Summary We present a parallel toolkit for pairwise distance computation in massive networks. Computing the exact shortest paths between a large number of vertices is a costly operation, and serial algorithms are not practical for billion‐scale graphs. We first describe an efficient parallel method to solve the single source shortest path problem on commodity hardware with no shared memory. Using it as a building block, we introduce a new parallel algorithm to estimate the shortest paths between arbitrary pairs of vertices. Our method exploits data locality, produces highly accurate results, and allows batch computation of shortest paths with 7%average error in graphs that contain billions of edges. The proposed algorithm is up to two orders of magnitude faster than previously suggested algorithms and does not require large amounts of memory or expensive high‐end servers. We further leverage this method to estimate the closeness and betweenness centrality metrics, which involve systems challenges dealing with indexing, joining, and comparing large datasets efficiently. In one experiment, we mined a real‐world Web graph with 700 million nodes and 12 billion edges to identify the most central vertices and calculated more than 63 billion shortest paths in 6 h on a 20‐node commodity cluster. Copyright © 2014 John Wiley & Sons, Ltd.

Leveraging cloud computing to acquire the necessary computation resources to scale out parallel applications is becoming common practice. However, many such applications also require communication and synchronization between processes. Although, commercial cloud platforms provide ready access to scalable compute and storage services, implementing communication and synchronization between cooperating processes and efficiently exchanging arbitrary size messages remains a challenge for application developers. In clouds, durable queues provide basic abstractions for communication. However, they are not sufficient for applications that require transferring arbitrary size messages or for applications that require higher level abstractions such as broadcast. Furthermore, direct socket based communication is susceptible to various fluctuations common in data center environments. We envision a solution to this problem that leverages scalable storage services, queues, and direct socket based communication. Publish/subscribe (pub/sub) is a well-known communication pattern that can achieve the above capabilities in a loosely coupled fashion, which is highly desirable in cloud environments where most services are asynchronous. In this paper, we describe the architecture of a pub/sub library implemented on a commercial cloud computing platform, which can be used to develop various parallel applications. We also present an evaluation of our implementation using both micro benchmarks and a real world application. Together, these demonstrate that our approach is both effective and scalable in performing communication and synchronization in cloud scale applications.

Text mining applications typically involve statistical models that require accessing and updating model parameters in an iterative fashion. With the growing size of the data, such models become extremely parameter rich, and naive parallel implementations fail to address the scalability problem of maintaining a distributed look-up table that maps model parameters to their values. We evaluate several existing alternatives to provide coordination among worker nodes in Hadoop [11] clusters, and suggest a new multi-layered look-up architecture that is specifically optimized for certain problem domains. Our solution exploits the power-law distribution characteristics of the phrase or n-gram counts in large corpora while utilizing a Bloom Filter [2], in-memory cache, and an HBase [12] cluster at varying levels of abstraction.

We study text analysis algorithms that use global optimization methods to compute local characteristics that are consistent with properties of the entire corpus rather than computed locally based on exogenous parameters. In the iterative implementations that we consider, each step both reads and updates a database of parameter values. Motivated by a need for rapid analysis of large corpora, we have developed methods for efficient access to such databases on parallel computers. These methods combine Bloom filters, in-memory caches, and an HBase cluster to reduce communication costs greatly relative to simpler approaches that either fully distribute or fully replicate the database. We also describe how this method can be incorporated into the MapReduce programming model, and illustrate its use within phrase segmentation programs. Our design can achieve considerable run time, latency and storage space improvements relative to other methods. In one phrase segmentation application, we improve performance by a factor of six relative to an HBase-based implementation.

Applying high level parallel runtimes to data/compute intensive applications is becoming increasingly common. The simplicity of the MapReduce programming model and the availability of open source MapReduce runtimes such as Hadoop, are attracting more users to the MapReduce programming model. Microsoft has released DryadLINQ for academic use, allowing users to experience a new programming model and a runtime that is capable of performing large scale data/compute intensive analyses. In this paper, we present our experience in applying DryadLINQ for a series of scientific data analysis applications, identify their mapping to the DryadLINQ programming model, and compare their performances with Hadoop implementations of the same applications.