Lifting sequential graph algorithms for distributed-memory parallel computation

Gregor, Douglas; Lumsdaine, Andrew

doi:10.1145/1094811.1094844

Cited by 46 publications

(30 citation statements)

References 29 publications

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“…Domain specialists have written parallel programs for some of the algorithms discussed above (see [5,20,31,33] among others). There are also parallel graph libraries such as Boost [22] and STAPL [2]. However, it is difficult to extract broadly applicable abstractions, principles, and mechanisms from these implementations.…”

Section: Introductionmentioning

confidence: 99%

The tao of parallelism in algorithms

et al. 2011

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For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in "regular" algorithms that use dense arrays, such as finite-differences and FFTs. In this paper, we argue that the dependence graph is not a suitable abstraction for algorithms in new application areas like machine learning and network analysis in which the key data structures are "irregular" data structures like graphs, trees, and sets.To address the need for better abstractions, we introduce a datacentric formulation of algorithms called the operator formulation in which an algorithm is expressed in terms of its action on data structures. This formulation is the basis for a structural analysis of algorithms that we call tao-analysis. Tao-analysis can be viewed as an abstraction of algorithms that distills out algorithmic properties important for parallelization. It reveals that a generalized form of data-parallelism called amorphous data-parallelism is ubiquitous in algorithms, and that, depending on the tao-structure of the algorithm, this parallelism may be exploited by compile-time, inspector-executor or optimistic parallelization, thereby unifying these seemingly unrelated parallelization techniques. Regular algorithms emerge as a special case of irregular algorithms, and many application-specific optimization techniques can be generalized to a broader context. These results suggest that the operator formulation and taoanalysis of algorithms can be the foundation of a systematic approach to parallel programming.

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Section: Introductionmentioning

confidence: 99%

The tao of parallelism in algorithms

et al. 2011

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“…The Parallel Boost Graph Library (PBGL) [19] is a C++ library for distributed memory graph computations. The API is similar to that of the boost graph library.…”

Section: Motivation and Requirementsmentioning

confidence: 99%

GraphCT: Multithreaded Algorithms for Massive Graph Analysis

Ediger

Jiang

Riedy

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analytic queries, often pushes the limits of algorithms and machine architectures. We present GraphCT, a scalable framework for graph analysis using parallel and multithreaded algorithms on shared memory platforms. Utilizing the unique characteristics of the Cray XMT, GraphCT enables fast network analysis at unprecedented scales on a variety of input data sets. On a synthetic power law graph with 2 billion vertices and 17 billion edges, we can find the connected components in 2 minutes. We can estimate the betweenness centrality of a similar graph with 537 million vertices and over 8 billion edges in under 1 hour. GraphCT is built for portability and performance.

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“…There are several parallel languages and libraries that have similar goals as stapl [12,3,4,6,8,15]. The PSTL (Parallel Standard Template Library) project [10,11] explored the same underlying philosophy as stapl of extending the C++ STL for parallel programming.…”

Section: Related Workmentioning

confidence: 99%

The STAPL pArray

Tanase

Bianco

Amato

et al. 2007

Proceedings of the 2007 Workshop on MEmory Performance: DEaling With Applications, Systems and Architecture

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The Standard Template Adaptive Parallel Library (stapl) is a parallel programming framework that extends C++ and STL with support for parallelism. stapl provides parallel data structures (pContainers) and generic parallel algorithms (pAlgorithms), and a methodology for extending them to provide customized functionality. stapl pContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They provide views as a generic means to access data that can be passed as input to generic pAlgorithms.In this work, we present the stapl pArray, the parallel equivalent of the sequential stl valarray, a fixed-size data structure optimized for storing and accessing data based on one-dimensional indices. We describe the pArray design and show how it can support a variety of underlying data distribution policies currently available in stapl, such as blocked or blocked cyclic. We provide experimental results showing that pAlgorithms using the pArray scale well to more than 2,000 processors. We also provide results using different data distributions that illustrate that the performance of pAlgorithms and pArray methods is usually sensitive to the underlying data distribution, and moreover, that there is no one data distribution that performs best for all pAlgorithms, processor counts, or machines.

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Lifting sequential graph algorithms for distributed-memory parallel computation

Cited by 46 publications

References 29 publications

The tao of parallelism in algorithms

The tao of parallelism in algorithms

GraphCT: Multithreaded Algorithms for Massive Graph Analysis

The STAPL pArray

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