HiHGNN:通过并行性和数据可重用性开发加速 HGNNs

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Runzhen Xue;Dengke Han;Mingyu Yan;Mo Zou;Xiaocheng Yang;Duo Wang;Wenming Li;Zhimin Tang;John Kim;Xiaochun Ye;Dongrui Fan
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引用次数: 0

摘要

异构图神经网络(HGNN)是处理异构图(HetGs)的强大算法,广泛应用于许多关键领域。为了捕捉 HetGs 中的结构和语义信息,HGNNs 首先聚合每个语义图中每个顶点的相邻特征向量,然后融合每个顶点在所有语义图中的聚合结果。遗憾的是,现有的图神经网络加速器并不适合加速 HGNN。这是因为它们无法有效地处理特定的执行模式,也无法利用 HGNN 中语义图处理过程中的高度并行性和数据可重用性。在这项工作中,我们首先在 GPU 上定量描述了一组具有代表性的 HGNN 模型,以揭示 HGNN 中每个阶段的执行约束、语义图之间的并行性以及语义图之间的数据可重用性。在研究结果的指导下,我们提出了一种高性能 HGNN 加速器 HiHGNN,以减轻执行约束并利用 HGNN 中新发现的并行性和数据可重用性。具体来说,我们首先提出了一种边界感知的阶段融合方法,该方法适合 HGNN 加速,可在感知各执行阶段的执行边界的情况下对其进行融合和管道化。其次,我们设计了一种独立感知并行执行设计,以利用语义图之间的并行性。最后,我们提出了一种相似性感知执行调度,以利用语义图间数据的可重用性。与运行在英伟达™(NVIDIA®)GPU T4和GPU A100上的最先进软件框架相比,HiHGNN分别实现了平均40.0倍和8.3倍的速度提升,以及99.59%和99.74%的能耗降低,而内存带宽仅为GPU A100的五分之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation
Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 40.0× and 8.3× speedup as well as 99.59% and 99.74% energy reduction with quintile the memory bandwidth of GPU A100.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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