Prefix Computations on Symmetric Multiprocessors (original) (raw)

On-Line Adaptive Parallel Prefix Computation

Lecture Notes in Computer Science, 2006

We consider parallel prefix computation on processors of different and possibly changing speeds. Extending previous works on identical processors, we provide a lower bound for this problem. We introduce a new adaptive algorithm which is based on the on-line recursive coupling of an optimal sequential algorithm and a parallel one, non-optimal but recursive and fine-grain. The coupling relies on a work-stealing scheduling. Its theoretical performance is analysed on p processors of different and changing speeds. It is close to the lower bound both on identical processors and close to the lower bound for processors of changing speeds. Experiments performed on an eight-processor machine confirms this theoretical result.

New parallel prefix algorithms

Proceedings of the 9th WSEAS international …, 2009

Google, Inc. (search), Subscribe (Full Service), Register (Limited Service, Free), Login. Search: The ACM Digital Library The Guide. ...

An efficient parallel strategy for high-cost prefix operation

The Journal of Supercomputing, 2020

The prefix computation strategy is a fundamental technique used to solve many problems in computer science such as sorting, clustering, and computer vision. A large number of parallel algorithms have been introduced that are based on a variety of high-performance systems. However, these algorithms do not consider the cost of the prefix computation operation. In this paper, we design a novel strategy for prefix computation to reduce the running time for high-cost operations such as multiplication. The proposed algorithm is based on (1) reducing the size of the partition and (2) keeping a fixed-size partition during all the steps of the computation. Experiments on a multicore system for different array sizes and number sizes demonstrate that the proposed parallel algorithm reduces the running time of the best-known optimal parallel algorithm in the average range of 62.7-79.6%. Moreover, the proposed algorithm has high speedup and is more scalable than those in previous works.

Linear-Time Computation of Prefix Table for Weighted Strings

Lecture Notes in Computer Science, 2015

The prefix table of a string is one of the most fundamental data structures of algorithms on strings: it determines the longest factor at each position of the string that matches a prefix of the string. It can be computed in time linear with respect to the size of the string, and hence it can be used efficiently for locating patterns or for regularity searching in strings. A weighted string is a string in which a set of letters may occur at each position with respective occurrence probabilities. Weighted strings, also known as position weight matrices or uncertain strings, naturally arise in many biological contexts; for example, they provide a method to realise approximation among occurrences of the same DNA segment. In this article, given a weighted string x of length n and a constant cumulative weight threshold 1/z, defined as the minimal probability of occurrence of factors in x, we present an O(n)-time algorithm for computing the prefix table of x. Furthermore, we outline a number of applications of this result for solving various problems on non-standard strings, and present some preliminary experimental results.

Asynchronous parallel prefix computation

IEEE Transactions on Computers, 1998

The prefix problem is to compute all the products x 1 ¬ x 2 ¬ L ¬ x k , for 1 k n, where ¬ is an associative binary operation. We start with an asynchronous circuit to solve this problem with O(log n) latency and O(n log n) circuit size, with O(n) ¬operations in the circuit. Our contributions are: 1) a modification to the circuit that improves its average-case latency from O(log n) to O(log log n) time, and 2) a further modification that allows the circuit to run at full-throughput, i.e., with constant response time. The construction can be used to obtain a asynchronous adder with O(log n) worst-case latency and O(log log n) average-case latency.

Using the Sadakane Compressed Suffix Tree to Solve the All-Pairs Suffix-Prefix Problem

BioMed Research International, 2014

The all-pairs suffix-prefix matching problem is a basic problem in string processing. It has an application in the de novo genome assembly task, which is one of the major bioinformatics problems. Due to the large size of the input data, it is crucial to use fast and space efficient solutions. In this paper, we present a space-economical solution to this problem using the generalized Sadakane compressed suffix tree. Furthermore, we present a parallel algorithm to provide more speed for shared memory computers. Our sequential and parallel algorithms are optimized by exploiting features of the Sadakane compressed index data structure. Experimental results show that our solution based on the Sadakane's compressed index consumes significantly less space than the ones based on noncompressed data structures like the suffix tree and the enhanced suffix array. Our experimental results show that our parallel algorithm is efficient and scales well with increasing number of processors.