Approximate parallel scheduling. II. Applications to logarithmic-time optimal parallel graph algorithms

Logarithmic Time Cost Optimal Parallel

When looking for new and faster parallel sorting algorithms for use in massively parallel systems it is tempting to investigate promising alternatives from the large body of research done on parallel sorting in the eld of theoretical computer science. Such \theoretical" algorithms are mainly described for the PRAM (Parallel Random Access Machine) model of computation 13, 26]. This paper shows h...

Parallel algorithms for scheduling data-graph computations

A data-graph computation — popularized by such programming systems as Pregel, GraphLab, Galois, Ligra, PowerGraph, and GraphChi — is an algorithm that iteratively performs local updates on the vertices of a graph. During each round of a data-graph computation, a user-supplied update function atomically modifies the data associated with a vertex as a function of the vertex’s prior data and that ...

Exact and approximate algorithms for high-multiplicity parallel machine scheduling

In many scheduling applications, a large number of jobs are grouped into a comparatively small number of lots, made of identical items. It is then sufficient to give, for each lot, the number of jobs it involves plus the description of one single job. The resulting high-multiplicity input format is much more compact than the standard one. As a consequence, in order to be efficient, standard sol...

Logarithmic Time Parallel Bayesian Inference

I present a parallel algorithm for exact prob­ abilistic inference in Bayesian networks. For polytree networks with n variables, the worst­ case time complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write paral­ lel random-access machine) with n processors, for any constant number of evidence variables. For arbitrary networks, the time complexity is O( r3w log n) for n processo...

Parallel Graph Algorithms