Journal ArticleDOI
Apache Spark: a unified engine for big data processing
Matei Zaharia,Reynold Xin,Patrick Wendell,Tathagata Das,Michael Armbrust,Ankur Dave,Xiangrui Meng,Josh Rosen,Shivaram Venkataraman,Michael J. Franklin,Ali Ghodsi,Joseph E. Gonzalez,Scott Shenker,Ion Stoica +13 more
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This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.Abstract:
This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applicationsread more
Citations
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An Online and Scalable Model for Generalized Sparse Nonnegative Matrix Factorization in Industrial Applications on Multi-GPU
TL;DR: Wang et al. as discussed by the authors proposed an online, scalable, and single-thread-based generalized sparse nonnegative matrix factorization (CUSNMF) for CUDA parallelization on GPU and multi-GPU.
Journal ArticleDOI
A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating
TL;DR: In this article, the authors show that the population is receptive to quantile regression for a large dataset on a distributed system and that the popula cation of the data set is large.
Journal ArticleDOI
Running resilient MPI applications on a Dynamic Group of Recommended Processes
TL;DR: This work presents a new model to deal with this problem in which processes execute tests among themselves in order to determine whether the processors (or cores) on which they are running are recommended or non-recommended.
Journal ArticleDOI
On the scalability of Big Data Cyber Security Analytics systems
TL;DR: In this paper , the authors investigate the scalability of a big data cyber security analytics (BDCA) system with default Spark settings and identify Spark configuration parameters (e.g., execution memory).
Journal ArticleDOI
MaRe: Processing Big Data With Application Containers on Apache Spark
TL;DR: MaRe enables scalable data-intensive processing in life science with Apache Spark and application containers and has the advantage of providing data locality, ingestion from heterogeneous storage systems, and interactive processing.
References
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Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Proceedings Article
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Matei Zaharia,Mosharaf Chowdhury,Tathagata Das,Ankur Dave,Justin Ma,Murphy McCauley,Michael J. Franklin,Scott Shenker,Ion Stoica +8 more
TL;DR: Resilient Distributed Datasets is presented, a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner and is implemented in a system called Spark, which is evaluated through a variety of user applications and benchmarks.
Journal ArticleDOI
A bridging model for parallel computation
TL;DR: The bulk-synchronous parallel (BSP) model is introduced as a candidate for this role, and results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
Proceedings ArticleDOI
Pregel: a system for large-scale graph processing
Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
TL;DR: A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.
Proceedings ArticleDOI
Dryad: distributed data-parallel programs from sequential building blocks
TL;DR: The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.