MultiGraph: Efficient Graph Processing on GPUs

Hong, Changwan; Sukumaran-Rajam, Aravind; Kim, Jin-Sung; Sadayappan, P.

doi:10.1109/pact.2017.48

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…As shown in Table 3, Gluon scales out Ligra (D-Ligra) and Galois (D-Galois) to 256 hosts. Single-GPU frameworks [24,26,28,34,55,69] and algorithm implementations [14,45,[49][50][51] have shown that the GPU can be efficiently utilized for irregular computations.…”

Section: Related Workmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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This paper introduces a new approach to building distributedmemory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for * Both authors contributed equally.

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Section: Related Workmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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“…In contrast, GraphPhi is carefully designed and implemented to take advantage of those features. Graph processing on GPU and Xeon Phi: There are also many graph processing frameworks on GPUs [9,18,19,23,32,34,39,44,50]. However, they also concern different problems compared to our work.…”

Section: Related Workmentioning

confidence: 94%

Graphphi

Peng

Powell

et al. 2018

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

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Modern parallel architecture design has increasingly turned to throughput-oriented devices to address concerns about energy efficiency and power consumption. However, graph applications cannot tap into the full potential of such architectures because of highly unstructured computations and irregular memory accesses. In this paper, we present GraphPhi, a new approach to graph processing on emerging Intel Xeon Phi-like architectures, by addressing the restrictions of migrating existing graph processing frameworks on shared-memory multi-core CPUs to this new architecture. Specifically, GraphPhi consists of 1) an optimized hierarchically blocked graph representation to enhance the data locality for both edges and vertices within and among threads, 2) a hybrid vertexcentric and edge-centric execution to efficiently find and process active edges, and 3) a uniform MIMD-SIMD scheduler integrated with a lock-free update support to achieve both good thread-level load balance and SIMD-level utilization. Besides, our efficient MIMD-SIMD execution is capable of hiding memory latency by increasing the number of concurrent memory access requests, thus benefiting more from the latest High-Bandwidth Memory technique. We evaluate our GraphPhi on six graph processing applications. Compared to two state-of-the-art shared-memory graph processing frameworks, it results in speedups up to 4X and 35X , respectively.

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“…CuSha [15] develops new graph representation method for high GPU utilization ratio. For better performance, MultiGraph [11] uses various graph representation and execution schemes based on the ratio of active vertices. Lux [13] uses the aggregated memory bandwidth of the distributed platform with multiple GPUs.…”

Section: Gpu-based Graph Processingmentioning

confidence: 99%

AsynGraph

Liao

Jin

et al. 2020

ACM Trans. Archit. Code Optim.

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Recently, iterative graph algorithms are proposed to be handled by GPU-accelerated systems. However, in iterative graph processing, the parallelism of GPU is still underutilized by existing GPU-based solutions. In fact, because of the power-law property of the natural graphs, the paths between a small set of important vertices (e.g., high-degree vertices) play a more important role in iterative graph processing's convergence speed. Based on this fact, for faster iterative graph processing on GPUs, this article develops a novel system, called AsynGraph, to maximize its data parallelism. It first proposes an efficient structure-aware asynchronous processing way. It enables the state propagations of most vertices to be effectively conducted on the GPUs in a concurrent way to get a higher GPU utilization ratio through efficiently handling the paths between the important vertices. Specifically, a graph sketch (consisting of the paths between the important vertices) is extracted from the original graph to serve as a fast bridge for most state propagations. Through efficiently processing this sketch more times within each round of graph processing, higher parallelism of GPU can be utilized to accelerate most state propagations. In addition, a forward-backward intra-path processing way is also adopted to asynchronously handle the vertices on each path, aiming to further boost propagations along paths and also ensure smaller data access cost. In comparison with existing GPU-based systems, i.e.,

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MultiGraph: Efficient Graph Processing on GPUs

Cited by 26 publications

References 19 publications

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Graphphi

AsynGraph

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