BSP vs MapReduce

Pace, Matthew

doi:10.1016/j.procs.2012.04.026

Cited by 42 publications

(15 citation statements)

References 15 publications

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“…BSP is a powerful generalization of MR. A subclass of BSP algorithms can be efficiently implemented in MR [11]. BSP is superfast on standard commodity hardware, orders of magnitude faster than the MR alone.…”

Section: Pregel and Giraphmentioning

confidence: 99%

Graph Databases: Their Power and Limitations

Pokorný

2015

Lecture Notes in Computer Science

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Part 1: Full Keynote and Invited PapersInternational audienceReal world data offers a lot of possibilities to be represented as graphs. As a result we obtain undirected or directed graphs, multigraphs and hypergraphs, labelled or weighted graphs and their variants. A development of graph modelling brings also new approaches, e.g., considering constraints. Processing graphs in a database way can be done in many different ways. Some graphs can be represented as JSON or XML structures and processed by their native database tools. More generally, a graph database is specified as any storage system that provides index-free adjacency, i.e. an explicit graph structure. Graph database technology contains some technological features inherent to traditional databases, e.g. ACID properties and availability. Use cases of graph databases like Neo4j, OrientDB, InfiniteGraph, FlockDB, AllegroGraph, and others, document that graph databases are becoming a common means for any connected data. In Big Data era, important questions are connected with scalability for large graphs as well as scaling for read/write operations. For example, scaling graph data by distributing it in a network is much more difficult than scaling simpler data models and is still a work in progress. Still a challenge is pattern matching in graphs providing, in principle, an arbitrarily complex identity function. Mining complete frequent patterns from graph databases is also challenging since supporting operations are computationally costly. In this paper, we discuss recent advances and limitations in these areas as well as future directions

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Section: Pregel and Giraphmentioning

confidence: 99%

Graph Databases: Their Power and Limitations

Pokorný

2015

Lecture Notes in Computer Science

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“…Every one of them has themselves advantages and disadvantages. Now the popular models are MapReduce and BSP, although they have been widely adopted in a wide range of areas, it hasn't been determined whether MapReduce and BSP are better than other models [6][7]. For example, MapReduce doesn't have strong theoretical foundations, it is demanded to establish its relationship to other major parallel computation models such as BSP and PRAM.…”

Section: The Parallel Computing Modelsmentioning

confidence: 99%

The Research of Synthesizing Parallel Computing Models with Graph Reduction

Shen¹,

Tong²

2014

IJAST

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“…Open source and free software frameworks exist for this model, on different architectures (GPU, multi-core, clusters), which makes it a practical choice. Moreover, theoretical works have found it equivalent to BSP and PRAM [26], both well-studied parallel models. In this model, the input data is first processed independently by many mappers, their results are then further processed independently by reducers.…”

Section: A Defining a Target Parallel Modelmentioning

confidence: 99%

Savant: Automatic parallelization of a scheduling heuristic with machine learning

Pinel

Bouvry

Dorronsoro

et al. 2013

2013 World Congress on Nature and Biologically Inspired Computing

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Abstract-This paper investigates the automatic parallelization of a heuristic for an NP-complete problem, with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the Savant syndrome. Its concurrency model is based on mapreduce. The approach is evaluated with the well-known Min-Min heuristic. Simulation results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality.

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BSP vs MapReduce

Cited by 42 publications

References 15 publications

Graph Databases: Their Power and Limitations

Graph Databases: Their Power and Limitations

The Research of Synthesizing Parallel Computing Models with Graph Reduction

Savant: Automatic parallelization of a scheduling heuristic with machine learning

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