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About 10,000 papers for “parallel algorithms”

Parallel Algorithms

Bhatt, Sandeep · DIMACS series in discrete mathematics and theoretical computer science · 1997

Cited by 0

Parallel Algorithms

Thomas Sterling, Matthew J. Anderson, Maciej Brodowicz · Elsevier eBooks · 2018

Cited by 1

Parallel Algorithms

Sandeep Sen, Amit Kumar · Cambridge University Press eBooks · 2019

There is a perpetual need for faster computation which is unlikely to be ever satisfied. With device technologies hitting physical limits, alternate computational models are being explored. The Big Data phenomenon precedes the coinage of this term by many decades. One of the earliest and natural direction to speed-up computation was to deploy multiple processors instead of a single processor for running the same program. The ideal objective is to speed-up a program p-fold by using p processors simultaneously. A common caveat is that an egg cannot be boiled faster by employing multiple cooks! Analogously, a program cannot be executed faster indefinitely by using more and more processors. This is not just because of physical limitations but dependencies between various fragments of the code, imposed by precedence constraints.

Cited by 1

Parallel Algorithms

Sergei Kurgalin, Sergei Borzunov · Texts in computer science · 2020

Cited by 0

Parallel Algorithms

Guy E. Blelloch, Bruce M. Maggs · Chapman & Hall/CRC applied algorithms and data structures series · 1998

Cited by 20

Parallel Algorithms

Henri Casanova · 2008

Focusing on algorithms for distributed-memory parallel architectures, Parallel Algorithms presents a rigorous yet accessible treatment of theoretical models of parallel computation, parallel algorithm design for homogeneous and heterogeneous platforms, complexity and performance analysis, and essential notions of scheduling. The book extract

Cited by 88

Parallel algorithms

Hossfeld, F., Kernforschungsanlage Juelich G.m.b.H. (Germany, F.R.). Zentralinstitut fuer Angewandte Mathematik · OpenGrey (Institut de l'Information Scientifique et Technique) · 1981

Cited by 0

Parallel Algorithms

Woongki Baek, Jaewoong Chung, Chi Cao Minh et al. · 2008

The world’s data is growing at exponential rates. To deal with this glut of information, computer systems must be able to understand and interpret data in ways that provide their users with practical knowledge [1]. To process ever increasing data sets, often,

Cited by 0

Parallel algorithms

Kindervater, Gerard, Lenstra, Jan Karel · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1985

Cited by 8Open access

Parallel Architectures and intrinsically Parallel Algorithms: Genetics Algorithms

R. CAMPANINI, G. DI CARO, I. D'ANTONE et al. · IRIS UNIMORE (University of Modena and Reggio Emilia) · 1994

Genetic algorithms are search or classification algorithms based on natural models. They present a high degree of internal parallelism. We developed two versions, differing in the way the population is organized and we studied and compared their characteristics and performances when applied to the optimization of multidimensional function problems. All the implementations are realized on transputer networks.

Cited by 0Open access

Parallel sorting algorithms

Akl, Selim G. · 1985

Parallel Sorting Algorithms explains how to use parallel algorithms to sort a sequence of items on a variety of parallel computers. The book reviews the sorting problem, the parallel models of computation, parallel algorithms, and the lower bounds on the parallel sorting problems. The text also presents twenty different algorithms, such as linear arrays, mesh-connected computers, cube-connected computers. Another example where algorithm can be applied is on the shared-memory SIMD (single instruction stream multiple data stream) computers in which the whole sequence to be sorted can fit in the

Cited by 16

Assessing parallel algorithms

Yuriy Baskakov, И.Г. Голубев · 2015

Various existing performance metrics within the parallel systems domain are analyzed. These include the different flavors of speedup, efficiency, and isoefficiency. Execution time still remains the most widely used metric. A new method to automatically estimate algorithmic cost is provided.

Cited by 0