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Isaac D. Scherson

Researcher at University of California, Irvine

Publications -  106
Citations -  1265

Isaac D. Scherson is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Scheduling (computing) & Parallel algorithm. The author has an hindex of 21, co-authored 106 publications receiving 1243 citations. Previous affiliations of Isaac D. Scherson include Princeton University & Pierre-and-Marie-Curie University.

Papers
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Proceedings ArticleDOI

An analysis of diffusive load-balancing

TL;DR: It is shown that the running time of the diffusion algorithm is bounded by: &OHgr;(log &sgr;/Γ) ≤ Time ≤ O(N&sGr;/ Γ) and &OH gr;( log &s Gr;/&Fgr;) ≤ time ≤ O(&sgr:/& Fgr;2), where N is the number of nodes in the network, &s gr; is the standard deviation of the initial load distribution
Journal ArticleDOI

Parallel sorting in two-dimensional VLSI models of computation

TL;DR: The gradual refinement of a general approach to two-dimensional sorting, the shear-sort algorithm, to more sophisticated and specialized sorting algorithms on mesh-connected computers is described.
Proceedings Article

Shear Sort: A True Two-Dimensional Sorting Techniques for VLSI Networks.

TL;DR: An efficient bubble sort network suitable for VLSI implementation with near-optimal area-time-squared performance is a direct application of the row-column sorting technique.
Book

Interconnection Networks for High-Performance Parallel Computers

TL;DR: A new hierarchical interconnection network for introduction to parallel computing architectures architecture of parallel computers uabt design of interconnection networks for mpp of next generation three-dimensional petersen-torus network.
Journal ArticleDOI

Bit-parallel arithmetic in a massively-parallel associative processor

TL;DR: A simple but powerful architecture based on the classical associative processor model, by distributing logic among slices of storage cells such that a number of bit-planes share a simple logic unit, bit-parallel arithmetic for massively parallel processing becomes feasible.