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Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
arXiv - CS - Distributed, Parallel, and Cluster Computing Pub Date : 2020-09-16 , DOI: arxiv-2009.07929
Mark Blanco, Tze Meng Low, Kyungjoo Kim

In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imbalance of parallel tasks in symmetric, triangular graphs by presenting a fine-grained parallel approach to executing the support computation. This approach also increases available parallelism, making it amenable to GPU execution. We demonstrate our fine-grained parallel approach using implementations in Kokkos and evaluate them on an Intel Skylake CPU and an Nvidia Tesla V100 GPU. Overall, we observe between a 1.261. 48x improvement on the CPU and a 9.97-16.92x improvement on the GPU due to our fine-grained parallel formulation.

中文翻译:

GPU和CPU负载均衡Eager K-truss的细粒度并行探索

在这项工作中,我们对 Eager K-truss 进行了性能探索,这是 K-truss 图算法的线性代数公式。我们通过提出一种执行支持计算的细粒度并行方法来解决与对称三角形图中并行任务的负载不平衡相关的性能问题。这种方法还增加了可用的并行性,使其适合 GPU 执行。我们使用 Kokkos 中的实现来演示我们的细粒度并行方法,并在 Intel Skylake CPU 和 Nvidia Tesla V100 GPU 上对其进行评估。总体而言,我们在 1.261 之间观察到。由于我们的细粒度并行公式,CPU 性能提高了 48 倍,GPU 性能提高了 9.97-16.92 倍。
更新日期:2020-09-18
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