Gunrock: A High-Performance Graph Processing Library on the GPU (1501.05387v6)

Abstract: For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five key graph primitives and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.

• The paper introduces a data-centric abstraction that optimizes GPU performance with techniques like kernel fusion and efficient frontier management.
• It presents simplified APIs enabling developers to effectively express a wide range of graph processing primitives on GPUs.
• Experimental results show significant speedups over CPU frameworks, especially on scale-free graphs with irregular data access patterns.

Overview of "Gunrock: A High-Performance Graph Processing Library on the GPU"

The paper "Gunrock: A High-Performance Graph Processing Library on the GPU" presented by Yangzihao Wang et al. provides a detailed exposition of Gunrock, a sophisticated graph-processing framework specially designed for Graphics Processing Units (GPUs). Gunrock offers a high-level, data-centric programming model, emphasizing operations on vertex or edge frontiers to tackle graph analytics efficiently.

Motivation and Approach

Gunrock seeks to address the challenges associated with the irregularity of data access and control flow in graph processing, which complicate GPU programming. Its novelty lies in adopting a data-centric abstraction rather than a computation-centric one. This approach allows for efficient manipulation of a subset of graph vertices or edges currently in focus—referred to as the "frontier." Gunrock's architecture supports iterative, convergent graph primitives through a blend of high-level programmability and low-level GPU performance optimizations.

Key Contributions

1. Data-Centric Abstraction: Gunrock introduces a novel abstraction for graph operations, providing a balance between expressiveness and performance. This method encompasses profitable optimizations such as kernel fusion, push-pull traversal, and use of priority queues.
2. Simplified APIs: The system includes a set of flexible APIs that allow developers to express a diverse range of graph processing primitives effectively.
3. Optimized GPU Operations: Gunrock implements sophisticated strategies for memory efficiency, load balancing, and workload management, achieving performance comparable to hardwired GPU solutions and outperforming existing high-level GPU libraries.
4. Comprehensive Evaluation: The paper offers an extensive experimental evaluation of Gunrock across several graph primitives, exhibiting significant performance gains over CPU-based systems and competitive results against hardwired GPU implementations.

Experimental Results

Gunrock demonstrates substantial speedups—at least an order of magnitude over CPU frameworks like Boost and PowerGraph, and comparable performance to the fastest fixed-function GPU primitives. The evaluation spans graph primitives such as Breadth-First Search (BFS), Betweenness Centrality (BC), Single-Source Shortest Path (SSSP), Connected Components (CC), and PageRank. Results indicate particularly strong performance on scale-free graphs due to Gunrock’s efficient load-balancing and traversal strategies.

Implications and Future Work

Gunrock represents a significant advancement in the field of GPU-based graph analytics, facilitating high-performance computing for complex graph algorithms. The framework’s open-source availability encourages further exploration and development by external researchers. Future directions could include scaling Gunrock for multi-GPU and multi-node environments, enhancing its capabilities for dynamic graphs, and improving global and neighborhood operations. There is also potential in evolving Gunrock's kernel-fusion techniques to bridge the performance gap with hardwired targets further.

Conclusion

In sum, this work underscores how Gunrock successfully elevates GPU graph processing by blending a powerful, data-centric model with high-level abstraction and raw performance optimization. The synergy between programmability and efficiency presented by Gunrock makes it a compelling tool for researchers and practitioners aiming to leverage GPU architectures for graph analytics.