The GAP Benchmark Suite (1508.03619v4)

Abstract: We present a graph processing benchmark suite with the goal of helping to standardize graph processing evaluations. Fewer differences between graph processing evaluations will make it easier to compare different research efforts and quantify improvements. The benchmark not only specifies graph kernels, input graphs, and evaluation methodologies, but it also provides optimized baseline implementations. These baseline implementations are representative of state-of-the-art performance, and thus new contributions should outperform them to demonstrate an improvement. The input graphs are sized appropriately for shared memory platforms, but any implementation on any platform that conforms to the benchmark's specifications could be compared. This benchmark suite can be used in a variety of settings. Graph framework developers can demonstrate the generality of their programming model by implementing all of the benchmark's kernels and delivering competitive performance on all of the benchmark's graphs. Algorithm designers can use the input graphs and the baseline implementations to demonstrate their contribution. Platform designers and performance analysts can use the suite as a workload representative of graph processing.

• The paper introduces a standardized graph processing evaluation framework that unifies graph kernels and methodologies.
• The paper details six key graph kernels and diverse input graphs to establish performance baselines for novel algorithms.
• The paper emphasizes optimized reference implementations and rigorous evaluation protocols, fostering reproducible and meaningful comparisons.

Overview of the GAP Benchmark Suite

The paper "The GAP Benchmark Suite" by Scott Beamer, Krste Asanovic, and David Patterson addresses a significant challenge in graph processing research: the lack of a standardized methodology for evaluating graph algorithms. The authors introduce the GAP (Graph Algorithm Platform) Benchmark Suite to facilitate more meaningful comparisons across different research initiatives by providing a comprehensive suite designed to set a standard in graph processing evaluations. This suite includes graph kernels, input graphs, and evaluation methodologies, along with optimized reference implementations, which aim to serve as performance baselines for benchmarking new contributions.

Motivations and Challenges

The resurgence of interest in graph algorithms is driven by their applications in social network analysis, science, and recognition tasks, necessitating diverse and high-performance graph processing capabilities across multiple domains. However, the absence of a standardized evaluation framework has led to inconsistencies and non-comparable results in the literature. The GAP Benchmark Suite seeks to address this issue by establishing consistent methodologies for graph processing.

A notable concern highlighted is the variation in graph processing approaches due to differences in input assumptions (e.g., directed versus undirected graphs) and problem definitions (e.g., vertex-tracking approaches in BFS). The suite aims to mitigate these disparities by providing a unified set of definitions and methodologies that are explicitly stated within the benchmark documentation.

Components of the GAP Benchmark Suite

The benchmark consists of several components that are crucial for comprehensive graph processing evaluations:

1. Graph Kernels: The suite includes six graph kernels – Breadth-First Search (BFS), Single-Source Shortest Paths (SSSP), PageRank (PR), Connected Components (CC), Betweenness Centrality (BC), and Triangle Counting (TC). These kernels were chosen based on their prevalence in graph application domains and their ability to represent both traversal-centric and compute-centric workloads.
2. Input Graphs: The suite specifies five diverse input graphs, including both real-world examples (e.g., Twitter and web crawls) and synthetic graphs (e.g., Kronecker and Uniform Random). This diversity ensures that evaluations are robust across different graph topologies, highlighting strengths and weaknesses of particular graph processing techniques.
3. Reference Implementations: The benchmark's optimized reference implementations serve as high-performance baselines that new algorithms should improve upon. These implementations use state-of-the-art algorithms for each kernel, facilitating an understanding of current algorithmic best practices while providing a practical point of comparison.
4. Evaluation Methodologies: The authors prescribe specific methodologies for executing trials, emphasizing the importance of controlling extraneous factors to ensure that evaluations accurately reflect algorithmic improvements rather than artifacts of differences in setup or assumptions.

Implications and Future Directions

The introduction of the GAP Benchmark Suite has significant implications for both practical advancements in graph processing and theoretical algorithm development. By providing a unified framework for evaluation, the benchmark suite encourages the development of more efficient graph processing algorithms and system implementations that are directly comparable.

This suite is anticipated to stimulate further research into optimizing algorithms for various graph types and processing environments, from shared memory systems to distributed architectures. It also serves as an educational resource, illustrating advanced concepts in graph algorithm implementation and facilitating a deeper understanding of performance optimization techniques.

The future of graph processing will likely see increased focus on leveraging this benchmark suite for developing scalable and portable solutions across heterogeneous computing platforms. As graph datasets continue to grow in size and complexity, the GAP Benchmark Suite will play a pivotal role in guiding research towards addressing these challenges effectively.

In conclusion, the GAP Benchmark Suite provides a critical infrastructure for standardizing graph processing evaluations, fostering innovation, and advancing the field of graph algorithms by establishing a robust baseline for performance measurement and comparison.