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496
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
, 2010
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Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
, 2011
"... We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algo ..."
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Cited by 239 (27 self)
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We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algorithms and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a restricted form of shared memory, based on coarsegrained transformations rather than fine-grained updates to shared state. However, we show that RDDs are expressive enough to capture a wide class of computations, including recent specialized programming models for iterative jobs, such as Pregel, and new applications that these models do not capture. We have implemented RDDs in a system called Spark, which we evaluate through a variety of user applications and benchmarks. 1
Mesos: A platform for fine-grained resource sharing in the data center
, 2010
"... We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI 1. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve ..."
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Cited by 160 (23 self)
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We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI 1. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today’s frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our experimental results show that Mesos can achieve near-optimal locality when sharing the cluster among diverse frameworks, can scale up to 50,000 nodes, and is resilient to node failures.
Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud
, 2012
"... While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill ..."
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Cited by 141 (2 self)
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While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.
HaLoop: Efficient Iterative Data Processing on Large Clusters
"... The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce and Dryad are two popular platforms in which the dataflow takes the form of a directed acyclic graph o ..."
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Cited by 135 (5 self)
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The growing demand for large-scale data mining and data analysis applications has led both industry and academia to design new types of highly scalable data-intensive computing platforms. MapReduce and Dryad are two popular platforms in which the dataflow takes the form of a directed acyclic graph of operators. These platforms lack built-in support for iterative programs, which arise naturally in many applications including data mining, web ranking, graph analysis, model fitting, and so on. This paper presents HaLoop, a modified version of the Hadoop MapReduce framework that is designed to serve these applications. HaLoop not only extends MapReduce with programming support for iterative applications, it also dramatically improves their efficiency by making the task scheduler loop-aware and by adding various caching mechanisms. We evaluated HaLoop on real queries and real datasets. Compared with Hadoop, on average, HaLoop reduces query runtimes by 1.85, and shuffles only 4 % of the data between mappers and reducers. 1.
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
"... Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly ..."
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Cited by 128 (4 self)
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Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability. In this paper, we characterize the challenges of computation on natural graphs in the context of existing graphparallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains. 1
GraphChi: Large-scale Graph Computation On just a PC
- In Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation, OSDI’12
, 2012
"... Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains c ..."
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Cited by 115 (6 self)
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Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel parallel sliding windows method, GraphChi is able to execute several advanced data mining, graph mining, and machine learning algorithms on very large graphs, using just a single consumer-level computer. We further extend GraphChi to support graphs that evolve over time, and demonstrate that, on a single computer, GraphChi can process over one hundred thousand graph updates per second, while simultaneously performing computation. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC. 1
Piccolo: Building Fast, Distributed Programs with Partitioned Tables
"... Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient appli ..."
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Cited by 86 (3 self)
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Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient application implementations. In particular, applications can specify locality policies to exploit the locality of shared state access and Piccolo’s run-time automatically resolves write-write conflicts using userdefined accumulation functions. Using Piccolo, we have implemented applications for several problem domains, including the PageRank algorithm, k-means clustering and a distributed crawler. Experiments using 100 Amazon EC2 instances and a 12 machine cluster show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface. 1
CIEL: a universal execution engine for distributed data-flow computing
- in Proceedings of the 8th USENIX Symposium on Networked System Design and Implementation (NSDI). USENIX
"... This paper introduces CIEL, a universal execution engine for distributed data-flow programs. Like previous execution engines, CIEL masks the complexity of distributed programming. Unlike those systems, a CIEL job can make data-dependent control-flow decisions, which enables it to compute iterative a ..."
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Cited by 78 (11 self)
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This paper introduces CIEL, a universal execution engine for distributed data-flow programs. Like previous execution engines, CIEL masks the complexity of distributed programming. Unlike those systems, a CIEL job can make data-dependent control-flow decisions, which enables it to compute iterative and recursive algorithms. We have also developed Skywriting, a Turingcomplete scripting language that runs directly on CIEL. The execution engine provides transparent fault tolerance and distribution to Skywriting scripts and highperformance code written in other programming languages. We have deployed CIEL on a cloud computing platform, and demonstrate that it achieves scalable performance for both iterative and non-iterative algorithms. 1
