论文标题
在GPU和CPU上探索用于负载平衡急切的K-Truss的细粒平行性的探索
Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
论文作者
论文摘要
在这项工作中，我们介绍了急切的K-Truss的性能探索，K-Truss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精细粒度并行方法来解决与对称，三角形图中平行任务的负载不平衡有关的问题。这种方法还增加了可用的并行性，使其可容纳GPU执行。我们证明了使用Kokkos实现的细粒平行方法，并在Intel Skylake CPU和NVIDIA TESLA V100 GPU上对其进行评估。总体而言，我们观察到1.261。由于我们的细粒平行配方，CPU上的48倍改进，GPU的9.97-16.92倍改善。
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.
