  Finite element schemes based on discontinuous Galerkin methods possess
features amenable to massively parallel computing accelerated with general
purpose graphics processing units (GPUs). However, the computational
performance of such schemes strongly depends on their implementation. In the
past, several implementation strategies have been proposed. They are based
exclusively on specialized compute kernels tuned for each operation, or they
can leverage BLAS libraries that provide optimized routines for basic linear
algebra operations. In this paper, we present and analyze up-to-date
performance results for different implementations, tested in a unified
framework on a single NVIDIA GTX980 GPU. We show that specialized kernels
written with a one-node-per-thread strategy are competitive for polynomial
bases up to the fifth and seventh degrees for acoustic and elastic models,
respectively. For higher degrees, a strategy that makes use of the NVIDIA
cuBLAS library provides better results, able to reach a net arithmetic
throughput 35.7% of the theoretical peak value.
