Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

Shi, Xuanhua; Luo, Xuan; Liang, Junling; Zhao, Peng; Di, Sheng; He, Bingsheng; Jin, Hai

doi:10.1109/tkde.2017.2745562

Cited by 35 publications

(19 citation statements)

References 27 publications

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“…Frog [16], [17] differs from other frameworks here in requiring (expensive) preprocessing to color the graph into sets of independent vertices. With the colored graph, they can process colors asynchronously.…”

Section: A Scalable Gpu Graph Librariesmentioning

confidence: 99%

“…GraphReduce [15] and Frog (asynchronous) [16], [17] are out-of-core GPU approaches, GraphMap [27] targets CPU distributed-memory clusters, and Totem [13] is an heterogeneous CPU-GPU approach. While out-of-core approaches have the promise to process graphs much larger than in-core work such as ours, our framework can comfortably process the largest graphs they used in any of their results [15]- [17], [27]. For these comparisons, we use the smallest number of GPUs possible for individual comparisons, and achieve much less processing time.…”

Section: Comparisons Vs Previous Mgpu Workmentioning

confidence: 99%

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Multi-GPU Graph Analytics

Pan

Wang

et al. 2017

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU implementations, our design only requires programmers to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, such as direction optimizing traversal, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system.

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Section: A Scalable Gpu Graph Librariesmentioning

confidence: 99%

Section: Comparisons Vs Previous Mgpu Workmentioning

confidence: 99%

Multi-GPU Graph Analytics

Pan

Wang

et al. 2017

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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“…From distributed computing environment [1,2] , to single highend server [3] , to the commodity personal computer [4,5] , these systems basically make tremendous efforts on software optimizations for programmability, high performance and scalability under traditional architectures. In an effort to accelerate graph workloads, multicore CPUs and GPUs have been recently adopted to expose a high degree of parallelism for high perfromance graph iteration, e.g., Medusa [6] , Cusha [7] , GunRock [8] , Frog [9] , MapGraph [10] and Enterprise [11] .…”

Section: Introductionmentioning

confidence: 99%

A Survey on Graph Processing Accelerators: Challenges and Opportunities

Gui

Zheng

et al. 2019

J. Comput. Sci. Technol.

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Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerator. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and complexity of hardware configurations. We finially present to discuss several challenges in details, and to further explore the opportunities for the future research.

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“…In this paper, we address this problem by exploiting graphics processing units (GPUs). Because of massive hardware parallelism and high memory bandwidth, GPUs have been widely used in diverse applications including machine learning [5][6][7], graph processing [8][9][10], big data analytics [11,12], image processing [13], and fluid dynamics [14]. In order to reap the power of GPUs, the algorithmic steps need to be mapped delicately onto the architecture of GPUs, especially the thread and memory hierarchy.…”

Section: Introductionmentioning

confidence: 99%

Efficient Tensor Sensing for RF Tomographic Imaging on GPUs

Zhang

2019

Future Internet

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Radio-frequency (RF) tomographic imaging is a promising technique for inferring multi-dimensional physical space by processing RF signals traversed across a region of interest. Tensor-based approaches for tomographic imaging are superior at detecting the objects within higher dimensional spaces. The recently-proposed tensor sensing approach based on the transform tensor model achieves a lower error rate and faster speed than the previous tensor-based compress sensing approach. However, the running time of the tensor sensing approach increases exponentially with the dimension of tensors, thus not being very practical for big tensors. In this paper, we address this problem by exploiting massively-parallel GPUs. We design, implement, and optimize the tensor sensing approach on an NVIDIA Tesla GPU and evaluate the performance in terms of the running time and recovery error rate. Experimental results show that our GPU tensor sensing is as accurate as the CPU counterpart with an average of 44.79 × and up to 84.70 × speedups for varying-sized synthetic tensor data. For IKEA Model 3D model data of a smaller size, our GPU algorithm achieved 15.374× speedup over the CPU tensor sensing. We further encapsulate the GPU algorithm into an open-source library, called cuTensorSensing (CUDA Tensor Sensing), which can be used for efficient RF tomographic imaging.

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Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

Cited by 35 publications

References 27 publications

Multi-GPU Graph Analytics

Multi-GPU Graph Analytics

A Survey on Graph Processing Accelerators: Challenges and Opportunities

Efficient Tensor Sensing for RF Tomographic Imaging on GPUs

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