  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.
