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.
@article{arxiv.2009.07929,
title = {Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU},
author = {Mark Blanco and Tze Meng Low and Kyungjoo Kim},
journal= {arXiv preprint arXiv:2009.07929},
year = {2020}
}
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2019 IEEE High Performance Extreme Computing Conference (HPEC)