For large-scale graph analytics on the GPU, the irregularity of data access/control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock," our high-level bulksynchronous graph-processing system targeting the GPU, takes a new approach to abstracting GPU graph analytics: rather than designing an abstraction around computation, Gunrock instead implements a novel data-centric abstraction centered 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 highlevel 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 graph primitives (BFS, BC, SSSP, CC, and PageRank) 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. * Currently an employee at Google. † Currently an employee at IBM. implemented and evaluated in Gunrock, we focus in this paper on breadth-first search (BFS), single-source shortest path (SSSP), betweenness centrality (BC), PageRank, and connected components (CC). Though the GPU's excellent peak throughput and energy efficiency [17] have been demonstrated across many application domains, these applications often exploit regular, structured parallelism. The inherent irregularity of graph data structures leads to irregularity in data access and control flow, making an efficient implementation on GPUs a significant challenge. Our goal with Gunrock is to deliver the performance of customized, complex GPU hardwired graph primitives with a highlevel programming model that allows programmers to quickly develop new graph primitives. To do so, we must address the chief challenge in a highly parallel graph processing system: managing irregularity in work distribution. Gunrock integrates sophisticated load-balancing and work-efficiency strategies into its core. These strategies are hidden from the programmer; the programmer instead expresses what operations should be performed on the frontier rather than how those operations should be performed. Programmers can assemble complex and high-performance graph primitives from operations that manipulate the frontier (the "what") without knowing the internals of the operations (the "how"). Our contributions are as follows: 1. We present a novel data-centric abstraction for graph operations that allows programmers to develop graph primitives at a high level of abstraction while simultaneously delivering high performance. This abstraction, unlike the abstractions of previous GPU programmable frameworks, is able to elegantly incorporate profitable optimizations-kernel fusion, push-pull traversal, idempotent traversal, and priority queues-into the core of its implementation. 2. We design and implement a set of simple and flexible APIs that can express a wide range of graph processing primitives at a high level of abstraction (at least as simple, if not more so, than other programmable GPU frameworks). 3. We describe several GPU-specific optimization strategies for memory efficiency, load balancing, and workload management that together achieve high performance. All of our graph primitives achieve comparable performance to their hardwired counterparts and significantly outperform previous programmable GPU abstractions. 4. We provide a detailed experimental evaluation of our graph primitives with performance comparisons to several CPU and GPU implementations. Gunrock is currently available in an open-source repository at http://gunrock.github.io/ and is currently available for use by external developers.
A data-centric abstraction with GPU-specific optimizations is provided in REF .
Gunrock: a high-performance graph processing library on the GPU
PPoPP '16
Computer Science