GPU和CPU上负载均衡早期K-桁架的细粒度并行性研究
Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
摘要：
在这项工作中，我们提出了一个性能探索的渴望K-桁架，线性代数公式的K-桁架图算法。我们提出了一种细粒度的并行方法来执行支持计算，以解决对称，三角图的并行任务的负载不平衡的性能问题。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中的实现演示了我们的细粒度并行方法，并在Intel Skylake CPU和Nvidia Tesla V100 GPU上对其进行了评估。总的来说，我们观察到1.261之间。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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
