Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

@article{Chen2019PangolinAE,
  title={Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU},
  author={Xuhao Chen and Roshan Dathathri and G. Gill and Keshav Pingali},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.06969}
}
There is growing interest in graph mining algorithms such as motif counting. Generic graph mining systems have been developed to provide unified interfaces for programming these algorithms. However, existing systems take minutes or even hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability. We present Pangolin, a high-performance and flexible in-memory graph mining framework targeting both shared-memory CPUs and GPUs. Pangolin is the… 
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42 Citations
Highly Influential Citations
13
Background Citations
23
Methods Citations
16
Results Citations
2

42 Citations

Efficient Strategies for Graph Pattern Mining Algorithms on GPUs

This work proposes novel strategies to design and implement subgraph enumeration efficiently on GPU that support a depth-first search style search that maximizes memory performance while providing enough parallelism to be exploited by the GPU, along with a warp-centric design that minimizes execution divergence and improves utilization of the computing capabilities.

A GPU-based Graph Pattern Mining System

  • Lin HuLei Zou
  • Computer Science
    Proceedings of the 31st ACM International Conference on Information & Knowledge Management
  • 2022
This work proposes a graph pattern mining framework on GPU, called GAMMA, which has great scalability and performance advantages compared with state-of-the-art graph mining works in experiments and presents several optimizations to process large graphs.

Efficient and Scalable Graph Pattern Mining on GPUs

G 2 Miner is described, the first GPM framework that runs efficiently on multiple GPUs and provides a code generator that automatically generates pattern-aware CUDA code to simplify programming and propose a customized scheduling policy to balance workload among multiple GPUs.

DIMMining: pruning-efficient and parallel graph mining on near-memory-computing

A DIMM-based Near-Memory-Computing architecture, which eliminates the large-volume data transfer between the computation and the memory, and the Systolic Merge Array is designed to further explore the parallelism on discontinuous vertices from the architecture perspective.

Sandslash: a two-level framework for efficient graph pattern mining

Sandslash is presented, an in-memory graph pattern mining framework that uses a novel programming interface to support productive, expressive, and efficient GPM on large graphs and which demonstrates that applications written using Sandslash high-level or low-level API outperform those in state-of-the-art GPM systems AutoMine, Pangolin, and Peregrine.

Khuzdul: Efficient and Scalable Distributed Graph Pattern Mining Engine

This paper proposes Khuzdul, a distributed execution engine with a well-defined abstraction that can be integrated with existing single-machine graph pattern mining (GPM) systems to provide…

Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration

This work proposes a novel two-vertex exploration strategy to accelerate the mining process and achieves significant speedups against the state-of-the-art graph pattern mining systems and supports larger pattern mining tasks that none of the existing systems can handle.

GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

GraphMineSuite (GMS) is proposed, the first benchmarking suite for graph mining that facilitates evaluating and constructing highperformance graph mining algorithms and is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graphs mining algorithms.

FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining

FlexMiner is presented, a software/hardware co-designed GPM accelerator that improves the efficiency without compromising the generality or productivity of state-of-the-art software GPM frameworks.

Mint: An Accelerator For Mining Temporal Motifs

A task-centric programming model that enables decoupled, asynchronous execution of temporal motif mining, and a novel optimization called search index memoization that significantly reduces memory traffic is proposed.

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