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Distributed and Massively Parallel Graph Algorithms

Project description

Solving open graph problems will support rapid parallel processing of data

Information technology has advanced at lightning speed since the first personal computers and large mainframes. The volume of datasets is growing faster than the capacity of single processors to deal with it. Rapid parallel processing of vast amounts of information is likely the future of computation and distributed and parallel graph algorithms will play a critical role. The EU-funded DistMaP project is developing improved distributed and massively parallel graph algorithms for many of the open problems in the field. Outcomes could help meet the challenge of rapidly and accurately analysing ever-increasing data for a wealth of present and future applications.

Objective

With the rapidly growing size of the data and the pervasiveness of distributed systems and networks, it is a certainty that distributed and parallel computations will play a vital role in the computations of the future. This project aims to advance our understanding of the foundational aspects of these areas. We tackle some of the central questions in distributed algorithms and massively parallel algorithms for graph problems, which require us to go well-beyond the current state of the art. Our research plan involves three directions:

- Developing efficient and particularly polylogarithmic-time deterministic distributed algorithms for some of the central graph problems of the area. Our hope is to do this through a general derandomization method that removes the randomness from efficient randomized algorithms. This question underlies some of the well-known open problems of the area.

- Developing improved and particularly sublogarithmic-time randomized distributed algorithms for some of the central local graph problems of the area, thus hopefully narrowing or ideally closing this decade old gap to the respective lower bounds.

- Developing improved massively parallel algorithms for some of the fundamental graph problems, with a special focus on the challenging regime of lower memory machines, which remains widely open.

Given the high risk nature of these questions, in each direction, besides our plan of attack on the bigger and more ambitious objectives, we also explain a number of smaller problems, which should be more feasible, and which would serve as stepping stones toward the bigger goal. Moreover, we are hopeful that the simultaneous study of distributed algorithms and massively parallel will lead to a strengthening of the connections between these two areas and would also bring the related scientific communities closer to each other.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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ERC-STG - Starting Grant

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Call for proposal

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(opens in new window) ERC-2019-STG

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Host institution

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 792 000,00
Address
HOFGARTENSTRASSE 8
80539 MUNCHEN
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 792 000,00

Beneficiaries (2)

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