  Financial firms are interested in simulation to discover whether a given
algorithm involving financial machine learning will operate profitably. While
many versions of this type of algorithm have been published recently by
researchers, the focus herein is on a particular machine learning training
project due to the explainable nature and the availability of high frequency
market data. For this task, hardware acceleration is expected to speed up the
time required for the financial machine learning researcher to obtain the
results. As the majority of the time can be spent in classifier training, there
is interest in faster training steps. A published Limit Order Book algorithm
for predicting stock market direction is our subject, and the machine learning
training process can be time-intensive especially when considering the
iterative nature of model development. To remedy this, we deploy Graphical
Processing Units (GPUs) produced by NVIDIA available in the data center where
the computer architecture is geared to parallel high-speed arithmetic
operations. In the studied configuration, this leads to significantly faster
training time allowing more efficient and extensive model development.
