Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. Specifically, the V100's new Tensor cores are not best suited for recurrent neural networks (RNN) broadly and a specialized version of them, long-short term memory models (LSTMs), according to Xcelerit; both are widely in finance applications for handling time series inputs. "For the tested RNN and LSTM deep learning applications, we notice that the relative performance of V100 vs. P100 increases with network size (128 to 1024 hidden units) and complexity (RNN to LSTM). We record a maximum speedup in FP16 precision mode of 2.05x for V100 compared to the P100 in training mode – and 1.72x in inference mode. Those figures are many-fold below the expected performance for the V100 based on its hardware specifications (spec below, click to enlarge)," reports Xcelerit, an Ireland-based provider of software tools for quantitative finance, engineering, and research.