Results 1 
3 of
3
Efficient LowContention Parallel Algorithms
, 1996
"... The queueread, queuewrite (qrqw) parallel random access machine (pram) model permits concurrent reading and writing to shared memory locations, but at a cost proportional to the number of readers/writers to any one memory location in a given step. The qrqw pram model re ects the contention propert ..."
Abstract

Cited by 32 (13 self)
 Add to MetaCart
(Show Context)
The queueread, queuewrite (qrqw) parallel random access machine (pram) model permits concurrent reading and writing to shared memory locations, but at a cost proportional to the number of readers/writers to any one memory location in a given step. The qrqw pram model re ects the contention properties of most commercially available parallel machines more accurately than either the wellstudied crcw pram or erew pram models, and can be e ciently emulated with only logarithmic slowdown on hypercubetype noncombining networks. This paper describes fast, lowcontention, workoptimal, randomized qrqw pram algorithms for the fundamental problems of load balancing, multiple compaction, generating a random permutation, parallel hashing, and distributive sorting. These logarithmic or sublogarithmic time algorithms considerably improve upon the best known erew pram algorithms for these problems, while avoiding the highcontention steps typical of crcw pram algorithms. An illustrative experiment demonstrates the performance advantage of a new qrqw random permutation algorithm when compared with the popular erew algorithm. Finally, this paper presents new randomized algorithms for integer sorting and general sorting.
A Note on Probabilistic Integer Sorting
"... We present a new probabilistic sequential algorithm for stable sorting n uniformly distributed keys in an arbitrary range. The algorithm runs in linear time with veryhigh probability 1 \Gamma 2 \Gamma\Omega\Gamma n) (the best previously known probability bound has been 1 \Gamma 2 \Gamma\Omega\Gamma ..."
Abstract
 Add to MetaCart
(Show Context)
We present a new probabilistic sequential algorithm for stable sorting n uniformly distributed keys in an arbitrary range. The algorithm runs in linear time with veryhigh probability 1 \Gamma 2 \Gamma\Omega\Gamma n) (the best previously known probability bound has been 1 \Gamma 2 \Gamma\Omega\Gamma n=(lg n lg lg n)) ). We also describe an EREW PRAM version of the algorithm that sorts in O((n=p + lg p) lg n= lg (n=p + lg n)) time using p n processors within the same probability bound. Additionally, we present experimental results for the sequential algorithm that establish the practicality of our algorithm.
Probabilistic Integer Sorting
"... We introduce a probabilistic sequential algorithm for stable sorting n uniformly distributed keys in an arbitrary range. The algorithm runs in linear time and sorts all but a very small fraction 2 # n) of the input sequences; the best previously known bound was 2 # n/(lg n lg lg n)) . An EREW ..."
Abstract
 Add to MetaCart
(Show Context)
We introduce a probabilistic sequential algorithm for stable sorting n uniformly distributed keys in an arbitrary range. The algorithm runs in linear time and sorts all but a very small fraction 2 # n) of the input sequences; the best previously known bound was 2 # n/(lg n lg lg n)) . An EREW PRAM version of the sequential algorithm sorts in O((n/p+lg p) lg n/ lg (n/p + lg n)) time using p # n processors under the same probabilistic conditions. For a CRCW PRAM we improve upon the probabilistic bound of 2 # n/(lg n lg lg n)) obtained by Rajasekaran and Sen to derive a 2 # n lg lg n/ lg n) bound. Two architecture independent parallel algorithms described under the framework of the BulkSynchronous Parallel model are also presented. For varying ratios of n/p they sort in optimal parallel computation time; the former algorithm sorts all but a 2 # n) fraction of the input sequences whereas the latter algorithm sorts all but a n #(1) fraction. Additionally, we present experi...