The vertex-centric programming model is an established computational paradigm recently incorporated into distributed processing frameworks to address challenges in large-scale graph processing. Billion-node graphs that exceed the memory capacity of commodity machines are not well supported by popular Big Data tools like MapReduce, which are notoriously poor performing for iterative graph algorithms such as PageRank. In response, a new type of framework challenges one to “think like a vertex” (TLAV) and implements user-defined programs from the perspective of a vertex rather than a graph. Such an approach improves locality, demonstrates linear scalability, and provides a natural way to express and compute many iterative graph algorithms. These frameworks are simple to program and widely applicable but, like an operating system, are composed of several intricate, interdependent components, of which a thorough understanding is necessary in order to elicit top performance at scale. To this end, the first comprehensive survey of TLAV frameworks is presented. In this survey, the vertex-centric approach to graph processing is overviewed, TLAV frameworks are deconstructed into four main components and respectively analyzed, and TLAV implementations are reviewed and categorized.

pdf/thinking-like-a-vertex-a-survey-of-vertex-centric-frameworks-19cg0gjqnm.pdf

Thinking Like a Vertex: A Survey of Vertex-Centric Frameworks for Large-Scale Distributed Graph Processing

We reduce the cost of communication and synchronization in graph processing by analyzing the fastest way to process graphs: pushing the updates to a shared state or pulling the updates to a private state. We investigate the applicability of this push-pull dichotomy to various algorithms and its impact on complexity, performance, and the amount of used locks, atomics, and reads/writes. We consider 11 graph algorithms, 3 programming models, 2 graph abstractions, and various families of graphs. The conducted analysis illustrates surprising differences between push and pull variants of different algorithms in performance, speed of convergence, and code complexity; the insights are backed up by performance data from hardware counters. We use these findings to illustrate which variant is faster for each algorithm and to develop generic strategies that enable even higher speedups. Our insights can be used to accelerate graph processing engines or libraries on both massively-parallel shared-memory machines as well as distributed-memory systems.

To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations

Graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually inspected. A popular metric used to analyze these networks is between ness centrality, which has applications in community detection, power grid contingency analysis, and the study of the human brain. However, these analyses come with a high computational cost that prevents the examination of large graphs of interest. Prior GPU implementations suffer from large local data structures and inefficient graph traversals that limit scalability and performance. Here we present several hybrid GPU implementations, providing good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously. We achieve up to 13x speedup on high-diameter graphs and an average of 2.71x speedup overall over the best existing GPU algorithm. We observe near linear speedup and performance exceeding tens of GTEPS when running between ness centrality on 192 GPUs.

Scalable and high performance betweenness centrality on the GPU

Graph processing applications are notorious for exhibiting poor cache locality due to an irregular memory access pattern. However, prior work on graph reordering has observed that the structural properties of real-world input graphs can be exploited to improve locality of graph applications. While sophisticated graph reordering techniques are effective at reducing the graph application runtime, the reordering step imposes significant overheads leading to a net increase in end-to-end execution time. The high overhead of sophisticated reordering techniques renders them inapplicable in many important use cases wherein the input graph is processed only a few times and, hence, cannot amortize the overhead of reordering. In this work, we identify lightweight reordering techniques that improve performance even after accounting for the overhead of reordering. We first conduct a detailed performance evaluation of these lightweight reordering techniques across a range of applications to identify the characteristics of applications that benefit the most from lightweight reordering. Next, we address a major impediment to the general adoption of these reordering techniques - input-dependent speedups – by linking the speedup from lightweight reordering to structural properties of the input graph. We leverage the structure dependence of speedup to propose a low-overhead mechanism to determine whether a given input graph would benefit from reordering. Using our selective lightweight reordering, we show maximum end-to-end speedup of up to 1.75x and never cause a slowdown beyond 0.1%.

When is Graph Reordering an Optimization? Studying the Effect of Lightweight Graph Reordering Across Applications and Input Graphs

Consider a directed edge-labeled graph, such as a social network or a citation network. A fundamental query on such data is to determine if there is a path in the graph from a given source vertex to a given target vertex, using only edges with labels in a restricted subset of the edge labels in the graph. Such label-constrained reachability (LCR) queries play an important role in graph analytics, for example, as a core fragment of the so-called regular path queries which are supported in practical graph query languages such as the W3C's SPARQL 1.1, Neo4j's Cypher, and Oracle's PGQL. Current solutions for LCR evaluation, however, do not scale to large graphs which are increasingly common in a broad range of application domains. In this paper we present the first practical solution for efficient LCR evaluation, leveraging landmark-based indexes for large graphs. We show through extensive experiments that our indexes are significantly smaller than state-of-the-art LCR indexing techniques, while supporting up to orders of magnitude faster query evaluation times. Our complete C++ codebase is available as open source for further research.

https://dl.acm.org/doi/pdf/10.1145/3035918.3035955

Landmark Indexing for Evaluation of Label-Constrained Reachability Queries

Breadth-First Search is an important kernel used by many graph-processing applications. In many of these emerging applications of BFS, such as analyzing social networks, the input graphs are low-di...

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