and makes it easy for the programmer to explore different pipeline cong urations.

Parallel out-of-core sorting: the third way

Abstract: Sorting very large datasets is a key subroutine in almost any application that is built on top of a large database. Two ways to sort out-of-core data dominate the literature: merging-based algorithms and partitioning-based algorithms. Within these two paradigms, all the programs that sort out-of-core data on a cluster rely on assumptions about the input distribution. We propose a third way of out-of-core sorting: oblivious algorithms. In all, we have developed six programs that sort out-of-core data on a cluster. The first three programs, based completely on Leighton's columnsort algorithm, have a restriction on the maximum problem size that they can sort. The other three programs relax this restriction; two are based on our original algorithmic extensions to columnsort. We present experimental results to show that our algorithms perform well. To the best of our knowledge, the programs presented in this thesis are the first to sort out-of-core data on a cluster without making any simplifying assumptions about the distribution of the data to be sorted.