There has been significant recent interest in parallel frameworks for processing graphs due to their applicability in studying social networks, the Web graph, networks in biology, and unstructured meshes in scientific simulation. Due to the desire to process large graphs, these systems have emphasized the ability to run on distributed memory machines. Today, however, a single multicore server can support more than a terabyte of memory, which can fit graphs with tens or even hundreds of billions of edges. Furthermore, for graph algorithms, shared-memory multicores are generally significantly more efficient on a per core, per dollar, and per joule basis than distributed memory systems, and shared-memory algorithms tend to be simpler than their distributed counterparts.In this paper, we present a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write. The framework has two very simple routines, one for mapping over edges and one for mapping over vertices. Our routines can be applied to any subset of the vertices, which makes the framework useful for many graph traversal algorithms that operate on subsets of the vertices. Based on recent ideas used in a very fast algorithm for breadth-first search (BFS), our routines automatically adapt to the density of vertex sets. We implement several algorithms in this framework, including BFS, graph radii estimation, graph connectivity, betweenness centrality, PageRank and single-source shortest paths. Our algorithms expressed using this framework are very simple and concise, and perform almost as well as highly optimized code. Furthermore, they get good speedups on a 40-core machine and are significantly more efficient than previously reported results using graph frameworks on machines with many more cores.

Ligra: a lightweight graph processing framework for shared memory

https://opus.lib.uts.edu.au/bitstream/10453/114834/4/OCC-90253_AM.pdf

A review of clustering techniques and developments

Trends in big data analytics

Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tackled by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.

https://dl.acm.org/doi/pdf/10.1145/2517349.2522739

A lightweight infrastructure for graph analytics

We survey recent trends in practical algorithms for balanced graph partitioning, point to applications and discuss future research directions.

/pdf/recent-advances-in-graph-partitioning-4eois71ejs.pdf

Recent Advances in Graph Partitioning

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 data-centric 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 tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.

/pdf/the-tao-of-parallelism-in-algorithms-4slfomyy9x.pdf

The tao of parallelism in algorithms

Approximate computing trades off accuracy of results for resources such as energy or computing time. There is a large and rapidly growing literature on approximate computing that has focused mostly on showing the benefits of approximate computing. However, we know relatively little about how to control approximation in a disciplined way. In this paper, we address the problem of controlling approximation for non-streaming programs that have a set of "knobs" that can be dialed up or down to control the level of approximation of different components in the program. We formulate this control problem as a constrained optimization problem, and describe a system called Capri that uses machine learning to learn cost and error models for the program, and uses these models to determine, for a desired level of approximation, knob settings that optimize metrics such as running time or energy usage. Experimental results with complex benchmarks from different problem domains demonstrate the effectiveness of this approach.

https://dl.acm.org/doi/pdf/10.1145/2872362.2872402

Proactive Control of Approximate Programs

Irregular algorithms are organized around pointer-based data structures such as graphs and trees, and they are ubiquitous in applications. Recent work by the Galois project has provided a systematic approach for parallelizing irregular applications based on the idea of optimistic or speculative execution of programs. However, the overhead of optimistic parallel execution can be substantial. In this paper, we show that many irregular algorithms have structure that can be exploited and present three key optimizations that take advantage of algorithmic structure to reduce speculative overheads. We describe the implementation of these optimizations in the Galois system and present experimental results to demonstrate their benefits. To the best of our knowledge, this is the first system to exploit algorithmic structure to optimize the execution of irregular programs.

/pdf/structure-driven-optimizations-for-amorphous-data-parallel-11hb513fb2.pdf

Structure-driven optimizations for amorphous data-parallel programs

Speculative execution is a promising approach for exploiting parallelism in many programs, but it requires efficient schemes for detecting conflicts between concurrently executing threads. Prior work has argued that checking semantic commutativity of method invocations is the right way to detect conflicts for complex data structures such as kd-trees. Several ad hoc ways of checking commutativity have been proposed in the literature, but there is no systematic approach for producing implementations.In this paper, we describe a novel framework for reasoning about commutativity conditions: the commutativity lattice. We show how commutativity specifications from this lattice can be systematically implemented in one of three different schemes: abstract locking, forward gatekeeping and general gatekeeping. We also discuss a disciplined approach to exploiting the lattice to find different implementations that trade off precision in conflict detection for performance. Finally, we show that our novel conflict detection schemes are practical and can deliver speedup on three real-world applications.

/pdf/exploiting-the-commutativity-lattice-1ugba2rext.pdf

Exploiting the commutativity lattice

Clustering of social networks is an important task for their analysis, however, most existing algorithms do not scale to the massive size of today&#x00E2;s social networks. A popular class of graph clustering algorithms for large-scale networks, such as PMetis, KMetis and Graclus, is based on a multilevel framework. Generally, these multilevel algorithms work reasonably well on networks with a few million vertices. However, when the network size increases to the scale of 10 million vertices or greater, the performance of these algorithms rapidly degrades. Furthermore, an inherent property of social networks, the power law degree distribution, makes these algorithms infeasible to apply to large-scale social networks. In this paper, we propose a scalable and memory-efficient clustering algorithm for large-scale social networks. We name our algorithm GEM, by mixing two key concepts of the algorithm, Graph Extraction and weighted kernel k-Means. GEM efficiently extracts a good skeleton graph from the original graph, and propagates the clustering result of the extracted graph to the rest of the network. Experimental results show that GEM produces clusters of quality comparable to or better than existing state-of-the-art graph clustering algorithms, while it is much faster and consumes much less memory. Furthermore, the parallel implementation of GEM, called PGEM, not only produces higher quality of clusters but also achieves much better scalability than most current parallel graph clustering algorithms.

/pdf/scalable-and-memory-efficient-clustering-of-large-scale-33seejxcyy.pdf

Xin Sui

Papers

The tao of parallelism in algorithms

Proactive Control of Approximate Programs

Structure-driven optimizations for amorphous data-parallel programs

Exploiting the commutativity lattice

Scalable and Memory-Efficient Clustering of Large-Scale Social Networks